Comparison of four heatdetection methods on an
experimental dairy-farm
in Austria
Research Project
M.G. van den Berg, BSc.
3381781
18-08-2014
Project supervisors:
Dr. H. Jonker, University of Utrecht
Prof. Dr. M. Drillich, Vetmeduni Vienna
Ing. Dr. J. Huber, Vetmeduni Vienna
Summary
Heat detection has been very important for the profitability of a dairy herd. There
have been a lot of products available to improve the rate of detection. These have been
based on visual detection, detection of mounting and/or activity. The activity measurement
methods were not included in this study due to location where predominantly small farms
exist. In this research four methods were compared, a visual routine of the stablemaster,
3x20 minutes of observation, the usage of cameras and Estrotect®. The research was
performed on the Teaching and Research Farm of the Vetmeduni (LFG Kremesberg).
Approximately 70 Simmental and Holstein Friesian cows were included in the study.
The Estrotect® was not significant (Chi2-test) because of the high number of false
positives and no association was found between the results of the Estrotect® and a true
positive or true negative oestrus. The other methods were not significant as well. However,
the 3x20 minutes had the highest accuracy (73%). The camera method and stablemaster
routine had the highest number of accurate detections (68/93) and (71/107). When
Estrotect® was combined with one of the other methods an increase of 34-56% in true
positives detections was found.
The usage of aids like Estrotect® for heat detection could be of added value if the
conditions of the stable or pasture are taken into account. Determining which method has
the best influence on heat detection rates is difficult, this depends on management,
environmental and animal factors. More research is needed for a conclusive advice.
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Contents
Summary .................................................................................................................................... 1
Introduction................................................................................................................................ 3
Literature .................................................................................................................................... 4
The cycle of the cow ............................................................................................................... 4
Heat detection ........................................................................................................................ 4
Pregnant cows ........................................................................................................................ 5
Economic losses ...................................................................................................................... 6
Methods for oestrus detection............................................................................................... 7
Activity measurements ....................................................................................................... 7
Mount detection aids.......................................................................................................... 8
Visual ................................................................................................................................. 10
Video cameras................................................................................................................... 10
Temperature ..................................................................................................................... 10
Aim of the study ................................................................................................................... 11
Materials and methods ............................................................................................................ 11
Animals ................................................................................................................................. 11
Experimental design ............................................................................................................. 11
Control .................................................................................................................................. 12
Statistics ................................................................................................................................ 12
Results ...................................................................................................................................... 13
Statistics ................................................................................................................................ 14
Discussion ................................................................................................................................. 16
The Estrotect®.................................................................................................................... 16
The cameras (video detection) ......................................................................................... 16
Visual observation 3x20 minutes ...................................................................................... 17
Stablemaster routine ........................................................................................................ 18
Future research ................................................................................................................. 18
Conclusion ................................................................................................................................ 20
Acknowledgements .................................................................................................................. 21
References ................................................................................................................................ 22
Appendix................................................................................................................................... 25
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Introduction
The detection of oestrus has been very important for the dairy herd’s profitability. By
an early and correct detection of oestrus (heat), artificial insemination has been carried out
at the right time. Because of its relevance, a lot of literature has been available on this
subject.
Oestrus behaviour is the result of hormonal changes. These hormonal changes are
necessary for ovulation and the implantation of the fertilized oocyte in the uterus.
Good oestrus detection prevents an extended calving interval and decreases the
number of artificial inseminations needed to impregnate the cow again (Roelofs et al., 2010).
This influences the herd’s profitability through better milk recordings and less costs of
insemination.
Not only these factors influence the herd’s profitability, but also the detection of
cows that do not come into oestrus is very important. These cows can be treated sooner so
they can become pregnant sooner as well.
The detection of oestrus in cows has become increasingly difficult. Farmers have
spent less time in the stables and are therefore less likely to see cows in oestrus. The cows
themselves have been showing oestrus for a decreased amount of time (Roelofs et al.,
2010). Especially with high-yielding cows like the Holstein Friesians, this has been an issue.
Consequently, heat detection aids have been becoming more important to help the farmer
to detect oestrus correctly.
The aim of this pilot study was to investigate if the use of heat detection devices
improved the detection rate of oestrus in cows that come into heat compared to the
traditional visual detection and the use of cameras.
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Literature
The cycle of the cow
A cow takes approximately 21 days (18-25 days) for each cycle (Nebel et al., 2004).
Each cycle starts with an ovulation. At the place from which the oocyte bursts out of the
ovarium, a corpus hemorrhagicum (CH) arises. After 3 days the corpus hemorrhagicum will
become a corpus luteum (CL), which produces progesterone (PG) until day 16-17, after
which it goes into regression under influence of prostaglandins (PGF). These prostaglandins
are produced in the endometrium and transported to the ovaria through the arteria uterina
and arteria ovarica with the use of a counter-current system (Kawakami et al., 1995).
As a result the corpus luteum goes into regression and the negative feedback from
progesterone on the hypothalamus stops. The hypothalamus starts producing more
gonadotropic releasing hormone (GnRH) and the hypophysis produces more follicle
stimulating hormone (FSH) and luteinizing hormone (LH). As a result, follicles start growing in
waves and produce more oestrogens.
During each oestrus cycle there are more growth cycles of follicles, some of these
follicles will become dominants and ovulate, but most of these follicles do not develop due
to the influence of inhibin produced by the pre-ovulatory follicle. When the inhibition of
progesterone is gone the pre-ovulatory follicle progresses and LH will start to rise. Together
this will make the follicle capable of ovulating. The pre-ovulatory follicle produces enough
circulating oestrogen that the cow goes into heat (D0).
Typical signs of heat are standing when mounted, mounting of other cows, increased
activity, vaginal mucus, drop in milk yield, hairless spots on caudal part of dorsal ridge and
chin resting. The oestrogens and lack of progesterone cause the peak of LH, which happens
after approximately 6 hours of heat, 28 hours later the ovulation happens (D1) (Pieterse et
al., 2008; Senger et al., 2003).
About 15 days after parturition, a cow will come into heat. This depends on the
nutritional status of the cow (Crowe, 2008). However, this first heat is generally silent,
followed by a shorter cycle than normal. Especially high-yielding Holstein cows are
susceptible to a delayed first heat because of their tendency towards a negative energy
balance (NEB) and lactation. This causes a suppression of LH and therefore delayed
resumption of the normal oestrus cycle (Crowe, 2008; Lucy et al., 1992).
Heat detection
The most important factor in heat detection is the standing heat. This happens only
during 7 hours of heat (Dransfield et al., 1998). Lopez et al. (2004) found even a lower
duration of oestrus (6.2±0.5 hr.) with high-yielding dairy cows. The optimal time to
inseminate a cow is approximately 12 hours after the beginning of the heat (mid until a few
hours after standing heat; Dransfield et al., 1998). This is the so-called “a.m. - p.m. rule”. This
is based on studies which found 27.6hr ± 5.4hr interval between the first standing heat and
ovulation. A minimum of 6 hr is needed for the spermatozoa to reach the oviduct, the place
of fertilization. The spermatozoa on the other hand will survive for approximately 24-30hr.
The follicle will be capable of fertilization for about 20-24hr (Dransfield et al., 2008) (figure
1).
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~27.6hr to ovulation
Fertilization
1st
standing
heat
Insemination
after ~12hr;
survival of
spermatozoa
~24hr
Ovulation
Survival
of follicle
~20hr
Figure 1: Timing of insemination, Dransfield et al., (2008)
The expression of oestrus is declining for a long period of time. In the literature
several reasons for this phenomenon can be found. Wiltbank et al. (2006) have found a
decreased follicular oestradiol production in high-yielding cows. Sangritavong et al. (2012)
have found a different reason, the metabolism of oestradiol 17β is higher in high-yielding
cows, causing a lower oestradiol concentration systemically. The concentration of this
oestradiol is thought to be linked to the expression of oestrus. With lower concentrations
the expression of the standing heat, amongst others, will decline.
Throughout the years there have been many methods available for oestrus detection.
Oestrus detection can be difficult because not all cows show oestrus in the same amount
and not all farmers detect it correctly (Roelofs et al., 2010). The expression of oestrus in
dairy cattle depends on many factors, like heritability, number of lactations, milk production,
lameness, hormonal treatments, nutrition, season, presence of a bull, housing, circadian
variation and herd size (Roelofs et al., 2010). Therefore, it is essential that when oestrus
expression is suboptimal, the detection of oestrus is optimal. The duration of the oestrus in
Holstein cows is becoming less as well as the expression. In a study of Van Eerdenburg et al.
(2002) only 50% of the cows showed standing oestrus. The chance that a cow is seen in
oestrus increases which each cow more in oestrus. This is because of the interaction
between cows in heat (Roelofs et al., 2010). A cow shows more oestrus expression with
increasing parity (López-Gatius et al., 2005). This is however contradicted in a study by Nebel
et al. (1997), which found a longer duration of oestrus in nulliparous heifers compared to
cows.
Pregnant cows
A pregnant cow can give signals that they are in oestrus. This happens because the
follicle waves continue throughout pregnancy. When there are large follicles present in the
ovaries, these produce a large amount of oestrogens, which in turn cause oestrus behaviour.
Approximately 6% of pregnant cows are seen displaying oestrus behaviour (Roelofs et al.,
2010). As a result, the animals are sometimes re-inseminated, causing either abortion or no
effects on the current pregnancy depending on how far into the uterus the artificial
inseminator is when depositing the sperm. If the inseminator gets through the cervix there is
a real chance of losing the pregnancy.
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Pregnant cows can make it easier to detect other cows in oestrus, but also makes the
recognition of true oestrus cyclic cows more difficult. If there are no other cows in heat it is
possible that the pregnant cows jump on the oestrus cows and therefore make it clear to the
farmer that the cow is in heat.
Economic losses
Oestrus detection in dairy cattle is a significant factor in good farming practice.
Failure to detect oestrus will cause a loss of profit. Partly because of lower milk yield, less
calves, a higher culling rate through failure to conceive, costs on replacement heifers and
veterinary costs. An annual loss of more than 300 million dollars is estimated in the United
States (Senger,1994). Esslemont et al. (1993) mention of £ 2.77 loss per day of delay
(extended calving interval) per cow. On a herd of 100 cows with an average of 50 days of
delay it would result in a loss of £ 13,850. This economic loss consists mainly of a lower milk
yield and less calves to be sold. A herd with a higher than average milk yield will result in
even higher costs (£ 2.84 per cow per day; Esslemont et al., 1993). Esslemont et al. (1992)
made a fertility index in which these costs are estimated:
“- Extra culling above 22% (£ 590 per 1%)
- Each day over 360 days calving interval (£ 3 per day per cow)
- Each extra service beyond 2 serves per conception (£ 18 per insemination)”
Dijkhuizen et al. (1985) already considered these kinds of costs. This paper estimates a loss
of 2% in gross production or about 10% of the farmer’s income in the Netherlands.
A different study was made by Galvão et al., (2013). This study compared the profit between
a heat detection of 40% compared to 60%. They concluded a difference of $70 per cow per
year with a price of $0,33/kg milk. On a herd of 100 cows this would mean a $7000 loss. This
is less than estimated by previous studies.
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Methods for oestrus detection
The detection of oestrus can be aided by several methods. Among many others, the
most common of these detection methods are visual detection, cameras, pedometers,
temperature measurements and hormonal measurements (Roelofs et al., 2010). The
detection rate of these aids varies from 37% to 100%. The majority of these methods are
dependent on the oestrus behaviour, mainly the standing when mounted and increased
activity.
Activity measurements
Pedometer
A pedometer is an accurate way to detect oestrus in cows. Nevertheless, it has some
restrictions. The system is less effective in tie-stalls (Felton et al., 2012; Sakaguchi et al.,
2007). The cows cannot show the increased activity as seen in a loose housing system.
Pedometer systems nowadays function in real-time. When the cows are on a pasture during
the day the read-outs may pose some difficulties, resulting in a time lapse between oestrus
and the read-out, causing a delayed detection of the oestrus or a false positive due to
different walking distances to the pasture each day (Sakaguchi et al., 2011). A main cause for
false negative readings is lameness. A lower reading of the pedometer can be caused by high
temperature, increased parity and milk production (López-Gatius et al., 2005). In a research
by Arney et al. (1994) it is however stated that a higher walking activity is present in
increased parity and milk production. Walking activity starts to increase from 80 hours
before oestrus, until the oestrus is finished (Arney et al., 1994). This could mean that a
pedometer has a bigger chance to detect the oestrus in time. It is postulated that a close
relationship is present between management practices and oestrus activity, which in turn is
linked to fertility (López-Gatius et al., 2005). A different kind of pedometer has become
available, the ALT-pedometer. This system does not only detect the activity of the cow, but
also detects the lying time and temperature. In summary, this pedometer could be even
more effective in continuous monitoring compared to traditional ones (Brehme et al., 2008).
Leg pedometers have an efficiency of 52% to 92%, depending on the stable or pasture
conditions (Sakaguchi et al., 2011).
Neck collar
The neck collar is an activity meter as well. It functions similarly to the pedometer. However,
the difference is that the neck collar can also be used for feeding stations and in the milking
parlour. This system has the same limitations and possibilities as the pedometer, although in
a study of Sakaguchi et al. (2007) the accuracy (the number of pedometer detected
oestruses/ the number of pedometer detection alerts * 100%) was at 32% compared to 83%
of the leg pedometer. Both systems had 100% efficiency (the number of pedometer
detected oestruses/ the number of total observed oestruses * 100%). Under paddock
circumstances, the efficiency dropped with both systems to 92%, but had an accuracy of 65%
with the neck collar and 100% with the pedometer. In the tie-stall the neck collar had an
efficiency of 92% with a 34% accuracy, compared to the pedometer which had a 78%
efficiency and accuracy. Depending on which housing-system is present on the farm, an
appropriate system can be chosen.
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Mount detection aids
Mount detection aids are available in several non-technological forms, like Estrotec®,
KaMaR® and tail-paints. There technological forms as well, like HeatWatchII® or
MountCount®. These generally function in real-time. Since they work on the principle that a
cow will be mounted when in oestrus, the research of Van Eerdenburg et al. (2002) is very
significant here. This study states that in some herds more than 50% of the cows will not
show standing heat during oestrus. The fact that not all cows will be mounted, limits all
these methods. A second problem with mount detection aids is that most dairy herds are
still too small to have more than one cow in oestrus at the same time. This limits the amount
of oestrus-activity. There has been considerable research on the accuracy and efficiency of
these methods. These studies are not always carried out with the newest versions of the
available methods. So for the sake of this review, the newest system will be explained, but
results from previous versions will be used.
Estrotec®
A colour marking like Estrotec® functions as a visual confirmation of mounting behaviour in
the cow. Estrotec® is a small patch, approximately 8 x 3 cm, with an adhesive coating on the
bottom and a colour coating on the top. When a cow stands when mounted, the layer on the
Estrotec® rubs off and turns it into a bright colour, like blue or red. These colours are easy to
spot and read. There are, however, some limitations. When an Estrotec® falls off the cow
during oestrus, it will cause a false negative. Furthermore, when a cow uses the automatic
brushes which are present in many stables nowadays, this may either cause a false positive
or may cause the Estrotec® to fall off. The main thing about the Estrotec® is that mounts of
short duration are also included in the result, unlike the Heatwatch® system
(www.estrotect.com).
KaMaR®
The KaMaR® heatmount detector uses a system similar to Heatwatch®; the cow needs to be
mounted for at least 3 seconds to activate the capsule. This KaMaR® capsule turns from
white to red. The KaMaR® is placed at the same position as the Estrotec, between the tail
and hip bones. On the bottom of the KaMaR® is a layer of glue which makes it easy to attach
it onto the cow. The main reason for false negatives is that the KaMaR® falls off. The second
reason is the herd: If there is only one oestrus cow at the same time in the herd, the oestrus
cow will show its heat shorter and less intensely (Roelofs et al., 2010). There is a similar
possibility as seen in Estrotec® that because of the automated brushes the KaMaR® may be
activated, causing false-positives (http://kamarinc.com).
Tail-painting
Tail-painting is a cheap and simple version of Estrotec®. In this case, an easily removable
paint is painted on the backside of the cow, in the same region as the Estrotec®. This paint
mayconsist of chalk, for example. When mounted, the paint will be rubbed off, meaning the
cow is in oestrus. The paint or lack of paint can be easily spotted on the cow. This system is
very easy to apply, but has some difficulties. Since it is easy to rub off, the automated
brushes may cause problems. Moreover, it is also dependent on the weather: chalk for
example will not endure rain, causing a false positive or false negative when the weather is
taken into account by the farmer (Van Eerdenburg et al., 2006).
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HeatWatchII®
HeatWatchII® is an electronic form of a mount detection aid from DDx Inc., Denver. This
system is glued onto the same location and is activated by a mount. The data that is sent to
the computer consists of the cow-number, duration of the mount, and time/date of each
mounting. The system considers the cow in oestrus when mounted at least three times for a
two-second period in a time lapse of 4 hours. This system records real-time, with a range of
about 500 meters which can be enhanced by so-called signal repeaters. The biggest problem
of the HeatWatch® is the possibility of losing the patch, causing false-negative results. The
producer also informs that the glue needs to be the right temperature to stick well; colder
climates may cause difficulties with this system (cowchips.net). About 25% of the
Heatwatch® systems required re-gluing (Rorie et al., 2002). Also according to this research,
100% of the cows were detected when in heat, however in this research the oestrus was
induced.
MountCount®
MountCount® is very similar to HeatwatchII®. This system is not linked to a computer, but
instead it flashes a series of lights. When a cow is mounted, the suspect light starts to flash.
When mounted three or more times in a period of 4 hours, the light of standing heat starts
to flash. The light which indicates the right time for insemination starts 4 hours after the
onset of standing heat. It will stop flashing after 14 hours (Rorie et al., 2002). This system can
work very well. It is easy to interpret and does not need a lot of hardware. There is no
limitation in the range. However, it requires a check of all the cycling cows a least twice
daily. This system is glued to the cow like the HeatWatchII® system.
Accubreed®
Accubreed® functions in the same way as HeatwatchII®, it is a pressure-sensitive system. At
each mount the duration, date, number of the cow and system update is sent to the
computer which collects the data. The data goes through a software program, which
presents the cows in oestrus to the farmer. The detectors can be recharged about 500 times.
The main advantage of this system is their acclaimed range. The data can be transmitted for
several miles, according to the manufacturer. The software uses the data to make lists of the
cows which are in oestrus, the cows having a prolonged cycle, etc. As with the other mount
detection systems, the patches are inclined to be lost at some point. This might cause false
negatives. This system has the same problems with the glue; it needs to be at body
temperature to work well (http://www.estrotect.com/accubreed-home.html).
Showheat®
Showheat® is also a mounting detection system. It functions with one light, which activates
when the cow is mounted three times. The light flashes in a series of 12 seconds. When the
light flashes once in a series, the standing heat has been going on for two hours. With every
flash in the series, another 2 hours are added till a maximum of 18 hours is reached after the
onset of oestrus. With this system, the optimum moment of insemination can be calculated.
It is a system that does not require any additional hardware, is location-independent and is
easy in use. The downside is that the amount of time per cow is quite high because of the
interpretation and the necessity of checking the animals at least twice a day. The Showheat®
is also glued on, meaning a loss of the system may occur (Rorie et al., 2002).
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Visual
Visual observation is the most traditional of all methods. The results vary when used in
research or during the daily functioning of the farmer. The amount of success depends on
three factors. Firstly, the time spent on watching, secondly, the ability of the farmer to
detect the oestrus signs and thirdly is the ability of the cow to display oestrus. In general,
there are a few things to consider when visual observations are used to determine oestrus in
dairy-cows. Cows display most of their oestrus- behaviour when the stable is quiet, so during
milking and feeding it is less likely to see this behaviour. An upside to the various heatmount
detectors, it is possible to observe the other signs of oestrus as well with visual detection,
like mucus, heightened activity and social behaviour towards other cows. A good system for
visual observation has been made by Van Eerdenburg (2006). In this system not only the
mounting is taken into account, but also the other factors. Each factor has a score and when
the total score exceeds 50 points, the cow is considered to be in oestrus.
Video cameras
Cameras are considered almost like a gold standard in oestrus detection, since in principle
the cows are watched 24/7. However there is still room for errors. Not every cow displays
the oestrus behaviour (Roelofs et al., 2010). The identification of a cow on tape is not always
that easy, although this also depends on the uniqueness of the cows on the farm and the
ability of the farmer to identify the cow. During night hours, this system presents even more
challenges in identifying cows, even with infra-red cameras. The second problem is the
coverage of the stable. Every corner needs to be clearly visible on tape to make sure nothing
is missed. This might mean a considerable number of cameras, which all have to be watched
to be of use. This takes a lot of time on a daily basis. In general, not every stable is suited for
this system and not every farming system is suitable for cameras. Especially the use of
pastures causes this system to be completely inadequate.
Temperature
The temperature of a cow changes during the cycle. Just before the oestrus the temperature
becomes lower. During the day of the heat the temperature is high, only to drop again
during ovulation. In the luteal phase the temperature is high again (Wrenn et al., 1958).
However, this fact is difficult to put to use. To adequately use this information, a baseline of
each cow needs to be present. This means every cow’s temperature needs to be taken at
least once a day to make any predictions about oestrus detection. A system which has been
in use for several years is the ALT-pedometer. In this system the activity, lying time and
temperature are all used to come to a conclusion about the cow in oestrus (Brehme et al.,
2008).
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Aim of the study
The aim of this pilot study was to investigate if the use of a heat-mount device
(Estrotect®) improved the detection rate of oestrus in cows compared to traditional visual
detection and the use of cameras.
Materials and methods
In this research project, four methods of oestrus detection were compared on the
Teaching and Research Farm Kremesberg (LFG). Kremesberg has been renovated in 2010 as
a free-stall barn with cubicles and straw bedding. There was a small outdoor patio available
for the cows. The floor was covered with rubber mats. An automatic feeding system was
present with two additional feeding stations for concentrates. The cows were milked twice a
day in the milking parlour. The average milk yield was over 9000 kg per year.
Animals
In this study 70 Simmental and Holstein Friesian dairy cows have been included.
These were the cyclic cows at the LFG. Cows were allocated to the research 10 days after
parturition (Wiltbank et al., 2011). When a cow had been found pregnant at approximately
42 days of gestation, she was taken out of the research.
Experimental design
The first method of heat detection was a visual detection, performed three times a
day (morning 08.00, midday 13.00 and afternoon 15.00), involving 20 minutes of watching.
During these periods there were no special activities like milking. If a cow was standing when
mounted, the animal was considered to be in oestrus.
The second method was carried out through 3 video cameras in the stables, 24 hours
a day; these recordings were watched every morning during the research. When a standing
animal was seen on the video for at least 3 seconds, this animal was considered to be in
oestrus. The videos were stored on hard drives to make it possible to watch them at a later
time when necessary.
The third method was colour markings on the cow, Estrotect®. The Estrotect® was
placed in a pouch to prevent bias with visual detections. This pouch was attached to the cow
with special glue, Mastix®. In case the pouch fell of the cow, the result of that day was taken
out of the database. When a pouch fell off two times, a normal Estrotect® was placed on the
cow. Practically this meant that almost every cow detected with Estrotect® had no pouch.
Every morning at 09.00 the Estrotect® was checked. If the Estrotect® was rubbed off 50% or
more, the animal was considered to be in oestrus. When in doubt or when the pouch had
fallen off the cow, the animal was taken out of the research for this cycle regarding the
Estrotect® results.
The fourth method was the normal routine check by the stable personnel. The
personnel watched the cows during their daily work. When considered in oestrus the
number of the cow and the day of detection has been noted.
The first and the fourth method have been done by two persons to prevent bias. The
second and third method has been done by the same person. The Estrotect® was judged
before watching the videos. The watching of the videos was considered unbiased because
there is no room for interpretation.
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Control
Each day around noon the animals which had been registered to be in oestrus by at
least one of the four methods in the previous 24 hours were checked to make sure there
were no false positives.
This was done by gynaecological examination with an ultrasound to determine if
there was either a corpus luteum (CL) or follicle (>10mm) present in the ovaries, but also if
there was a tonus of uterus and outflow of mucus. If there was a CL present there can be no
oestrus at the same moment (false positive). If there was no CL but a follicle present (>10
mm) the animal was considered to be in oestrus (true positive).
In addition, a blood sample was drawn from the coccygeal vein and later analysed for
concentrations of progesterone. If concentration of progesterone was > 1 ng/ml, these
animals were considered not to be in oestrus (false positive). However, when the
progesterone was slightly above 1 ng/ml (<1.3 ng/ml) the result of the ultrasound was
indicative for the end result. Meaning, if the cow has a progesterone concentration of 1. 1
ng/ml with a positive ultrasound, the cow was considered to be in oestrus even though the
concentration of progesterone was too high.
Statistics
The oestrus detection efficiency by using different strategies has varied between 37
to almost 100% (Roelofs et al., 2010). We have assumed that the difference between the
best and worst strategy was 20%-points. The difference was considered to be statistically
significant at a power of 80% with P<0.05. The data has been statistically analysed with Excel
using the chi2 test. The data was analysed with the help of a contingency table based on
table 1, each method separately being tested.
A detection event was specified when at least one of the four methods was positive
for a cow. An accurate detection was either a positive result with a method in agreement
with the control (true positive) of or a true negative result in agreement with the control
(true negative). The accuracy was the number of accurate detections/total number of
detection events x100% within each detection method. False positive was a positive
detection event of a non-existing oestrus, an oestrus being based on a combination of the
results of the blood progesterone and ultrasound as previously mentioned. A false negative
was no detection with a method even though the cow was in positive in oestrus with
another method.
Specificity was calculated by the true negatives/ (true negative detection events +
false positive detection events of the method) x100%. The sensitivity of a method was
calculated by the true positives/ (true positives detection events + false negatives detection
events the method) x100%.
Comparison of four heat-detection methods on an experimental dairy farm in 12
Austria| Materials and methods
Results
In total, the cows showed 107 detection events of which 60 were true oestrus events
(appendix 1). A detection event was present when at least one of the four methods was
positive on a cow. The total detection events were different with Estrotect® and the cameramethod. This was due to loss of the Estrotect® and technical difficulties with the cameras for
one week.
Several cows had multiple detection events (appendix 1). Six cows were detected
more than five times each and 12 cows were detected between 3-5 times. In total detection
events were seen in 33 cows. Cow number 39 had the most false positive results. Seven of
the nine false-positive results were caused by the Estrotect®, in these cases none of the
other methods were positive.
Stable
master
Estrotect®
Camera
3x 20 min.
True
positives
37 (35%)
False
positives
14 (13%)
False
negatives
21 (20%)
True
negatives
35 (33%)
Total
34 (39%)
27 (29%)
22 (21%)
41 (47%)
3 (3%)
6 (6%)
8 (9%)
23 (25%)
39 (36%)
4 (5%)
40 (43%)
40 (37%)
87 (100%)
93 (100%)
107 (100%)
107 (100%)
Table 1. Number and percentage of true positives, false positives, false negatives and true negatives and total number of
detection events specified for each method.
The number of accurate detections differed between 39 and 71 per method; these
have been either true positive or true negative. A big difference was found in the accuracy of
the different methods. The best detection method was at 58% accuracy (3x20 min method)
while the worst one was at 44% accuracy (Estrotect®).
Stablemaster
Estrotect®
Camera
3x 20 min.
Sensitivity (%)
63
80
54
36
Specificity (%)
71
9
93
87
Table 2. Sensitivity and specificity in % for each method.
The sensitivity was highest for the Estrotect® (80%), when a cow was in oestrus there
was an 80% chance that oestrus was detected by the Estrotect®. The 3x 20 min. visual
detection method had the lowest sensitivity (36%), meaning there was a high chance of
missing an oestrus with this method (table 2).
The Estrotect had the lowest specificity (9%), meaning there was a high chance of a
false positive result when a detection event has occurred. The video detection method had
the highest specificity, meaning there was a high chance that when a negative result occurs
the cow is truly not in oestrus (table 2).
Comparison of four heat-detection methods on an experimental dairy farm in 13
Austria| Results
The stablemaster routine detected most oestrus events with an accuracy of 66%. This
system functioned throughout the pilot. In total this system had 37 true positive detections.
When these results were combined with the Estrotect® method there were a total of 56 true
positive detections. This meant an extra 19 cows were detected correctly on a total of 56
(+34%) (appendix 1).
The Estrotect® had the lowest accuracy (44%); this was partially due to the loss of the
Estrotect® during the trial. All the cows that lost the Estrotect® at a detection event were
excluded in the evaluation of the Estrotect®. This has been further discussed in the
discussion. Another important observation regarding the Estrotect®, was that of the 60
detected true positive cows, 15 cows were detected by the Estrotect® only, and not by any
of the other methods (appendix 1). This was 25% of all the true positive detected cows.
The cameras had a high accuracy of 73%. The cameras were not active during the first
week of the trial because of technical difficulties. Therefore the total number of detection
events was not 107. When the camera method was combined with the Estrotect® there
were 44 true positive detections instead of the 27 with just the use of cameras (appendix 1).
Meaning 17 (+39%) more detections compared to only using cameras.
The 3x20 min. visual detection had the second highest accuracy, i.e. 58%. The
number of accurate detection events was 62. In total this method has detected 22 true
positive oestruses (appendix 1). When the 3x 20min. visual detection method was combined
with the Estrotect® a total of 50 true positive oestrus were detected. This was 28 true
positive detections more, i.e. an increase of 56% (table 3).
Method
Number of true positive detections
+ Estrotect®
Stablemaster
37
56 (+34%)
Camera
27
44 (+39%)
3x20 min.
22
50 (+56%)
Table 3. Number of true positive detection events and the number of true positive detection events when the methods
were combined with Estrotect® in number and percentage.
Comparison of four heat-detection methods on an experimental dairy farm in 14
Austria| Results
Statistics
All the methods were not statistically significant (table 4); no association was found
between the result of any method and a true positive or true negative oestrus. This was
done through statistical analysis (Chi 2 -test) based on table 1. All four methods had a p>0.05
(table 4). That was why it will not be discussed further in the analysis.
The confidence interval (CI) has been calculated with a 95% probability. This meant
that with the mean found in our research, this interval included 95% of the population. The
CI represented the possibility that a positive result (positive detection) was a true positive
oestrus detection. For example: the camera method had a true positive result in 31.2% to
62.8% of the cases (table 4). The smaller and higher this CI was, the more meaningful its
results were when interpreting. The camera method had the highest confidence interval
(31.2 - 62.8%). The second highest was the stablemaster method; this method had a
confidence interval of 17.4-52.9 %. The Estrotect® had a negative confidence. The second
lowest confidence interval was the 3x20 minutes method, in which the confidence interval
was between 7.5-38.5%.
Method
Stablemaster
Chi2-test
2.29 E- 4
p-value
1.00
Confidence Interval (CI)
17.4 - 52.9%
Estrotect®
0.170
0.865
-50.3 - 7.6%
Camera
1.320E-06
1.00
31.2 - 62.8%
3x20 min-method
0.007
0.992
7.5% - 38.5%
Table 4. The Chi2 was based on the results on the true positive, false positive, true negative and false negative results of
each method (table 1) with the p-value and confidence interval (CI).
The significance of each method in combination with the Estrotect® was tested using
the
on the same data (table 1). This was done to see if the added value of the
Estrotect® caused the methods to become statistically significant. However there was still no
association found between the results of this combination and a true positive or true
negative oestrus. All combinations of Estrotect® and the other methods had a p>0.05 (table
5).
Chi2-test
Method
Stablemaster + Estrotect®
Camera + Estrotect®
3x20 min. + Estrotect®
Chi2-test
3.690E-04
3.128E-04
0,047
p-value
1.00
1.00
0.96
Table 5. The Chi2 was based on the results on the true positive, false positive, true negative and false negative results of
each method (table 1) with the p-value.
Comparison of four heat-detection methods on an experimental dairy farm in 15
Austria| Results
Discussion
Of the four methods used in this research, each had some difficulty in the implementation.
Below, each method will be discussed, taking into account its difficulties. The results
mentioned previously were dependant on our definition of total detection events. Since not
all cows were checked daily it is possible that a few oestruses were missed during our study.
And therefore the percentage of detection events depicted in our study maybe higher than
the actual situation.
The Estrotect®
The main problem with the Estrotect® was the presence of a brush in the stable. The
cows used this brush frequently causing the greater part of the false positives in this
research.
The Estrotect® losses were also considerable; approximately 10% of the Estrotect®
were lost each day during the research. This could have been due to mounting activity since
a high number of cows which lost their Estrotects® were in oestrus at that moment.
Checking the Estrotect® twice a day, or the use of a better fixation method, could decline the
chance of a false negative result. This would have made the chance of a false negative less
likely to occur. Checking could be done fairly easy while herding the cows together for
milking. When a milking robot was present, it would have been necessary to go into the
stable and check the cows.
Comparable research by Cavalieri et al. (2008) on heatmount systems had a
sensitivity of 85.7% and 91.3% with the use of Heatwatch® and tail-painting across three
separate herds. This was high compared to this research, which could be explained by the
fact that Cavalieri et al. (2008) only used resynchronised heifers on pasture conditions.
Bonato et al. (2012) compared Estrotect® with visual controls (2 hours daily) in 58
cows, which were synchronized using a timed artificial insemination system. They detected
no significant differences between the visual observation and the use of the Estrotect®.
According to this article, both methods could have been used with the same results. They
detected approximately 87% of the oestruses.
Even though the Estrotect® had no statistical significance it still could be of use. In
this study 25% of the true positives were only detected by the Estrotect® and not by any of
the other methods used. Under different circumstances (no brushes present) the usage of
Estrotect® could have given better oestrus detection results.
A second remark that was important were the priorities of the farmer. If a high
number of false positives (extra work) were not an issue for the farmer, the added value of
possibly more true positive detections could have been important. However if this extra
work was regarded as a downside it might have been better to advise the farmer to not use
Estrotect®.
The cameras (video detection)
The second method entailed video detection by cameras. These were placed at eight
different locations in the stable. Two had an overview that was watched daily, the remaining
cameras were only used when the identification of a cow proved to be difficult. With the
cameras, mostly true positives were detected (n= 27) out of 30 in total. But because of the
incomplete coverage of the patio, a few oestruses were not detected.
Comparison of four heat-detection methods on an experimental dairy farm in 16
Austria| Discussion
The biggest problem with this system was the amount of time it required. Each video
took about 1.5 hours to be watched completely, making this a very ineffective system for the
farmers who keep getting busier every day. However, this could have been a good fail-safe
system. If a farmer was unsure which cow is in heat, this could have been checked relatively
easy through the tapes. The down-side of this was the initial investment, not only the
hardware, but also the software was quite expensive. The upside was that when the
investment was made, no alterations have to be made to keep it up to date.
In comparison with a research from Bruyére et al. (2012), our camera system has
lacked the programs for efficient detection. Bruyére et al. (2012) used a program called
“camera-icons”, which made it possible to analyse the data of four cameras of one day in 8
to 32 minutes. This program took a snap-shot each ten minutes. About 80% of the ovulations
were detected (standing heat)using a milk progesterone concentration control twice a week
and classical visual detection. This study had adequate cameras for night oestrus detection.
This could have been one of the reasons for the relatively high detection rate. Another
reason could have been the straw litter on the floors, in comparison to our rubber floors
which has made it easier to display oestrus behaviour.
The big downside of “camera-icons” was that not all cows will show standing heat,
making the other signs of oestrus more important (Sveberg et al., 2011; Van Eerdenburg et
al., 2011). These signs, however, were not easily recognised by camera programs. Possibly in
the future programs will be developed to interpret multiple signs. According to research
from Ranasinghe et al. (2010) only 2/3 of the cows showed oestrus signals.
There were a few limitations in our study; one of them was the identification of the
cows. But when a farmer knows its cows, this should not have been a problem. Second was
the darkness, although we were using infra-red cameras and the emergency lighting it was
very difficult to identify the cows. The third was that this system only worked well when the
cows were always within sight of the cameras. This was not the case at Kremesberg, the
patio was not observed, causing a few false negatives in the detection.
Visual observation 3x20 minutes
The third method of observation had the least true positives. This was the 3x20
minutes observation time daily. As seen by Van Eerdenburg et al. (2002), about half of the
cows in heat were missed. In this study only 27 true positives were seen. In total 62
detection events were available with this method, with an accuracy of 58%. One of the
difficulties with this method of detection was that it was not divided over the day with 8
hours between each observation event. Instead it was done in a practical manner,
resembling the opportunities of a farmer to go into the stable and actually watch the cows.
As a result, however, this method was less likely to detect the oestrus. These events were
consistent with the fact that a cow was more likely to show oestrus signs during moments of
quiescence in the stable.
Interesting was that through the surveillance of the cameras, it was noticed that the
observer was at the right time in the stables with some of the standing heats. This standing
heat was not always noticed because the observer was in the wrong part of the stable
and/or looking the wrong way. This meant that a lot depends on the observer in detecting
the oestrus.
In the review of Roelofs et al. (2010), it was mentioned that with a visual observation
two times a day it was possible to detect 94% of the oestrus cows. This was done with the
Van Eerdenburg system. This system has not only taken into account standing heat, which
Comparison of four heat-detection methods on an experimental dairy farm in 17
Austria| Discussion
has explained a part of this high percentage. In comparison to a different study in this review
of visual observation of three times a day for 30 minutes, in which just standing heat was
used, only 14% of the oestruses was detected. When this same observation period was used
with all the components of the Van Eerdenburg system, which included other signs of
oestrus, this system had a detection rate of 74% of the oestruses (Roelofs et al., 2010). A
different study in this review, which only used visual observations twice a day for 30
minutes, had a detection rate of 19% (Roelofs et al., 2010). It was preferable to use periods
of observation longer than 20 minutes.
Stablemaster routine
The fourth method of detection was the normal stable routine at LFG Kremesberg. All
the employees who work in the stables were aware that the cows can come into heat. And
that it was necessary to be alert while working, by writing down the number of the cow
which has shown standing heat. During the day, this system was very effective because
someone was present almost continuously. In the evenings and during the weekends it was
more likely that an oestrus was missed.
However, this method proved to the most effective in this study. The only problem
was that in normal stables, compared to this teaching and research farm, the amount of
time this method costs was not feasible. A way to make the system more effective is by
implementing the Van Eerdenburg system.
A second remark to the stablemaster routine was the high number of detection
events compared to the 3x20 min. visual observation and the video detection. Even though
this method had 14 false positive detection events, the high number of detection events
might have been more important to the farmer than the possibility of missing an oestrus.
When Estrotect® was combined with the stablemaster method this resulted in more
than a third added true positive detections. It could have been of added value to use
Estrotect® even in this system with a high number of positive detections. This however was
dependent on the farmer (management system), the stable/pastures and cows.
Future research
In future research there should be a gold standard present; otherwise nothing can be
said about the sensitivity and specificity of a method. This can be done by daily progesterone
testing either in milk or serum. This is necessary to create a baseline. The best evidence for a
heat is a low progesterone (<1.00 ng/ml) after a period of high progesterone (>1.00 ng/ml).
The use of ultrasonography should only be applied in cases with doubtful
progesterone, since its use has restrictions. The skill of the individual researcher and also
inter-researcher differences could lead to subjective ultrasound results.
Also, an activity meter should be included in the research, which allows the several
types of heat detection to be compared under the same circumstances. Activity
measurement aids like a neck collar or pedometer were not included in this study due to the
location, on average a farm has 20 milking cows in Austria
(http://ec.europa.eu/agriculture/rica/pdf/Dairy_report_2012.pdf). A system like the
pedometer would be very expensive in use per cow. But for a good comparison on larger
farms it is necessary to include this method as well.
The “3x20 min” visual observation method should be changed to half hour periods.
This is necessary to ensure that this method can compete with the other methods in the
detection rate.
Comparison of four heat-detection methods on an experimental dairy farm in 18
Austria| Discussion
In heat detection methods there are a lot of new developments and to be sure of the
most effective way of detection, these new methods need to be compared to the existing
ones. In addition to this, the cows change with time: there are more cows not displaying
classic mounting behaviour (Van Eerdenburg et al., 2002). It means that some of our current
methods are not up to date to deal with this changing situation. This is why further research
needs to be done to find new possible ways of oestrus detection and to compare these with
the existing methods.
Comparison of four heat-detection methods on an experimental dairy farm in 19
Austria| Discussion
Conclusion
In conclusion, there were a lot of systems available to enhance heat detection for a farmer.
The activity measurement method was not included due to the location of this study. All four
methods have not resulted in statistical significant differences in detection and no
association was found between the result of each method and a true positive or true
negative oestrus. In this research the conventional visual detection (stablemaster) system
had the best results. The video detection system by cameras had very good results as well,
even though there were quite a few practical limitations present. For practical reasons, only
the visual system was applicable, meaning the 3x20 minutes method and the stablemaster
method in this stable. The usage of aids like Estrotec® for heat detection can be of added
value if the conditions of the stable or pasture are taken into account. To determine which
method has the best influence on heat detection rates is difficult, for this depends on
management, environmental, and animal factors. More research is needed for a conclusive
advice.
Comparison of four heat-detection methods on an experimental dairy farm in 20
Austria| Conclusion
Acknowledgements
I would like to thank prof. dr. M. Drillich for giving me the opportunity of doing this research
at LFG Kremesberg. Secondly I wanted to thank Herr Huber for all the friendly advice during
my research and all the help in the farm, practically as well as content wise. Thirdly I would
like to thank dr. H. Jonker for helping me all the way and pushing me to get it finished. At last
I would like to thank all the people of Kremesberg, for taking me in and making me feel very
welcome amongst them.
Furthermore I would like to thank my family and friends who have supported me through
this journey and back home again!
Comparison of four heat-detection methods on an experimental dairy farm in 21
Austria| Acknowledgements
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Appendix
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Stablem Estrot
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Came Ultras
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P
P
P
P
P
N
P
N
P
P
N
P
P
P
P
P
P
P
P
N
N
N
N
P
P
P
N
N
N
P
N
N
N
P
P
N
P
N
P
N
N
P
N
N
P
Blood
progesterone
(ng/ml)
2,69
X
1,07
0,49
0,74
0,22
0,49
0,55
0,59
0,68
0,3
1,12
0,34
0,52
0,81
3,79
5,19
0,87
1,02
1,07
4,06
0,47
1,4
0,55
0,54
1,68
1,21
0,16
0,43
7,01
3,83
1,03
0,78
5,75
1,07
0,33
6,91
2,16
0,54
Oest
rus
N
P
P
P
P
P
P
P
P
P
P
P
P
P
P
N
N
P
P
P
N
P
N
P
P
N
N
P
P
N
N
N
P
N
N
P
N
N
P
Comparison of four heat-detection methods on an experimental dairy farm in 25
Austria| Appendix
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
39
29
92
63
25
14
45
32
60
75
39
29
52
74
21
28
45
87
60
25
19
25
39
45
92
19
37
86
78
73
45
74
39
78
25
14
45
60
92
39
83
78
74
32
21
N
P
P
P
P
N
N
N
N
P
N
N
P
P
P
P
P
P
P
P
P
P
N
P
N
N
P
P
N
P
N
N
N
P
N
N
N
P
N
N
P
N
N
N
N
P
P
L
L
N
P
P
P
P
L
P
P
P
P
L
P
P
N
P
L
L
P
P
P
P
N
L
L
L
P
P
P
P
L
L
P
P
P
P
P
L
P
P
P
P
N
N
N
P
N
N
N
N
N
P
N
N
N
P
P
N
P
N
N
N
P
N
N
N
N
N
P
P
N
N
N
N
N
N
N
N
N
N
N
N
P
N
N
N
N
N
N
P
P
N
N
N
N
N
P
N
N
N
P
P
N
P
N
N
P
P
P
N
P
N
P
P
P
P
P
N
N
N
P
P
N
N
P
N
N
P
N
N
N
N
N
N
N
P
N
N
N
N
N
P
N
N
U
P
P
P
P
P
N
N
P
N
N
P
N
P
P
P
P
P
N
N
N
C
P
P
N
P
N
N
P
N
N
N
N
2,21
3,2
1,02
0,49
5,97
0,27
3,06
7,67
3,53
0,89
6,05
4,54
0,32
0
1,57
0,71
0,33
0,58
3,15
0,39
0,56
0,28
9,17
0,33
1,94
0,49
0,55
0,43
0,13
0,79
1,9
3,43
4,98
0,11
0,28
0,99
3,91
0,08
4,07
6,58
0,24
0,86
3,99
8,09
5,94
N
N
N
P
N
P
N
N
N
P
N
N
P
P
N
P
P
P
N
P
P
P
N
P
N
P
P
P
P
P
N
N
N
P
P
P
N
P
N
N
P
P
N
N
N
Comparison of four heat-detection methods on an experimental dairy farm in 26
Austria| Appendix
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
19
9
40
29
39
52
93
40
74
92
39
21
52
93
39
14
45
32
92
14
19
63
86
N
N
N
P
N
P
N
P
N
P
N
N
N
N
P
P
P
N
N
P
P
N
P
P
P
N
L
P
P
P
L
P
P
P
P
P
P
P
P
P
P
P
P
L
P
N
N
N
P
P
N
P
N
P
N
N
N
P
N
N
N
N
N
N
N
N
N
N
N
N
N
P
P
N
N
N
P
P
P
N
N
N
N
N
N
P
N
N
N
N
N
N
N
P
U
P
N
P
N
P
P
P
N
N
P
U
P
P
N
N
N
P
P
P
N
1,24
0,56
0,24
0,25
10,71
3,48
2,47
0,37
0,9
0,46
1,28
7,32
0,69
3,21
1,56
0,55
0,41
6,57
0,49
0,09
0,67
0,21
5,12
N
P
P
P
N
N
N
P
P
P
N
N
P
N
N
P
P
N
P
P
P
P
N
Appendix 1. Results per method per cow and end results if the cow is in oestrus according to blood progesterone and
ultrasound. In the table there are several possible results: P= positive; N=negative; X= no data available; L= lost;
U=unsure; C=cyste.
Comparison of four heat-detection methods on an experimental dairy farm in 27
Austria| Appendix