Macrae, Alastair

Barraclough, Rosie

Haskell, Marie

2021-09-01T15:25:07Z

2021-09-01T15:25:07Z

2021-07-31

The UK trend of increasing dairy herd size and milk yield per cow has generated challenges for dairy farmers, namely in the realm of cow health and herd management. Technology has the potential to facilitate livestock production, however the uptake of cow monitoring technologies within the UK has not been widely researched. There are key periods within the life of a cow where high levels of cow monitoring are required, for example calving, and technology has the potential to aid farmers with cow management. Calving cows require regular observation as it is a period of high risk for cow and calf; two common issues at calving are calving difficulty (dystocia) and calf mortality. However as average herd size increases, farm staff are under pressure to manage their time effectively and calving presents a management challenge. In the period surrounding calving, cows are susceptible to a range of disorders such as hypocalcaemia – a metabolic disorder which can be fatal. Automated systems could be used to detect calving and clinical hypocalcaemia on commercial dairy farms to help facilitate herd management and improve cow health and welfare. The first study was a survey investigating the prevalence and use of automated cow technologies was completed by 122 UK dairy farmers. The results showed that approximately 3 in 5 dairy farmers utilised automated cow monitoring technology, and the main parameters that were monitored on UK dairy farms were heat detection, daily milk yield, and illness detection. Half of dairy producers that do not have automated cow monitoring technology installed will invest within the next 5 years, and it is therefore expected that the prevalence of automated cow technologies will increase. Results indicated that dairy producers were satisfied with automated cow monitoring technology on their farms. The main barrier to adoption of technology was initial investment cost. The second study investigated the behavioural changes of eutocic and dystocic dairy cows in late gestation and on the day of calving. An accelerometer was attached to the hind leg of dairy cattle to collect lying and activity behavioural data. Data were collected from 32 multiparous and 12 primiparous Holstein dairy cattle to describe normal calving behavior and parity differences. To quantify behaviour related to calving difficulty, the data from 14 animals that had dystocia at calving were matched to cows that had an eutocic calving based on parity, locomotion score, calf breed, calf sex, month, and year of calving. Retrospective analysis was conducted on lying and activity data in the period before calving (d -4 to d -1) and on the day of calving (d 0). Findings suggest that cow behaviour on the day of calving was significantly different when compared to a non-calving control period (d -4). Important differences were found in the behaviour of primiparous and multiparous cows during the period prior to calving. In addition, the days relative to calving were found to affect activity behaviours. Three different types of machine learning methods (random forest, decision tree, and neural network) were unable to successfully use behavioural changes to classify the day before calving or the 2h period before calving. There was no difference in the behaviour between 14 cows with assisted calvings (dystocic) and 14 cows with non-assisted calvings (eutocic). The third study was designed to describe and quantify any behavioural differences between cows diagnosed with normocalcaemia, subclinical hypocalcaemia, and clinical hypocalcaemia at calving. A total of 51 multiparous cows and 21 primiparous cows were categorised as having either clinical hypocalcaemia, subclinical hypocalcaemia, or normocalcaemia at calving. Lying and activity behaviours of multiparous and primiparous cows within each blood calcium category was assessed for differences. In the 14 d before calving, multiparous cows with normocalcaemia had fewer lying and standing bouts compared to multiparous cows with subclinical hypocalcaemia and clinical hypocalcaemia. In addition, the step count of primiparous cows with normocalcaemia decreased across the period. These results suggested behaviour could be used to categorise cows into blood calcium group categories prior to calving. Cows that had clinical hypocalcaemia at calving were less active and lay down more in the 21 d post-calving. This finding suggests that the effect of hypocalcaemia on cow behaviour was long lasting. Overall, this thesis has shown that the use of remote sensing technology can be used to detect behavioural changes associated with calving and hypocalcaemia. These findings could be used to develop automated detection systems for calving and hypocalcaemia which could aid dairy producers in herd and cow health management. In addition, a survey of UK dairy farmers has shown that farmers are willing to invest in cow monitoring technology and 68% surveyed farmers would invest in the next 5 years. Return on investment was considered the most important criteria when selecting a technology for purchase. Therefore, it is important that technology companies can prove the monetary and non-monetary benefits of technologies.

https://hdl.handle.net/1842/37972

https://doi.org/10.7488/era/1243

en

The University of Edinburgh

advanced technology

dairy herd

milk yield per cow

dairy farmers

cow health

herd management

livestock production

cow monitoring technologies

Calving cows

automated cow technologies

Use of advanced technology to enhance monitoring of dairy cow health

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy