novel approaches to expression and detection of oestrus in dairy cows
novel approaches to expression and detection of oestrus in dairy cows novel approaches to expression and detection of oestrus in dairy cows
ate decreased dramatically to an average of 53.5%, without any increase in script accuracy; therefore the optimised script resulted in the best oestrous detection. From the daily analysis of the reports of oestrus, mounting and standing behaviour, it was clear that the incidence of erroneously declared mounting was more prevalent on days when cows were actually in oestrus (Tables 5.5 and 5.6). It was also clear that more error was attributed to cows that came into oestrus during the trial compared to control cows not in oestrus. The increased error during oestrus could be due to the number of cows in oestrus at once; for example increased number of cows in oestrus increases oestrous expression (Hurnik et al., 1975;Van Vliet and Van Eerdenburg, 1996), which may mean that the error is a result of the experimental design because 6 cows were synchronised to come into oestrus simultaneously. In a commercial situation the number of cows in oestrus together may be lower which could result in a more accurate detection rate. Spikes in the data affect the accuracy of mounting detection because in the UWB data error spikes are recorded in the same format as actual mounts and so could be mistaken. Falsely declared mounts were investigated by looking at the MU position on CCTV cameras. This showed that there was no pattern associated with the error spikes. Potential reasons could be attributed to a cows’ position in an area of poor geometry in the dairy barn (Figure 4.9), or due to non-line of sight from the master unit (Harmer et al., 2008). Because the dairy farm is a complex environment with various obstructions, line of sight may become obstructed and thus cause reflections in the signal which give false or less accurate 3D positions. Although, filtering by script analysis can help to eliminate these spikes. Upon further analysis of the data from POC 2 and POC 3 it appears that data quality in POC 2 was better than POC 3, therefore data from POC 3 required further filtering. It is obvious that 2 MUs in POC 3 had higher incidence of error and more false positives were declared by script analysis (Cow 38 and 623; control cow and oestrus cow). These particular MUs were tested against a normally functioning MU in the same BU network in the same positions of good geometry; results revealed no difference in position or error, leading to the conclusion that error could be attributed to a cow’s favoured position within the location, which could be an area of poorer geometry and more obstruction. Furthermore it is possible that 140
when the cows lie down, especially in areas of poor geometry, that MUs may lose line of sight with the master unit or become obstructed by other objects or cows, which could contribute to error. In summary, these results were extremely important in providing the basis for automated detection of oestrus by UWB; because error increased on days of oestrus and was associated with cows in oestrus. Hence a clear distinction could be made between those cows in oestrus, thus eligible for AI, and those that were not showing behavioural oestrus. 5.4.2 UWB for Automated Oestrous Detection This novel method of oestrous detection accurately detected all the 9 cows in oestrus in this study using automated analysis of UWB data. The herd level script was developed to detect oestrus in a group of cows following analysis of UWB error, which as discussed increased with oestrous behaviour. The herd level script can detect initial mounting behaviour and therefore identify cows coming into oestrus. If mounting and/ or standing to be mounted continue and occur again within 3 hours the cow is deemed in oestrus. In total 10 cows displayed oestrus during trials POC 2 and 3, although one cow was discounted from analysis (as explained above), therefore these results show that 9 out of 9 cows (100%) can be detected in oestrus automatically by continuous monitoring of cows’ 3D position to detect mounting and standing to be mounted. In support of these results 6 out of 6 cows were also correctly identified as not being in oestrus. Time of onset of oestrus, mounting, can also be determined although this requires further investigation. This sign of oestrus is important to monitor because mounting and disorientated mounting are more intense behaviours which are displayed around the time of oestrus (Van Eerdenburg et al., 1996). Therefore in the case that standing to be mounted is not displayed, as fewer cows stand to be mounted (Dobson et al., 2008), mounting can be a useful indicator of oestrus. Importantly standing to be mounted can also be identified, as this is the definitive sign that a cow is in true oestrus (Orihuela, 2000). Standing to be mounted is also the most closely related sign of oestrus to ovulation and therefore if the time of onset is known provides an accurate prediction for ovulation and when to AI. Standing heat occurs 26.4 hours before ovulation (Roelofs et al., 2005), showing how real-time UWB detection of standing to be mounted can be a useful predictor for time of AI which is required in order to maximise conception 141
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when the <strong>cows</strong> lie down, especially <strong>in</strong> areas <strong>of</strong> poor geometry, that MUs<br />
may lose l<strong>in</strong>e <strong>of</strong> sight with the master unit or become obstructed by other<br />
objects or <strong>cows</strong>, which could contribute <strong>to</strong> error.<br />
In summary, these results were extremely important <strong>in</strong> provid<strong>in</strong>g the basis<br />
for au<strong>to</strong>mated <strong>detection</strong> <strong>of</strong> <strong>oestrus</strong> by UWB; because error <strong>in</strong>creased on<br />
days <strong>of</strong> <strong>oestrus</strong> <strong>and</strong> was associated with <strong>cows</strong> <strong>in</strong> <strong>oestrus</strong>. Hence a clear<br />
dist<strong>in</strong>ction could be made between those <strong>cows</strong> <strong>in</strong> <strong>oestrus</strong>, thus eligible for<br />
AI, <strong>and</strong> those that were not show<strong>in</strong>g behavioural <strong>oestrus</strong>.<br />
5.4.2 UWB for Au<strong>to</strong>mated Oestrous Detection<br />
This <strong>novel</strong> method <strong>of</strong> oestrous <strong>detection</strong> accurately detected all the 9 <strong>cows</strong><br />
<strong>in</strong> <strong>oestrus</strong> <strong>in</strong> this study us<strong>in</strong>g au<strong>to</strong>mated analysis <strong>of</strong> UWB data. The herd<br />
level script was developed <strong>to</strong> detect <strong>oestrus</strong> <strong>in</strong> a group <strong>of</strong> <strong>cows</strong> follow<strong>in</strong>g<br />
analysis <strong>of</strong> UWB error, which as discussed <strong>in</strong>creased with oestrous<br />
behaviour. The herd level script can detect <strong>in</strong>itial mount<strong>in</strong>g behaviour <strong>and</strong><br />
therefore identify <strong>cows</strong> com<strong>in</strong>g <strong>in</strong><strong>to</strong> <strong>oestrus</strong>. If mount<strong>in</strong>g <strong>and</strong>/ or st<strong>and</strong><strong>in</strong>g<br />
<strong>to</strong> be mounted cont<strong>in</strong>ue <strong>and</strong> occur aga<strong>in</strong> with<strong>in</strong> 3 hours the cow is deemed<br />
<strong>in</strong> <strong>oestrus</strong>. In <strong>to</strong>tal 10 <strong>cows</strong> displayed <strong>oestrus</strong> dur<strong>in</strong>g trials POC 2 <strong>and</strong> 3,<br />
although one cow was discounted from analysis (as expla<strong>in</strong>ed above),<br />
therefore these results show that 9 out <strong>of</strong> 9 <strong>cows</strong> (100%) can be detected<br />
<strong>in</strong> <strong>oestrus</strong> au<strong>to</strong>matically by cont<strong>in</strong>uous moni<strong>to</strong>r<strong>in</strong>g <strong>of</strong> <strong>cows</strong>’ 3D position <strong>to</strong><br />
detect mount<strong>in</strong>g <strong>and</strong> st<strong>and</strong><strong>in</strong>g <strong>to</strong> be mounted. In support <strong>of</strong> these results 6<br />
out <strong>of</strong> 6 <strong>cows</strong> were also correctly identified as not be<strong>in</strong>g <strong>in</strong> <strong>oestrus</strong>.<br />
Time <strong>of</strong> onset <strong>of</strong> <strong>oestrus</strong>, mount<strong>in</strong>g, can also be determ<strong>in</strong>ed although this<br />
requires further <strong>in</strong>vestigation. This sign <strong>of</strong> <strong>oestrus</strong> is important <strong>to</strong> moni<strong>to</strong>r<br />
because mount<strong>in</strong>g <strong>and</strong> disorientated mount<strong>in</strong>g are more <strong>in</strong>tense behaviours<br />
which are displayed around the time <strong>of</strong> <strong>oestrus</strong> (Van Eerdenburg et al.,<br />
1996). Therefore <strong>in</strong> the case that st<strong>and</strong><strong>in</strong>g <strong>to</strong> be mounted is not displayed,<br />
as fewer <strong>cows</strong> st<strong>and</strong> <strong>to</strong> be mounted (Dobson et al., 2008), mount<strong>in</strong>g can<br />
be a useful <strong>in</strong>dica<strong>to</strong>r <strong>of</strong> <strong>oestrus</strong>. Importantly st<strong>and</strong><strong>in</strong>g <strong>to</strong> be mounted can<br />
also be identified, as this is the def<strong>in</strong>itive sign that a cow is <strong>in</strong> true <strong>oestrus</strong><br />
(Orihuela, 2000). St<strong>and</strong><strong>in</strong>g <strong>to</strong> be mounted is also the most closely related<br />
sign <strong>of</strong> <strong>oestrus</strong> <strong>to</strong> ovulation <strong>and</strong> therefore if the time <strong>of</strong> onset is known<br />
provides an accurate prediction for ovulation <strong>and</strong> when <strong>to</strong> AI. St<strong>and</strong><strong>in</strong>g<br />
heat occurs 26.4 hours before ovulation (Roel<strong>of</strong>s et al., 2005), show<strong>in</strong>g<br />
how real-time UWB <strong>detection</strong> <strong>of</strong> st<strong>and</strong><strong>in</strong>g <strong>to</strong> be mounted can be a useful<br />
predic<strong>to</strong>r for time <strong>of</strong> AI which is required <strong>in</strong> order <strong>to</strong> maximise conception<br />
141
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