N.A. Lyons

Comparison of 2 systems of pasture allocation on milking intervals and total daily milk yield of dairy cows in a pasture-based automatic milking system

Cows milked in pasture-based automatic milking systems (AMS) have greater milking intervals than cows milked in indoor AMS. Long milking intervals greater than 16h have a negative effect on milk yield and udder health. The impact of 2 systems of pasture allocation in AMS on milking interval and yield was investigated at the FutureDairy AMS research farm (Elizabeth Macarthur Agricultural Institute, New South Wales Department of Primary Industries, Camden, New South Wales, Australia) in late November to early December 2010. Two- (2WG) versus 3-way grazing (3WG) allocations per 24-h period were compared in a field study to test the hypothesis that an increase in the frequency of pasture allocation would reduce the milking interval and, therefore, increase milking frequency. The study involved the entire milking herd of 145 cows, with (mean ± SD) DIM=121±90d, 7-d average milking frequency=1.52±0.41 milkings/cow per day, and 7-d average milk yield=21.3±7.6kg/cow per day. Cows were milked using 2 DeLaval VMS milking units (DeLaval International AB, Tumba, Sweden). Cows in the 3WG treatment had 31% reduced milking interval, 40% greater milking frequency, and 20% greater daily milk production compared with 2WG. Increased milking frequency and milk production for 3WG was associated with greater utililization levels of the AMS milking units throughout the day. These results support the recommendation that, wherever possible, farmers installing AMS should incorporate sufficient infrastructure to accommodate 3WG, which provides additional flexibility with managing extremely long (and short) milking intervals.

Rumination and activity levels as predictors of calving for dairy cows.

The Australian dairy herd size has doubled over the last 20 years substantially increasing the time that farmers require for individual animal attention to monitor and intervene with events such as calving. Technology will help focus this limited labour resource on individual cows that require assistance. The objective of this experiment was to first determine the profiles of rumination duration and level of activity as determined by sensors between, and within, days around calving and second to use these data to predict the day of calving for pasture-based dairy cows. After 2 weeks from the expected calving date, 27 cows were fitted with SCR HR LD Tags, located in 40×90 m2 paddock and offered ad libitum oaten hay and 2 kg grain-based concentrate/cow per day until calving. Hourly activity and rumination data for each cow, as determined by the SCR tags, were fitted with linear mixed models and all parameters were estimated using restricted maximum likelihood. Rumination duration decreased by 33% over the day prior and the day of calving, with the decline in rumination duration starting the day prepartum. Activity levels were maintained prepartum but increased in the days postpartum. The day of calving was recorded and used to determine the gold standard positive (the day before calving) and negative (all other) dates. A threshold rumination level of 0.9 (decline in rumination duration of 10%) gave the optimal combination of 70% sensitivity and 70% specificity. This experiment shows the potential to use rumination duration to predict the day of calving and the opportunity to use sensor data to monitor animal health.

Effect of pre- versus postmilking supplementation on traffic and performance of cows milked in a pasture-based automatic milking system.

Cows milked in a pasture-based automatic milking system tend to have a lower daily milking frequency in comparison with cows milked in indoor systems. Milking events with intervals beyond 16h have been reported to have a negative effect on milk yield and udder health, and therefore it is important to minimize their occurrence. As feed is the main incentive to encourage cow traffic around the system, a study was conducted to compare pre- (PRE) versus postmilking (POST) supplementary feed placement strategies in a pasture-based automatic milking system. We hypothesized that PRE cows would have a stronger incentive to walk voluntarily from the paddock to the dairy facility to get milked (due to the reward being more immediate), thereby reducing their milking interval and increasing daily milking frequency and milk yield. The PRE cows returned to the dairy facility sooner (PRE=11.9 vs. POST=13.27h) but had longer milking intervals (PRE=15.3 vs. POST=14.28h). This was due to the additional time spent in the prefeeding area (PRE=56 versus POST=23min) combined with a longer average time spent in the premilking waiting yard (PRE=97 versus POST=77min). Treatment did not affect daily milk yield per cow. The result of this study demonstrates the potential of manipulating feeding management strategies to influence cow behavior and traffic in voluntary milking systems.

Animal behavior and pasture depletion in a pasture-based automatic milking system

In pasture-based automatic milking systems (AMS), feed is the main incentive that can be managed to encourage reliable and consistent voluntary and distributed cow traffic. Modifying timing, placement and size of feed allocations is expected to have an effect on cow behavior that could avoid the occurrence of extended milking intervals, which have a negative effect on milk yield. Therefore, behavioral studies provide information on how cows modify their actions under different management regimes and can help explain the impact of those regimes. Behavioral observations were conducted in spring 2011 at the FutureDairy AMS research farm, as part of a study where a herd of 175 cows was split into two groups that received supplementary feed either before (PRE), or immediately after (POST) milking. In addition, all cows were offered access to two daily pasture allocations. Observations were conducted in the pasture allocation on 15 focal cows from each treatment group during four periods of 24 h to detect presence and behavior (grazing, ruminating, idling and other) every 15 min. In addition, bite rate and pasture biomass were measured every hour. Overall, despite the finding that more POST cows than PRE cows entered the pasture allocation during the first 8 h of active access, there was no difference in the total proportion of cows that had gained access by the end of the active access period (average 68% for both treatments). Cows in the PRE treatment started exiting the pasture allocation just 6 h after entering, compared with 8 h for POST cows, although their behaviors in the pasture allocation did not differ. Behaviors and bite rate were more dependent on pasture biomass than on supplementary feeding management.

Early detection of clinical mastitis from electrical conductivity data in an automatic milking system

Mastitis adversely affects profit and animal welfare in the Australian dairy industry. Electrical conductivity (EC) is increasingly used to detect mastitis, but with variable results. The aim of the present study was to develop and evaluate a range of indexes and algorithms created from quarter-level EC data for the early detection of clinical mastitis at four different time windows (7 days, 14 days, 21 days, 27 days). Historical longitudinal data collected (4-week period) for 33 infected and 139 healthy quarters was used to compare the sensitivity (Se; target >80%), specificity (Sp; target >99%), accuracy (target >90%) and timing of ‘alert’ by three different approaches. These approaches involved the use of EC thresholds (range 7.5– 10 mS/cm), testing of over 250 indexes (created ad hoc), and a statistical process-control method. The indexes were developed by combining factors (and levels within each factor), such as conditional rolling average increase, percentage of variation, mean absolute deviation, mean error %; infected to non-infected ratio, all relative to the rolling average (3–9 data points) of either the affected quarter or the average of the four quarters. Using EC thresholds resulted in Se, Sp and accuracy ranging between 47% and 92%, 39% and 92% and 51% and 82% respectively (threshold 7.5 mS/cm performed best). The six highest performing indexes achieved Se, Sp and accuracy ranging between 68% and 84%, 60% and 85% and 56% and 81% respectively. The statistical process-control approach did not generate accurate predictions for early detection of clinical mastitis on the basis of EC data. Improved Sp was achieved when the time window before treatment was reduced regardless of the test approach. We concluded that EC alone cannot provide the accuracy required to detect infected quarters. Incorporating other information (e.g. milk yield, milk flow, number of incomplete milking) may increase accuracy of detection and ability to determine early onset of mastitis.

Current and potential system performance on commercial automatic milking farms

Dairy farmers considering installing automatic milking systems (AMS) would benefit from adequate contextual information on commercial AMS farm performance. The aim of the present study was to capture key performance indicators related to AMS utilisation on commercial Australian AMS farms on a monthly basis, with the aim of understanding the current and potential system performance. Eight Australian AMS farms were monitored on a monthly basis for a 12-month period. The average number of milking events (milkings/robot.h) was calculated for every hour of the day, on a monthly basis for each of the participating farms. Data exported electronically also allowed the calculation of the number of current and potential extra (both average and maximum) milkings (milkings/robot.day), cows (cows/robot), yield (kg milk/robot.day) and milking time (h/robot.day) for every month on each farm. Despite a wide range in farm performance, the actual milkings (120 milkings/robot.day), cows (51 cows/robot), yield (1263 kg milk/robot.day) and milking time (13.63 h/robot.day) indicated that there is an opportunity to improve these parameters by a maximum of ~60%. To achieve this would require the adoption of a variety of strategies that might be quite farm specific and would be reliant on optimisation of data relating to cow traffic and system utilisation that are relevant to automatic milking systems.

Milking permission and milking intervals in a pasture-based automatic milking system

In a pasture-based, automatic milking system, a proportion of milking events occur with milking intervals (MI) >16 h (extended MI). Additionally, cows necessarily walk longer distances than in indoor-based systems. The decision to milk a cow is based on milking permission criteria, which are generally based on time since last milking but can often take into account expected yield as well. Any cow arriving at the dairy and that does not receive milking permission is drafted to a pasture allocation, but it is not known whether milking refusal influences total time of return and therefore MI. Data from a 33-day period from the FutureDairy pasture-based, automatic milking system research farm using a prototype robotic rotary were analysed to investigate the hypothesis that a greater proportion of milking events occurring with extended MI would correspond to cows that had experienced a previous milking refusal. If this were the case then management practices could be implemented to deal with cows that visit the dairy soon after the last milking event. Results indicate that one-third of milking events had extended MI, although only 16% of them had a previous milking refusal. The average refusal took place 3 h after the last milking event and caused extended MI in >60% of the cases. This indicated that although attention should be placed on cows that returned to the dairy before milking permission (because they were likely to have an extended MI), milking refusals were not the main cause of extended MI. Therefore, cows that visit the dairy facility earlier than expected could be sorted to a feeding area close to the dairy, yet the greatest impact on overall MI will probably be achieved by reducing time spent in any one pasture allocation.