Automatic milking systems (AMS) are becoming increasingly popular due to the growing cost of labour and reduced labour availability. The voluntary cow traffic and resultant distribution of milkings throughout the day and night affects most aspects of herd and farm management in AMS. The literature review (Chapter 1) highlighted a need to evaluate the effects of milk yield and milking frequency during early lactation on reproductive performance. The analysis of a 5-year historic database from Australia’s first AMS research farm (Chapter 2) found no significant association of average milk yield and milking frequency during 100 days in milk with any of the reproductive measures. However, the interval from calving to first oestrus increased gradually within the study period and consequently influenced other reproductive outcomes. As a result, a series of studies were conducted with a multidisciplinary approach (both physiological and technological) to investigate the potential to improve oestrus detection on pasture-based AMS farms. A field study (Chapter 3) was conducted to allow for the development and application of an algorithm to assess the application accuracy of an infrared thermography (IRT) device when used to detect oestrus events or pending oestrus events by detecting the time of ovulation. Vulval and muzzle temperatures were measured by IRT in twenty synchronized cows (using a controlled internal drug release and prostaglandin F2α). Whilst the IRT showed some potential as an oestrus detection aid with higher sensitivity than visual observation (67%) and Estrotect activation (67%), the specificity and positive predictive value were lower with the IRT. The vulva and muzzle were the focus areas for the IRT application and some concern was generated with regard to the potential for the IRT data to impacted by faecal contamination, obscuring of the vulva by the tail and time since last drinking (affecting muzzle surface temperature). To address these concerns a further study (Chapter 5) was conducted to test the hypothesis that the specificity of IRT in detecting oestrus (or imminent oestrus) could be improved if other body parts were focused on. In that study (Chapter 5), an additional technology was incorporated to test the hypothesis that the combined activity and rumination data generated by an accelerometer (SCR heat and rumination long distance tags) would provide a more accurate indication of oestrus and/or ovulation than the activity and rumination data alone. Unfortunately the monitoring of eyes and/or ears did not provide the improvement in accuracy of IRT (as an oestrus detection aid) indicating that as an oestrus detection aid there was likely to be limited value in developing this as an automated stand-alone device. Alerts generated by accelerometer based on a lower activity threshold level had high sensitivity and may be able to detect a high proportion of cows in ovulatory periods in pasture-based system; however, the specificities and positive predictive value were lower than the visual assessment of mounting indicators and would still require the herd’s person to filter data to identify the false alerts to ensure that cows are not inseminated unnecessarily. Whilst the use of in-line milk monitoring has already been commercialized for the assessment of milk progesterone, there is potential for other biomarkers to provide further opportunities for the assessment of milk components. Biomarkers of oxidative stress were evaluated in plasma showing that plasma glutathione was lower in ovulated cows compared to those of an-ovulated cows (Chapter 4). Whilst baseline plasma data for oxidative stress biomarkers was a useful starting point, the real value of these biomarkers would be realised if their concentration in milk could be linked with oestrus (and or ovulation). Milk superoxide dismutase activity was shown to be higher in ovulated cows while lipoperoxides, glutathione peroxidase were lower in ovulated cows compared to those in an-ovulated cows (Chapter 6). Further work would be required to determine the accuracy with which these biomarkers could be used to identify oestrus cows but these results are promising and suggest that there may be some potential to develop in-line milk sampling technology to alert the herdsperson to cows that should be inseminated. udIn summary, this thesis provides very useful, scientifically based information on potential use of technologies for oestrus and ovulation detection in dairy cows, which should serve as a foundation to develop and upgrade automated on-farm technologies and biosensors for better reproductive management of cows in pasture-based AMS. However, it is noted that the most likely success with automated oestrus detection is to require a combination of different indicators that should be incorporated to truly increase the accuracy of detection beyond that which can be achieved by skilled and devoted herd’s people.
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