@conference {89257, title = {Cows becoming clinically lame differ in changes in behaviour and physiology compared to cows that do not become clinically lame}, booktitle = {Proceedings of the New Zealand Society of Animal Production}, volume = {73}, year = {2013}, month = {Jan}, pages = {5-10}, publisher = {New Zealand Society of Animal Production}, organization = {New Zealand Society of Animal Production}, address = {Hamilton}, abstract = {

The hypothesis that sensors can detect changes in behaviours and physiologiesassociated with cows becoming clinically lame was tested by comparing trends of sensor data from lame cows with non-lame cows. Sensor data included data from live weighing scales, pedometers (average steps per hour) and milk meters collected between November 2010 and June 2012 on five Waikato dairy farms. Farmers were trained in detecting and diagnosing lame cows. For each lameness event (n = 318 events affecting 292 cows) a time period of 14 days prior to the day ofdetection was randomly matched by farm and date to 10 non-lame cows. In this period, lame cows decreased in weight, steps taken per hour, milk yield in the first two minutes of milking, total milk yield, and milking duration. Lame cows also entered the milking platform later. In comparison, non-lame cows had no change in sensor data trends. These differences (P \<0.05) in sensor data trends imply potential value of sensor data in detecting lameness automatically. Large variations in sensor data values between and within lame and non-lame cows indicated that future research should focus on combinations of variables that show the best potential to detect lameness automatically.

}, issn = {1176-5283}, author = {Kamphuis C and Burke JK and Jago JG} } @conference {89261, title = {Verification of an automated camera-based system of oestrus detection in dairy cows}, booktitle = {Proceedings of the New Zealand Society of Animal Production}, volume = {73}, year = {2013}, month = {Jan}, pages = {26-28}, publisher = {New Zealand Society of Animal Production}, organization = {New Zealand Society of Animal Production}, address = {Hamilton}, abstract = {

Accurate oestrus detection is critical in New Zealand dairy farming, where many farms operate a seasonal farming model and, therefore, rely on a compact calving spread and a short mating period to farm efficiently. Automated oestrus detection systems are available that automatically identify and draft cows in oestrus. There are two types of automatic systems available commercially in New Zealand; activity-based systems that involve pedometers/accelerometers and systems that detect mounting behaviour using a sensor attached to the animal{\textquoteright}s back.

}, issn = {1176-5283}, author = {Hempstalk K and Burke CR and Kamphuis C} } @conference {78982, title = {An evaluation of a progesterone-based diagnostic as an aid to re-insemination decisions in a seasonal, pasture-grazed dairy cow herd}, booktitle = {Proceedings of the New Zealand Society of Animal Production}, volume = {72}, year = {2012}, month = {Jan}, pages = {35-37}, publisher = {New Zealand Society of Animal Production}, organization = {New Zealand Society of Animal Production}, address = {Christchurch}, abstract = {

Progesterone-based diagnostic tests are commercially available to predict pregnancy status 19 to 23 days following an insemination, thus providing decision-making support for artificial breeding. The study objective was to evaluate the likely performance of such a test in a pasture-grazed, seasonal dairy farm. Progesterone was determined in milk samples collected twice weekly from 553 cows at the Lincoln University Dairy Farm. Receiver operating characteristic analysis was applied to these data to determine sensitivity, false positive rate and the positive predictive value using a range of progesterone threshold values for detecting non-pregnant cows. The gold standard was non-pregnancy as measured by ultrasonography. At an optimised threshold of 4.1 ng/mL, the sensitivity, false positive rate and positive prediction rate were 78\%, 2.6\% and 96\% for detecting non-pregnant cows on Days 19 to 23. A major source of inaccuracy was the 28 cows with elevated levels of progesterone in milk on Days 19 to 23 after first insemination that were subsequently diagnosed non-pregnant. Another source of error was the inherent variance in progesterone concentration profiles among individuals. At a current cost of about $5 per test, this technology would have limited appeal for detecting non-pregnant cows in dairy systems where a satisfactory level of oestrus detection performance can be achieved.

}, issn = {1176-5283}, author = {Burke CR and Kamphuis C and Meier S and Pellow R} }