Effect of setting a maximum milking time, from peak lactation, on production, milking time and udder health

Jago

Journal of Dairy Science

2013

It was hypothesized that reducing cow cluster-on time by increasing automatic cluster remover (ACR) thresholds above 0.4 kg/min would require premilking stimulation of the mammary gland to maintain milk yield. This was tested by examining the interaction between 4 ACR thresholds and 3 premilking treatments over an 8-wk period with 96 mixed-age Friesian-Jersey cross cows being milked twice per day in late lactation (average production: 13.9 kg/d). The 3 premilking treatments were attach cluster immediately (control), attach cluster 60s after entering the dairy (delay), or remove 2 squirts of foremilk from each quarter and attach cluster 60s after entering the dairy (Prep). Four ACR thresholds were chosen, where the cluster was removed after the milk flow rate was less than 0.2 (ACR2), 0.4 (ACR4), 0.6 (ACR6), and 0.8 kg/min (ACR8). Measurements included individual cow milk yield, cluster-on time, average milk flow rate, maximum milk flow rate, time from cluster attachment to average milk flow rate, milk yield in the first 2 min, time from maximum milk flow rate to end of milking, and the milk flow rate at predetermined intervals during each milking session. Composite milk samples were collected weekly at a.m. and p.m. milkings to determine composition and somatic cell count (SCC). On 3 occasions during the experiment, postmilking strip yield was measured. No interactions were detected between premilking treatment and ACR threshold in any of the measured variables. Cows receiving the Prep treatment had a 5 to 9% shorter cluster-on time than the control treatment. Milk yield, SCC, postmilking strip yield, and maximum flow rate were not different between the 3 premilking treatments. Cluster-on time of the ACR8 cows was 21 to 29% less than ACR2, but SCC and milk production variables were not different between the 4 end-of-milking treatments despite higher strip yields as ACR threshold increased. Increasing ACR threshold offers the potential to reduce the duration of milking without detriment to overall productivity. The results of the premilking treatments indicate that to achieve the most efficient routine, the operator should attach clusters as close as possible to the first bail in rotary dairies to increase bail utilization in pasture-based systems. If cluster attachment can be sped up and ACR threshold lifted, significant potential exists to decrease herd milking duration and improve labor productivity.

Section: Discussionmentioning

confidence: 78%

Short-term application of prestimulation and increased automatic cluster remover threshold affect milking characteristics of grazing dairy cows in late lactation

Jago

Journal of Dairy Science

2013

“…Milk yield (Figure 4) is the logical choice, as it explains a large amount of variation in milking duration, can be easily calculated from milk-collection receipts, and consequently is the method advocated by researchers in New Zealand and Australia (CowTime, 2003;. However, Jago et al (2010b) reported that current guidelines may result in more than 20% of milkings being truncated. Figure 4 can be used to select an appropriate maximum milking time for a given yield and highlights how significant improvements to average cow milking duration can be achieved by truncating the slowest cows.…”

Section: Discussionmentioning

confidence: 99%

Analysis of milking characteristics in New Zealand dairy cows

Jago

Journal of Dairy Science

2014

The objective of this study was to describe the variation in milking characteristics, and factors associated with these traits, in grazing dairy cows milked without premilking stimulation. Milk yield, duration, and average and maximum milk flow rate data were collected from 38 farms in New Zealand at 2 time points (spring and autumn) during the 2010 to 2011 season. Subsequently, a second data set, allowing the generation of daily milk flow profiles, was collected from 2 farms in the 2011 to 2012 season. Corresponding animal data, such as breed, date of birth, and ancestry information, were extracted from the New Zealand Dairy Industry Good Animal Database (New Zealand Animal Evaluation Ltd., Hamilton, New Zealand). Residual milking duration (deviation from the regression line of milk yield on milking duration) was calculated, allowing the identification of fast-milking cows independent of milk yield. Variance components for the milking characteristics traits were estimated using an animal linear mixed model. The average milk yield was 10 kg/milking and the average milking duration was 360 s. The average milk flow rate was 1.8 kg/min and maximum milk flow 3.3 kg/min, with 44% of milk flow curves being classified as bimodal. Primiparous animals exhibited different milk flow profiles, with a lower maximum flow, than multiparous animals, possibly due to differences in cisternal capacity. Residual milking duration was shortest (-10s) in mid-lactation (121-180 d) and was 13s longer for Jersey compared with Friesian cows; however, it was 19s shorter when adjusted for energy content. Residual milking duration had a negligible genetic correlation (-0.07) with milk yield, indicating that selection for cows with shorter residual milking duration should have a negligible effect on milk yield. A heritability of 0.27 indicated that residual milking duration could be valuable as part of a breeding program. Knowledge of the distribution of milking durations for a given milk yield allows farmers to choose appropriate cluster-on time when using a maximum milking time strategy to improve milking efficiency.

“…The presence of residual milk is considered by many to be linked with increased mastitis. However, studies done in Australasia examining the use of a maximum milking time (Clarke et al 2004; Jago et al 2010a, b) have reported no effect of setting a maximum milking-time on SCC, the incidence of clinical mastitis, number of infected quarters or milk production, even when truncating 30% of cows (Jago et al 2010b). Similar results have been reported by other studies examining the use of increased ACR thresholds to shorten cow milking duration (Edwards et al 2013b, d).…”

Section: Discussionmentioning

confidence: 99%

“…The performance of milking parlours can be influenced by the milking duration of individual cows (Jago et al 2010b; Edwards et al 2012), where shorter cow milking durations can allow greater throughput. In herringbone parlours some operators choose to wait for slow-milking cows to milk out, hence creating longer row times and reducing throughput.…”

mentioning

confidence: 99%

“…For example, in a survey of 280 operators of herringbone parlours in New Zealand 56% answered that they always or sometimes wait for cows to milk out (Cuthbert, 2008). Research has indicated that an effective strategy to reduce cow milking duration is to apply a maximum milking time, thereby truncating the milking of cows with milking durations longer than this selected time (Clarke et al 2004; Jago et al 2010b). Furthermore, minimal effects on overall production or indicators of udder health were reported in studies where a maximum milking time was applied (Clarke et al 2008; Jago et al 2010a).…”

mentioning

confidence: 99%

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Milking efficiency of swingover herringbone parlours in pasture-based dairy systems

O’Brien

Journal of Dairy Research

et al. 2013

The objective of this study was to collect and analyse milking data from a sample of commercial farms with swingover herringbone parlours to evaluate milking efficiency over a range of parlour sizes (12-32 milking units). Data were collected from 19 farms around the Republic of Ireland equipped with electronic milk metres and herd management software that recorded data at individual milking sessions. The herd management software on each farm was programmed to record similar data for each milking plant type. Variables recorded included cow identification, milking date, identification time, cluster-attachment time, cluster/unit number, milk yield, milking duration, and average milk flow rate. Calculations were performed to identify efficiency benchmarks such as cow throughput (cows milked per h), milk harvesting efficiency (kg of milk harvested per h) and operator efficiency (cows milked per operator per h). Additionally, the work routine was investigated and used to explain differences in the benchmark values. Data were analysed using a linear mixed model that included the fixed effects of season-session (e.g. spring-AM), parlour size and their interaction, and the random effect of farm. Additionally, a mathematical model was developed to illustrate the potential efficiency gains that could be achieved by implementing a maximum milking time (i.e. removing the clusters at a pre-set time regardless of whether the cow had finished milking or not). Cow throughput and milk harvesting efficiency increased with increasing parlour size (12 to 32 units), with throughput ranging from 42 to 129 cows/h and milk harvesting efficiency from 497 to 1430 kg/h (1-2 operators). Greater throughput in larger parlours was associated with a decrease in operator idle time. Operator efficiency was variable across farms and probably dependent on milking routines in use. Both of these require consideration when sizing parlours so high levels of operator efficiency as well as cow throughput can be achieved simultaneously. The mathematical model indicated that application of a maximum milking time within the milking process could improve cow throughput (66% increase in an 18-unit parlour when truncating the milking time of 20% of cows). This could allow current herd milking durations to be maintained as herd size increases.