novel approaches to expression and detection of oestrus in dairy cows

Daily mounting activity determined by the optimised script is reported in
Tables 5.5 and 5.6. These results showed that the automated script
analysis declared mounting on all days of the trial where mounting only
occurred on one day in POC 2 and over 2 days in POC 3. 97 out of 173
declared mounts were visually observed mounts during POC 2 on one day
when cows were in oestrus and 62 out of 117 declared mounts were
visually observed mounts during POC 3 spanning 2 days when cows
exhibited oestrous behaviour. These results again suggested 50% error in
the accuracy of the script. In Tables 5.5 and 5.6, however, an important
pattern to note is that the level of error was increased on the days when
cows were in oestrus compared to days of no oestrus activity. Furthermore
the number of erroneously declared mounts by the script was substantially
increased for cows in oestrus compared to those not in oestrus.
Results of the automated analysis of UWB data are reported in Table 5.7 to
detect cows that are in oestrus rather than individual events of mounting
and standing to be mounted. Oestrous data was reported by cow number
and in order of date and real-time of onset of mounting. Onset of standing
to be mounted was also reported. The results show that 9 out of 10 cows
were correctly identified in oestrus by the script; confirmed by visual
observation, milk progesterone concentration of 10ng/ml (Figure 5.7 and 5.8), and no activity
increase from baseline (Figure 5.10 and 5.11). Two cows that were
synchronized to come into oestrus but did not show any overt signs of
oestrus were also identified not in oestrus by UWB automated analysis.
These cows had no increase in activity but progesterone concentration was
low, which may indicate a silent oestrus.
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