Adrenocortical activity and time budgets in a robotic milking system in two variants of cow traffic (partially forced, free) were compared with a conventional milking system, focusing on the relationships between adrenocortical activity, time budgets, and social rank of the cows. Both groups were housed in identical conditions and consisted of 30 cows each. Within each experiment, direct observations of social behaviour and 24-hour video recordings were conducted during six two-day blocks. We calculated the dominance value and the time budget (‘lying’, ‘feeding’, ‘other activity’) of each cow. Faeces from each cow were collected to determine the concentration of cortisol metabolites (CCM) as an indicator of baseline adrenocortical activity and possible chronic stress. Analysis of CCM and time budgets did not indicate any adverse effects of the robotic milking system, with one exception: under partially-forced cow traffic, cows of low social rank had longer waiting times in front of the robot. Considering the fact that the number of cows milked by the robot was less than half of that recommended by the manufacturers, our results do not exclude the possibility that a higher stocking rate may lead to further adverse effects.

Automatic milking systems (AMS), or milking robots, are becoming widely accepted as a milking technology that reduces labour and increases milk yield. However, reported amount of labour saved, changes in milk yield, and milk quality when transitioning to AMS vary widely. The purpose of this study was to document the impact of adopting AMS on farms with regards to reported changes in milking labour management, milk production, milk quality, and participation in dairy herd improvement (DHI) programmes. A survey was conducted across Canada over the phone, online, and in-person. In total, 530 AMS farms were contacted between May 2014 and the end of June 2015. A total of 217 AMS producers participated in the General Survey (Part 1), resulting in a 41% response rate, and 69 of the respondents completed the more detailed follow-up questions (Part 2). On average, after adopting AMS, the number of employees (full- and part-time non-family labour combined) decreased from 2.5 to 2.0, whereas time devoted to milking-related activities decreased by 62% (from 5.2 to 2.0 h/day). Median milking frequency was 3.0 milkings/day and robots were occupied on average 77% of the day. Producers went to fetch cows a median of 2 times/day, with a median of 3 fetch cows or 4% of the herd per robot/day. Farms had a median of 2.5 failed or incomplete milkings/robot per day. Producers reported an increase in milk yield, but little effect on milk quality. Mean milk yield on AMS farms was 32.6 kg/cow day. Median bulk tank somatic cell count was 180 000 cells/ml. Median milk fat on AMS farms was 4.0% and median milk protein was 3.3%. At the time of the survey, 67% of producers were current participants of a DHI programme. Half of the producers who were not DHI participants had stopped participation after adopting AMS. Overall, this study characterized impacts of adopting AMS and may be a useful guide for making this transition.

This Research Communication explores the usefulness of predictive modelling to explain bacterial behaviour during cooling. A simple dynamic lag phase model was developed and validated. The model takes into account the effect of the cooling profile on the lag phase and growth in bulk tank milk. The time before the start of cooling was the most critical and should not exceed 1 h. The cooling rate between 30 and approximately 10 °C was the second most critical period. Cooling from 30 to 10 °C within 2 h ensured minimal growth of psychrotrophic bacteria in the milk. The cooling rate between 10 and 4 °C (the slowest phase of cooling) was of surprisingly little importance. Given a normal cooling profile to 10 °C, several hours of prolonged cooling time made practically no difference in psychrotrophic counts. This behaviour can be explained by the time/temperature dependence of the work needed by the bacteria to complete the lag phase at low temperature. For milk quality advisors, it is important to know that slow cooling below 10 °C does not result in high total counts of bacteria. In practice, slow cooling is occasionally found at farms with robotic milking. However, when comparing psychrotrophic growth in bulk milk tanks designed for robotic milking or conventional milking, the model predicted less growth for robotic milking for identical cooling profiles. It is proposed that due to the different rates of milk entering the tank, fewer bacteria will exit the lag phase during robotic milking and they will be more diluted than in conventional milking systems. At present, there is no international standard that specifies the cooling profile in robotic systems. The information on the insignificant effect of the cooling rate below 10 °C may be useful in the development of a standard.

Dairy cows grazing pasture and milked using automated milking systems (AMS) have lower milking frequencies than indoor fed cows milked using AMS. Therefore, milk recording intervals used for herd testing indoor fed cows may not be suitable for cows on pasture based farms. We hypothesised that accurate standardised 24 h estimates could be determined for AMS herds with milk recording intervals of less than the Gold Standard (48 hs), but that the optimum milk recording interval would depend on the herd average for milking frequency. The Gold Standard protocol was applied on five commercial dairy farms with AMS, between December 2011 and February 2013. From 12 milk recording test periods, involving 2211 cow-test days and 8049 cow milkings, standardised 24 h estimates for milk volume and milk composition were calculated for the Gold Standard protocol and compared with those collected during nine alternative sampling scenarios, including six shorter sampling periods and three in which a fixed number of milk samples per cow were collected. Results infer a 48 h milk recording protocol is unnecessarily long for collecting accurate estimates during milk recording on pasture based AMS farms. Collection of two milk samples only per cow was optimal in terms of high concordance correlation coefficients for milk volume and components and a low proportion of missed cow-test days. Further research is required to determine the effects of diurnal variations in milk composition on standardised 24 h estimates for milk volume and components, before a protocol based on a fixed number of samples could be considered. Based on the results of this study New Zealand have adopted a split protocol for herd testing based on the average milking frequency for the herd (NZ Herd Test Standard 8100:2015).