The main objective of this research communication was to explore the extent of milkability changes caused by the incidence of clinical mastitis. Our second objective was to investigate if the milkability of cows shortly before mastitis incidence significantly differed in comparison with healthy cows. Milk yield, milking time, average milk flow, partial milk flows and the occurrence of bimodal milk flows were monitored during the first 120 d in milk for all cows that calved on the farm during the experimental period (n = 127). A veterinarian diagnosed 27 cows with clinical mastitis, while the remaining healthy cows served as a control group. The period surrounding the mastitis was monitored in a timeline from 2 weeks before to 4 weeks after the diagnosis. We did not observe any significant differences in milkability between the healthy cows and the cows in the pre-mastitis period, which suggests that monitoring these might not be useful for early mastitis detection. Milk yield and milk flow were significantly decreased in the week of mastitis diagnosis compared to the pre-mastitis period. Milking time was not affected in the week of diagnosis, but was significantly increased in the following week, because milk yield returned to pre-mastitis values, but average milk flow was still significantly decreased. Milk yield was normalized to pre-mastitis values as the first of monitored parameters. Milk flow returned to pre-mastitis values in the fourth week after mastitis diagnosis. Milking time was the only parameter that was significantly affected up to a month after diagnosis. Our results showed that milkability changes caused by mastitis are significant and should not be ignored. The ability to adjust milking settings for cows diagnosed with mastitis could become a useful tool for improving mastitis treatment.

Small ruminants not only differ on mammary gland anatomy, milk’s properties and the amount of milk yielded comparable to those of dairy cattle, but also on the milking routine strategies and machine milking settings to maximize daily milk secretion. The udder compartment is proportionally larger in dairy sheep and goats, which requires modifications in the milking machine settings, milking procedures and allows the use of different milking strategies as they better tolerate extension of milking intervals. Depending on the breed, cisternal milk in goats varies from 70% to 90%, whereas in dairy sheep it varies from 50% to 78% of the total gland capacity. This explains why these species are commonly milked without pre-milking teat preparation, while in goats it is applied only in cases of high prevalence of intramammary infection in the herd. Recent French researchers observed that 40% of the goats presented an unbalanced udder as well as unbalanced morphology (21% to 30%) and functional milk flow (around 10% to 20% more) which could induce overmilking. In dairy sheep, selection for higher milk production increases teat angle insertion. Thus, to increase machine milk fraction, it is recommended to use either the ‘Sagi hook’ as an alternative for lifting up the ‘pendulous’ udder during milking or to perform machine stripping. There are three cluster removal strategies for small ruminants: manual, timed and milk flow driven automatic cluster removal (ACR). Automatic cluster removal reduces overmilking, improves teat condition, enables labour saving and provides a consistent milking routine in small ruminants. There are three to five main milk flow profiles in ewes and goats, which result in curves with one or two peaks (or plateau) and different patterns of the milk flow decreasing phase due to the degree of mammary gland imbalance and teat characteristics. When taking into account our current knowledge, ACR recommended take-off settings for goats are: 200 g/min+10 s delay time (DT) for a long decreasing phase or two plateau curves and 500 g/min+5 s DT for a short decreasing phase and one plateau curve. The ACR take-off settings for ewes are: 150 g/min +10 s DT for long decreasing phase and 200 g /min+5 s DT for a short decreasing phase. This review is intended to be useful for scientists and producers seeking basic knowledge of milking routines and cluster detachment settings for parlour performance and milk quality.

Cattle’s relationship with humans is a crucial factor regarding their welfare. In dairy cows, interactions with humans occur regularly during milking. We tested the effect of gentle interactions (stroking, talking in a gentle voice) during milking on avoidance distance and milk composition, yield and flow characteristics as well as behaviour during milking. Over the course of 15 days, an experimenter interacted gently with 14 German Holstein cows for 2 min during morning and evening milkings, totalling 60 min; the experimenter stayed at a similar distance to 12 control cows of the same breed for the same amount of time. There were no significant differences between the groups in behaviour during milking. Over the course of the experimental phase, avoidance distance at the feeding rack decreased significantly in stroked but not in control cows. The treatment did not improve any of the measures of milk composition, yield or flow; on the 1st day of the treatment, milk ejection was impaired in stroked cows, which points towards an effect of the novelty of the treatment. We conclude that gentle interactions during milking improve the relationship between cows and a human. Possible reasons for the absence of an effect on milk characteristics are that cows may not have perceived the interactions as positive or that a ceiling effect occurred due to otherwise optimal milking routines.

The aim of this paper was to study the relationship between milk flow emission variables recorded during milking of dairy goats with variables related to milking routine, goat physiology, milking parameters and milking machine characteristics, to determine the variables affecting milking performance and help the goat industry pinpoint farm and milking practices that improve milking performance. In total, 19 farms were visited once during the evening milking. Milking parameters (vacuum level (VL), pulsation ratio and pulsation rate, vacuum drop), milk emission flow variables (milking time, milk yield, maximum milk flow (MMF), average milk flow (AVMF), time until 500 g/min milk flow is established (TS500)), doe characteristics of 8 to 10 goats/farm (breed, days in milk and parity), milking practices (overmilking, overstripping, pre-lag time) and milking machine characteristics (line height, presence of claw) were recorded on every farm. The relationships between recorded variables and farm were analysed by a one-way ANOVA analysis. The relationships of milk yield, MMF, milking time and TS500 with goat physiology, milking routine, milking parameters and milking machine design were analysed using a linear mixed model, considering the farm as the random effect. Farm was significant (P<0.05) in all the studied variables. Milk emission flow variables were similar to those recommended in scientific studies. Milking parameters were adequate in most of the farms, being similar to those recommended in scientific studies. Few milking parameters and milking machine characteristics affected the tested variables: average vacuum level only showed tendency on MMF, and milk pipeline height on TS500. Milk yield (MY) was mainly affected by parity, as the interaction of days in milk with parity was also significant. Milking time was mainly affected by milk yield and breed. Also significant were parity, the interaction of days in milk with parity and overstripping, whereas overmilking showed a slight tendency. We concluded that most of the studied variables were mainly related to goat physiology characteristics, as the effects of milking parameters and milking machine characteristics were scarce.

Genetic selection for milking speed is feasible. The existence of a correlation structure between milking speed and milk yield, however, necessitates a selection strategy to increase milking speed with no repercussion on genetic merit for milk yield. Residual milking duration (RMD) and residual milking duration including somatic cell score (RMDS), defined as the residuals from a regression model of milking duration on milk yield or milk yield plus somatic cell score (SCS) have been advocated. The objective of this study was to undertake a first ever genetic analysis of these novel traits. Data on electronically recorded milking duration and other milking characteristics from 235 005 test-day records on 74 608 cows in 1075 Irish dairy herds were available. Variance components for the milking characteristic traits were estimated using animal linear mixed models and covariances with other performance traits, including udder-related type traits, were estimated using sire models. The heritability of milking duration, RMD and RMDS was 0.20, 0.22 and 0.18, respectively. There were little differences in the heritability of RMD or RMDS when defined using genetic regression. The genetic standard deviation of RMDS defined on the phenotypic or genetic level was 36.8 s and 37.6 s, respectively, clearly indicating considerable exploitable genetic variation in milking duration independent of both milk yield and SCS. The genetic correlation between phenotypically derived RMDS and milk yield was favourable (−0.43), but RMDS was unfavourably genetically correlated with SCS (−0.30); the genetic correlations with both traits when RMDS was defined at a genetic level were zero. RMDS defined at the phenotypic level was negatively (i.e. unfavourable) genetically correlated (−0.35; s.e. = 0.15) with mastitis; however, when defined using genetic regression, shorter RMDS was not associated with greater expected incidence of mastitis. RMDS, defined at the genetic level, is a useful heritable trait with ample genetic variation for inclusion in a national breeding strategy without influencing genetic gain in either milk yield or udder health.

A questionnaire was sent to 2099 dairy farms to investigate the occurrence of poor milkability. Based on that, the frequency of poor milkability in Swiss dairy cows was 4% and the percentage of cows treated with oxytocin (OT) was 2%. In addition, 270 dairy farms that had reported cases of animals with poor milkability were contacted for an interview to classify the disorders. Farmers suspected disturbed milk ejection in 52%, anatomical dysfunction of the teat and/or the udder in 16% and milk ejection disorder or impaired milkability caused by discernable environmental factors in 32% of the cases. Forty-eight animals from 18 farms with suspected milk ejection disorders were selected for an experimental field study which included milk flow recording and OT administration to induce milk ejection. After cessation of the spontaneous milk flow, a low dose of OT (0·2, 0·5 or 1 i.u.) was injected i.v. to test the responsiveness of the udder to OT at a physiological level. When milk flow ceased again, 10 i.u. OT was injected i.v. (supraphysiological) to ensure complete udder emptying and to determine the residual milk. Milk ejection disorder could be confirmed in 69% of the cases, i.e. if residual milk was >20% of the total milk. Because in 27% of the animals milk ejection disorder was not confirmed on the basis of elevated residual milk, an anatomical disorder of the teat and/or the udder was suspected. Milk ejection disorder could be confirmed in 69% of the cases whereas in 27% of the suspected cases an anatomical disorder of the teat and/or the udder was suspected. An increased cortisol production in cows with milk ejection disorder was not obvious because faecal concentrations of cortisol metabolites with a 5β-androstane-3α,11oxo-structure were not augmented in animals with disturbed milk ejection.

A database consisting of 35291 milking records from 83 cows was built over a period of 10 months with the objectives of studying the effect of teat cup attachment failures and milking interval regularity on milk production with an automated milking system (AMS). The database collected records of lactation number, days in milk (DIM), milk production, interval between milkings (for both the entire udder and individual quarters in case of a teat cup attachment failure) and average and peak milk flows for each milking. The weekly coefficient of variation (CV) of milking intervals was used as a measure of milking regularity. DIM, milking intervals, and CV of milking intervals were divided into four categories coinciding with the four quartiles of their respective distributions. The data were analysed by analysis of variance with cow as a random effect and lactation number, DIM, the occurrence of a milking failure, and the intervals between milkings or the weekly CV of milking intervals as fixed effects. The incidence of attachment failures was 7·6% of total milkings. Milk production by quarters affected by a milking failure following the failure was numerically greater owing to the longer interval between milkings. When accounting for the effect of milking intervals, milk production by affected quarters following a milking failure was 26% lower than with regular milkings. However, the decrease in milk production by quarters affected by milking failures was more severe as DIM increased. Average and peak milk flows by quarters affected by a milking failure were lower than when milkings occurred normally. However, milk production recovered its former level within seven milkings following a milking failure. Uneven frequency (weekly CV of milking intervals >27%) decreased daily milk yield, and affected multiparous more negatively than primiparous cows.