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Detection of oestrus is a key determinant of profitability of dairy herds, but is increasingly difficult to observe in the modern dairy cow, with shorter duration and less intense oestrus. Current trends in the dairy industry also exacerbate the problem of poor oestrous detection as herd sizes are increasing, yet there is less labour on the farm. As a consequence fewer cows are seen standing to be mounted, the definite sign that a cow is in oestrus. Concurrent with the unfavourable correlation between milk yield and fertility, oestrous detection rates have declined to less than 50%. Although visual detection of oestrus is accurate, it can be time consuming and inefficient. In response to these constraints and poor oestrous detection rates automated methods of detection are currently employed although they are lacking in accuracy and efficiency. The current work investigated possible risk factors among the herd for decreased oestrous expression, measured by activity monitors (Lely-HR Tags), with emphasis on individual cow factors affecting the activity increase at oestrus (n=205 cows). A novel approach was also tested, Ultra-wide band (UWB) technology (Thales Research Technology, UK) for proof of concept that oestrus, mounting and standing to be mounted, could be detected in dairy cows (initial validation studies plus 2 week long trials, n=16 cows; 8 in each). Several parameters were investigated for their association with maximum activity increase at oestrus using generalised linear mixed models. Activity increases at oestrus between 2 and 4 fold. Various influential factors that affect the activity increase were reported in this study: parity, successive oestrous number post partum and milk yield are inversely related to the activity increase at oestrus and activity increases were affected by time of year for each oestrus event (P<0.05). In addition, larger activity increases at oestrus were not related to an increased probability of conception. The three dimensional position of 12 cows, with their oestrous cycles synchronized, and 4 pregnant control cows were monitored continuously, using UWB mobile units (MU) operating within a base unit (BU) network for a period of 7 days. Cow position was reported twice per second in real-time with this system. In the complete study 10 cows came into oestrus as confirmed by simultaneous visual observation & CCTV recording, activity monitoring (Lely-HR Tags) and by analysis of milk progesterone concentration. Raw data taken from the UWB system were then analysed post trial to determine whether oestrus could be detected; including elevations in cow height and cow interactions. Furthermore, automated software was developed and script analysis (MatLab R2012b, The MathWorks, Inc., US) was carried out to detect cows in oestrus, reporting the time of oestrus onset in real-time. UWB accurately confirmed oestrus in 9 out of 10 cows in oestrus as confirmed by real-time video recording and continuous visual observation of activity. Although due to the constraints of the script 1 cow could not be detected in oestrus by UWB as she was the only cow in oestrus at the time equipped with a MU. Further confirmation of oestrus was carried out by physiological measurements; increases in activity on the day of oestrus and low progesterone concentrations <1ng/ml. In addition, UWB accurately confirmed 6 out of 6 cows as not being in oestrus. In conclusion UWB accurately detected cows in oestrus. Furthermore, automated detection by UWB enables the identification of the onset of oestrus, mounting, and when cows are in oestrus and first stood to be mounted, in real-time. Therefore UWB is advantageous because knowledge of onset of oestrus allows for accurately timed artificial insemination (AI) coinciding with ovulation, in order to increase conception rates. In summary, variables that affect expression of oestrus have been identified by this work. This would allow for identification of cows prone to decreased oestrous expression. In addition UWB accurately detected oestrus when cows displayed mounting and standing to be mounted behaviour. This work has shown ‘proof of concept’ that with further development UWB could be used as a novel automated method of oestrous detection. Therefore the current work has provided knowledge on factors that influence oestrous expression and possible solutions to the permanent improvement of detection. The work also provides evidence of a novel technology that can be developed in order to increase oestrous detection rates.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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