Effects on milk yield of milking interval regularity and teat cup attachment failures with robotic milking systems

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.     

Estimating efficiency in automatic milking systems

Milking data of 34 single automatic milking system (AMS) units on 29 Galician dairy farms were analyzed to determine the system capacity in each farm under actual working conditions. Number of cows, milk yield, milkings per cow per day, actual milking time, rejected milking time, cleaning time, and machine downtime were used to determine the number of cows milked per AMS unit to obtain the optimal values of milkings per cow and milk production. Multiple linear regression data analysis was used to model the linear relationship between the dependent variable, milk yield per AMS per year, and the predictor variables: number of cows per AMS, milkings per cow per day, milk flow rate, and rejections per AMS per year. An AMS unit milked 52.7±9.0 cows daily at 2.69±0.28 milkings per cow, with a total milking downtime of 1,947±978 h/yr and a milk yield of 549,734±126,432 kg/yr. The predictor variables cow and milk flow rate had a greater level of influence on the milk yield per AMS than milkings per cow and rejections, and explained the 87% of the variation. The AMS in Galician dairy farms could facilitate an increase of 16±8.5 cows per AMS without impairing milking performance; in this way, the quantity of milk obtained per robot annually could be increased (185,460±137,460 kg). This would make it possible to recoup the cost of the system earlier. In the present situation, the daily milking throughput could be maximized at 2.4 to 2.6 milkings per cow.     

Technical note: Effects of attachment of hind teats before cleaning and attachment of front teats on milking characteristics in automatic milking systems

Milking characteristics differ between the 4 quarters of a dairy cow udder. In particular, milking time is mostly prolonged in hind quarters compared with front quarters because of the usually higher amount of stored milk. The standard milking routine (STDMR) in both conventional and automatic milking systems (AMS) consists of teat preparation of all 4 quarters, followed by attachment of the 4 teat cups, regardless of the distribution of milk between quarters. In the current study, an alternative teat preparation and milking routine (ALTMR) in AMS was tested, which consisted of cleaning and starting the milking of hind teats before cleaning and attachment of front teats. The hypothesis was based on the fact that hind quarters have usually a longer milking time than front quarters. Starting the milking of hind quarters while the front teats are being cleaned may reduce the difference in the end of milking between front and hind quarters and thus reduce total milking time. Both routines were tested on 5 Swedish dairy farms equipped with AMS in a 4-wk experiment in which treatments were alternated weekly. Total milk yield did not differ between treatments. Machine-on time (MOT) was longer in ALTMR than in STDMR because the difference in milking time between hind and front quarters was less than the time needed to prepare the front teats. However, the longer MOT in ALTMR was compensated by a shorter total preparation time, including the attachment of the first teat cup, as only the hind teats (instead of all 4 teats) were cleaned before milking was started. This resulted in a similar total milking time from start of cleaning of the first quarter until the end of milking of the last quarter in both treatments. Because of the prolonged MOT, average milk flow rate was lower in ALTMR than STDMR. Peak flow rate was higher in ALTMR than STDMR, but only in teat cups 1 (first attached, hind quarter) and 3 (third attached, front quarter), whereas main milk flow was higher in ALTMR than STDMR in both front quarters. In conclusion, splitting teat cleaning and the start of milking between hind and front quarters does not prolong total milking time, including teat cleaning. The partially positive effect on peak and main milk flow indicates that the ALTMR is a suitable milking routine in AMS. In herds with a greater difference of milk stored in hind compared with front quarters, a reduced total milking time can be expected for ALTMR.     

Lactating dairy cows adapt quickly to being milked by an automatic milking system

Transitioning a dairy herd to an automatic milking system (AMS) from a conventional parlor system may be stressful for the cow, as many changes occur during this process. Chronic stress may affect the welfare of the cow, and acute stress during milking can decrease milk yield. Therefore, it is important to quantify if and how long stress during adaptation to an AMS might persist. Seventy-seven cows with acceptable udder and teat conformation that would not interfere with adaptation to the AMS and that were lactating n = 18, early [0 to 100 d in milk (DIM)]; n = 27, mid (100 to 200 DIM); and n = 32, late (200+ DIM) for the full duration of the project were chosen for observation. All cows had been milked previously in a double-6 herringbone milking parlor. Four stress-related behaviors [step-kick behavior both before and after attachment of teat cups, elimination (urination and defecation instances), and vocalization] were recorded during milking by trained observers, whereas milk yield was automatically recorded by the AMS. Data were collected for 24-h periods beginning on the day the cows transitioned to milking in the AMS (d 0), and on d 1, 2, 4, 8, 16, and 32 thereafter. Instances of elimination and vocalization were greater on d 0 compared with all other days (elimination: d 0 = 3.1 ± 0.09, d 1 = 0.6 ± 0.07, and 0 ± 0 instances thereafter; vocalization: d 0 = 1.7 ± 0.07, d 1 = 0.05 ± 0.04, and 0 ± 0 instances thereafter). Milk yield increased between d 0 (18.3 ± 1.7 kg) and d 1 (30.9 ± 1.7 kg). Primiparous cows (n=28) were more likely than multiparous cows (n = 49) to display step-kick behaviors both before (8.3 ± 2.5; 5.5 ± 0.6, respectively) and after (15.6 ± 2.4; 13.3 ± 1.3, respectively) teat cup attachment during milking. Eight days after introducing the cows to the AMS, over 60% of the herd was milking voluntarily and 95% of the herd was milking voluntarily within a month, which suggests that cows did not find the AMS aversive. Greater elimination and vocalization behavior and lower milk yield on d 0 relative to subsequent days indicated initial stress and discomfort with the milking process in the new system; however, the cows appeared to adapt within 24h.     

Influence of air intake on the concentration of free fatty acids and vacuum fluctuations during automatic milking

The main objective of the study was to determine whether the amount of air intake during quarter milking influences the concentration of free fatty acids (FFA) and vacuum fluctuations at the teat end when milking automatically. Air intake in the teat cup was restricted from the normal inlet of 4.5 to 7 L/min to 1.7 and 0 L/min on 2 farms and experiments were carried out as half-udder studies with 40 cows. Blockage of the air inlet reduced FFA from 1.02 to 0.77 mEq/100 g of fat in one herd and from 1.50 to 1.17 mEq/100 g of fat in the other herd. Milk yield per milking was the most significant factor influencing FFA. Air intake accounted for <20% of the variation in FFA concentration. Characteristics of the cow explained the most variation, which could mainly be assigned to the effects of milk yield, fat percentage, fat globule size, and fat globule size distribution. The interval between milkings was not significant when adjusting for milk yields. Blockage of the air inlet caused vacuum fluctuations at the teat end to increase from 15.4 to 21.5 kPa for one model of an automatic milking system (AMS), but from 12.8 to 53.6 kPa for another model. Measurements made with a flow simulator and water revealed that the AMS model and water flow were the most important factors influencing vacuum fluctuations, and that interactions existed between the diameter of the short milk tube and air intake. Free fatty acids in bulk milk from 5,980 herds averaged 0.75 mEq/L of milk for conventional herds and varied from 0.77 to 0.94 mEq/L of milk for the 5 AMS models on the Danish market. Fault detection in 55 herds pointed out that the most frequent faults in conventional herds were air leakages and intake of too much air in the cluster, whereas AMS herds had problems with the cooling and stirring of milk. Correction of the cooling faults caused FFA to decrease by 0.52 mEq/L in the AMS herds. We concluded that air intake during automatic milking is not the most important factor in reducing FFA, whereas milk yield per milking matters the most. More attention should be paid to the cooling and stirring of milk. Reducing the air intake causes vacuum fluctuations during milking to increase significantly.     

Visual detection of technical success and effectiveness of teat cleaning in two automatic milking systems

Technical success and effectiveness of teat cleaning and the management factors associated with them were evaluated in 9 automatic milking herds. In total, 616 teats cleaned with a cleaning cup and 716 teats cleaned with rotating brushes were included. Technical success and the effectiveness of teat cleaning, including the location and nature of the dirt, were evaluated visually. On average, 79.9% of teat cleanings with a cleaning cup, and 85.0% of those cleaned with brushes succeeded technically; that is, the teat was correctly positioned in the cleaning device throughout the whole cleaning process. The difference between use of teat cups and brushes was significant. However, because technical success of teat cleaning is strongly dependent on herd characteristics, these results should be interpreted with caution. Factors associated with the technical success of teat cleaning with a cleaning cup were herd, days in milk, behavior of the cow, teat color, and teat location. For rotating brushes, behavior of the cow, teat location, udder and teat structure, and days in milk were associated with technical success. Excessive udder hair and technical failure of the automatic milking machine also caused a few technically unsuccessful teat cleanings with a cleaning cup. Teats with technically successful teat cleanings were evaluated for the effectiveness of teat cleaning. From originally dirty teats, the cleaning cup had a significant advantage over the brushes in the percentage of teats that became clean or almost clean during the cleaning process (79.8 vs. 72.9%). Teat orifices were least effectively cleaned compared with the teat barrel and apex. Bedding material (peat, sawdust, or straw) on the teat was cleaned almost completely. Factors associated with the effectiveness of teat cleaning were teat cleanliness before cleaning, herd, teat cleaning method, and teat condition. The variation among herds indicates the likelihood that herd management factors can be adjusted to improve milking hygiene. There is also a need to improve the precision and effectiveness of the teat cleaning mechanisms of automatic milking systems.     

Supplementary feeding at milking and minimum milking interval effects on cow traffic and milking performance in a pasture-based automatic milking system

In extensive pastoral dairy farming systems herds graze 12 months of the year with the majority fed a near-100% pasture or conserved pasture diet. The viability of automatic milking in these systems will depend partly upon the amount of supplementary feed necessary to encourage cows to walk from the pasture to the milking unit but also on the efficient use of the automatic milking system (AMS). This paper describes a study to determine the importance of offering concentrate in the milking unit and the effect of minimum milking interval on cow movement and milking performance in a pasture-based AMS. The effects of feeding rate (FR0=0 kg or FR1=1 kg crushed barley/d) and minimum milking interval (MM6=6 h or MM12=12 h) on cow movement and behaviour during milking were studied in a multi-factorial cross-over (feeding level only, 4 weeks per treatment) experiment involving 27 mixed-breed cows milked through a single AMS. Feeding 1 kg barley in the milking unit resulted in a higher visiting frequency to the pre-selection unit (FR0=4.6 visits/d, FR1=5.4 visits/d, sed=0.35, P<0.05) and a higher yield (FR0=22.5 kg/d, FR1=23.6 kg/d, sed=0.385, P<0.01) but had no effect on milking frequency (FR0=1.6 milkings/d, FR1=1.7 milkings/d, sed=0.04, NS). Minimum milking interval was the major factor influencing milking frequency (MM6=1.9, MM12=1.4 milkings/d, sed=0.15, P<0.01). The absence of feeding in the milking unit had no negative effect on behaviour during milking or the number of cows that had to be manually driven from the paddock. The results show that automatic milking can be combined with a near-100% pasture diet and that milking interval is an important determinant for maximizing milk harvested per AMS.     

Genetic analyses of novel temperament and milkability traits in Norwegian Red cattle based on data from automatic milking systems

The number of dairy cows milked in automatic milking systems (AMS) is steadily increasing in Norway. Capacity and efficiency of AMS are highly dependent on the individual cow's milking efficiency, such as milking speed and occupation time in the milking robot. Cows meet new challenges in herds utilizing AMS. Consequently, new or revised traits may be needed for genetic evaluation of dairy cattle. The AMS records relevant information on an individual cow basis. The aims of this study were to estimate genetic parameters of new automatically recorded milkability and temperament traits. Data from 77 commercial herds with Norwegian Red dairy cattle were analyzed by mixed linear animal models. The final data set contained 1,012,912 daily records from 4,883 cows in first to ninth lactation. For variance component estimation, univariate and bivariate models were used. Daily records of box time (BT), average flow rate (FR), kilograms of milk per minute of box time (MEF), handling time (HT), log-transformed HT, milking frequency, and milking interval were analyzed with repeatability models. Among these traits, FR, BT, and MEF showed the highest heritabilities of 0.48, 0.27, and 0.22, respectively, whereas heritability of log-transformed HT, HT, milking frequency, and milking interval was low (0.02–0.07). Unsuccessful milkings expressed as rejected milkings, incomplete milkings (IM), milkings with kick-offs (KO), and teat not found also showed low heritabilities (0.002–0.06). Due to low frequency, KO, rejected milkings, IM, and teat not found were also analyzed as proportions per lactation, which resulted in slightly higher heritability estimates. Genetic correlations were favorable and intermediate to strong between BT, HT, MEF, and FR with absolute values above 0.50. Intermediate and favorable correlations were found for IM and KO with BT, HT, MEF, and FR. Cow milkability in AMS can be improved by selection for reduced number of unsuccessful milkings, faster FR, increased MEF, and shorter BT and HT. Our results confirm that automatically recorded data on milkability and temperament can be valuable sources of information for routine genetic evaluations and that milking efficiency in AMS can be genetically improved.     

Simulation model of quarter milk flowrates to estimate quarter and cow milking duration and automated milking system's box duration

The aims of this research were (1) to develop a model to simulate a herd of cows and quarter milk flowrates for a milking and derive quarter and udder milking durations and box duration (i.e., the time a cow spends inside the robot) for a group of cows milked with an automatic milking system (AMS); (2) to validate the simulation by comparing the model outcomes with empirical data from a commercial AMS dairy farm; and (3) to apply teatcup removal settings to the simulation to predict their effect on quarter and cow milking duration and box duration in an AMS. For model development, a data set from an AMS farm with 32 robots milking over 1,500 cows was used to fit the parameters to the variables days in milk, parity, and milking interval, which were subsequently used to create a herd of cows. A second data set from 2019 from an AMS farm with 1 robot milking 60 cows that contained quarter milk flowrates (at 2-s intervals) was used to extract the parameters necessary to simulate quarter milk flowrates for a milking. We simulated a herd of cows, and each was assigned a parity, days in milk, milking interval, and milk production rate. We also simulated milk flowrates every 1 s for each quarter of each cow. We estimated quarter milking duration as the total time that flowrate was greater than 0.1 kg/min after a minimum of 1 min of milk flow. We incorporated a randomly sampled attachment time for each quarter and calculated cow milking duration as the time from the first quarter attached to the last quarter detached. We included a randomly sampled preparation time which, added to cow milking duration, represented box duration. For simulation application, we tested the effect of quarter teatcup removal settings on quarter and cow milking duration. The settings were based on absolute flowrate (0.2, 0.4, and 0.6 kg/min) or a percentage of the quarter's 30-s rolling average milk flowrate (20, 30, and 50%). We simulated over 84,000 quarter milkings and found that quarter milking duration (average 212 s) had a mean absolute percent error (MAPE) of 7.5% when compared with actual data. Simulated cow milking duration (average 415 s) had a MAPE of 8%, and box duration (average 510 s) had a MAPE of 12%. From simulation application, we determined that quarter milking duration and box duration were reduced by 19% (209 vs. 170 s) and 6.5% (512 vs. 479 s), respectively, when increasing the teatcup removal flowrate from 0.2 to 0.6 kg/min. Quarter milking duration and box duration were 7% (259 vs. 241 s) and 3% (590 vs. 573 s) longer respectively by using a teatcup removal setting of 20% of the quarter's rolling average milk flowrate, compared with 30%. Both results agree with previous research. This simulation model is useful for predicting quarter and cow milking and box duration in a group of cows and to analyze the effect of milking management practices on milking efficiency.     

Evaluation of the performance of the first automatic milking system for buffaloes

The objective of this study was to evaluate the response of buffaloes to automatic milking, examining the relationships between milking interval, milk production, and milking time for this species. A total of 7,550 milking records from an average of 40 buffaloes milked by an automatic milking system (AMS) were analyzed during a 3-mo experimental period at a commercial farm with Italian Mediterranean buffaloes in southern Italy. Date and time of animal identification, milk yield, milking duration, milking interval, and average milk flow rate were determined for each milking. The results were also used to predict the maximum number of milkings per day and the optimal number of buffaloes per AMS for different levels of milk production. The average interval period between 2 consecutive milkings was 10.3 h [standard deviation (SD) 3.3]. Overall, 3.4 and 25.7% of the milkings had an interval of ≤6 h or >12 h, respectively. Milking duration averaged 8.3 min per buffalo per milking (SD 2.7). The average milk flow rate was 1.3 kg/min (SD 0.5) at a milk yield of 2.8 kg per milking (SD 1.4). Assuming that the milking station is occupied 80% of the time, the number of milkings ranged from 136 to 152 per day and the optimal number of buffaloes per AMS ranged from 59 to 66 when the production level increased from 2 to 5 kg of milk per milking. Automatic milking systems are suitable for buffalo, opening new options for the management of dairy buffalo farms.     

Factors associated with productivity on automatic milking system dairy farms in the Upper Midwest United States

The objective of this study was to identify housing and management factors associated with productivity on automatic milking system (AMS) dairy farms measured as daily milk yield/AMS and daily milk yield/cow. Management, housing, and lameness prevalence data were collected from 33 AMS farms in Minnesota and Wisconsin during a farm visit. All farms in the study used free-flow cow traffic. Mixed model analysis of cross-sectional data showed that farms with automatic feed push-up via a robot produced more milk per AMS/day and per cow/day than farms where feed was pushed up manually. New versus retrofitted facility, freestall surface, manure removal system, and the number of AMS units/pen were not associated with daily milk yield per AMS or per cow. Cow comfort index (calculated as number of cows lying down in stalls divided by total number of cows touching a stall) was positively associated with daily milk yield/cow. Prevalence of lameness and severe lameness, number of cows per full-time employee, depth of the area in front of the AMS milking station, and length of the exit lane from the AMS milking station were not associated with daily milk yield per AMS or per cow. Multivariable mixed model analysis of longitudinal AMS software data collected daily over approximately an 18-mo period from 32 of the farms found a positive association between daily milk yield/AMS and average age of the cows, cow milking frequency, cow milking speed, number of cows/AMS, and daily amount of concentrate feed offered/cow in the AMS. Factors negatively associated with daily milk yield/AMS were number of failed and refused cow visits to the AMS, treatment time (the time spent preparing the udder before milking and applying a teat disinfectant after milking), and amount of residual concentrate feed/cow. Similar results were also found for daily milk yield on a per cow basis; however, as it would be expected, average days in milk of the herd were also negatively associated with daily milk yield/cow. These findings indicate that several management and cow factors must be managed well to optimize AMS productivity.     

Effect of automatic cluster remover settings on production, udder health, and milking duration

This study evaluated the effect of 4 criteria for determining the end-point of milking on milk yield, milk composition, completeness of milking-out, teat skin condition, somatic cell count (SCC), and the incidence of clinical mastitis (CM) in pasture-based dairy cows milked over 35 wk. The objective was to reduce milking duration without affecting milk production, SCC, or CM. Milking end-point treatments were as follows: cluster removed at a milk flow rate of 0.2 kg/min (ACR200); cluster removed at a milk flow rate of 0.4 kg/min (ACR400); cluster removed at a milk flow rate of 0.2 kg/min or at a maximum cluster attachment time from d 5 of lactation (MaxTEarly); and cluster removed at a milk flow rate of 0.2 kg/min until an average of 63 ± 21 d in milk, then cluster removed at a milk flow rate of 0.2 kg/min or a maximum cluster attachment time (MaxTPeak). Maximum cluster attachment times were set at 7.5 min and 5.4 min for morning and afternoon milkings, respectively. Cows (approximately 94/treatment) were assigned to treatment at calving and milked twice daily at intervals of 9 and 15 h. Milking duration was shorter for ACR400, MaxTEarly, and MaxTPeak compared with ACR200. During wk 1 to 15, milk, protein, and lactose yields were less for MaxTEarly than for ACR400 and MaxTPeak, but not different from ACR200. During wk 16 to 35 and over the entire experiment, total milk, fat, protein, and lactose yields did not differ among treatments. Teat condition did not differ among the 4 treatments. Postmilking strip yield in wk 12 was greatest for MaxTEarly and least for ACR200; at wk 27, however, treatment had no effect on the completeness of milking-out. No differences were observed in either teat condition or the proportion of cows with at least 1 case of CM during the 35 wk. Somatic cell count was low across all treatments, but highest for ACR400. Increasing the automatic cluster remover threshold setting from 0.2 to 0.4 kg/min decreased milking duration without affecting milk production, CM, or teat condition. Combining a cluster removal milk flow threshold setting with a maximum cluster attachment time, when applied from either early lactation or from peak lactation, reduced milking duration without affecting milk production, CM, or SCC. Both strategies have potential to improve milking efficiency in dairy herds in which premilking preparation is minimal.     

Relationship among udder and teat morphology and milking characteristics.

Teat cup liner slips, manual milking machine adjustments, milk yields, and milking times were recorded during both morning and evening milkings for 8 d on 97 Holstein cows in The Pennsylvania State University dairy herd. Fore and rear udder heights (distance from floor to udder), udder levelness, distances between teats (before and after milking), teat lengths, teat diameters, and teat end shapes were measured on the same cows. Product-moment correlations among the morphological characteristics, linear slips, manual adjustments, milk yields, and milking times were determined. Residual correlations from a model including lactation number and DIM (linear and quadratic) were also calculated. The variation among cows in machine liner slips and manual adjustments within and across lactation number and DIM can be partially explained by udder and teat morphology. Wider teats were associated with increased linear slips and increased manual adjustments. More tilted udders (rear quarters lower than front quarters) were associated with increased liner slips and tended to be associated with increased manual adjustments. In addition, larger teat diameters and longer teats tended to be associated with increased liner slips.     

Detecting and predicting changes in milk homogeneity using data from automatic milking systems

To ensure milk quality and detect cows with signs of mastitis, visual inspection of milk by prestripping quarters before milking is recommended in many countries. An objective method to find milk changed in homogeneity (i.e., with clots) is to use commercially available inline filters to inspect the milk. Due to the required manual labor, this method is not applicable in automatic milking systems (AMS). We investigated the possibility of detecting and predicting changes in milk homogeneity using data generated by AMS. In total, 21,335 quarter-level milk inspections were performed on 5,424 milkings of 624 unique cows on 4 farms by applying visual inspection of inline filters that assembled clots from the separate quarters during milking. Images of the filters with clots were scored for density, resulting in 892 observations with signs of clots for analysis (77% traces or mild cases, 15% moderate cases, and 8% heavy cases). The quarter density scores were combined into 1 score indicating the presence of clots during a single cow milking and into 2 scores summarizing the density scores in cow milkings during a 30-h sampling period. Data generated from the AMS, such as milk yield, milk flow, conductivity, and online somatic cell counts, were used as input to 4 multilayer perceptron models to detect or predict single milkings with clots and to detect milking periods with clots. All models resulted in high specificity (98–100%), showing that the models correctly classified cow milkings or cow milking periods with no clots observed. The ability to successfully classify cow milkings or cow periods with observed clots had a low sensitivity. The highest sensitivity (26%) was obtained by the model that detected clots in a single milking. The prevalence of clots in the data was low (2.4%), which was reflected in the results. The positive predictive value depends on the prevalence and was relatively high, with the highest positive predictive value (72%) reached in the model that detected clots during the 30-h sampling periods. The misclassification rate for cow milkings that included higher-density scores was lower, indicating that the models that detected or predicted clots in a single milking could better distinguish the heavier cases of clots. Using data from AMS to detect and predict changes in milk homogeneity seems to be possible, although the prediction performance for the definitions of clots used in this study was poor.     

Effects of an automatic postmilking teat dipping system on new intramammary infections and iodine in milk

A technology of automatically applying a postmilking teat dip via the milking machine prior to machine detachment was compared to manual postmilking teat dipping with a teat dip cup for effects on new IMI and iodine content in milk. One hundred twenty Holstein cows were experimentally challenged in a 22-wk trial with Streptococcus agalactiae and Staphylococcus aureus and 148 Holstein cows were experimentally challenged with Streptococcus uberis in another 22-wk trial. The bacterial suspensions were applied to teats of all of the cows after premilking udder preparation and immediately prior to milking machine attachment. In both trials, cows were divided among four treatments: no postmilking teat dipping; manual postmilking teat dipping with a proven efficacious iodophor teat dip; manual postmilking teat dipping with an iodophor teat dip formulated for an automatic postmilking teat dipping system; and automatically postmilking teat dipping via milking machines with an iodophor teat dip formulated for the automatic postmilking teat dipping system. The postmilking teat dipping treatments reduced new Staph. aureus IMI by 64.5, 76.5, and 88.2%; new Strep. agalactiae IMI by 61.5, 77.8, and 94.4%; and new Strep. uberis IMI by 63.5, 82.5, and 93.8%, respectively, against the treatment of no postmilking teat dipping. The treatment applying the postmilking teat dip automatically via milking machines had the lowest number of new IMI caused by the three pathogens. Teat end and teat skin condition were characterized as normal at the end of the study with no differences between treatments. There were no differences with regard to iodine content in milk between treatments.     

Effect of teatcup removal settings on milking efficiency and milk quality in a pasture-based automatic milking system

In automatic milking systems (AMS), it is important to maximize the amount of milk harvested per day to increase profitability. One strategy to achieve this goal is to reduce the time it takes to milk each cow. Several studies in conventional milking systems have shown that milking time can be reduced by increasing the milk flow rate at which the teatcup is removed. One study analyzed the effect of increasing the milk flow switch point on milking time in a confinement AMS. No research has been conducted on teatcup removal settings in pasture-based automatic milking systems. Furthermore, not all AMS remove the teatcups based on absolute milk flow rate (kg/min); hence, it is important to study alternative strategies. The aim of this experiment was to measure the effect of 3 novel teatcup removal strategies on box time (time in the AMS), milking time, somatic cell count (SCC), and milk production rate of cows milked in a pasture-based automatic milking system. Each teatcup removal strategy in this study was applied for a period of 1 wk to 1 of 3 groups of cows and then switched to the following group until cows had transitioned through all treatments. The teatcup removal strategies consisted of removing the teatcup when the quarter flow rate fell below 20% of the quarter rolling average milk flow rate (TRS20), when quarter milk flow rate was below 30% of the rolling average milk flow rate (TRS30), and when quarter milk flow rate dropped below 50% of the rolling average milk flow rate (TRS50). A limit prevented teatcup removal if the calculated milk flow rate for teatcup removal was above 0.5 kg/min. This limit was in place for all treatments; however, it only affected the TRS50 treatment. The TRS30 strategy had 9-s shorter milking time and 11-s shorter box time than the TRS20 removal strategy. The TRS50 strategy had 8-s shorter milking time and 9-s shorter box time than the TRS20 teatcup removal strategy. There was no significant difference in milking time or box time between the TRS30 and TRS50 teatcup removal strategies, probably due to the large variability in milk flow rate at teatcup removal. The TRS20 and TRS30 strategies did not differ in SCC or milk production rate. The 0.5 kg/min limit, which affected roughly 25% of milkings in the TRS50 treatment, may have distorted the effect that this setting had on milk time, box time, milk production rate, or SCC. The difference in box time for the TRS30 and TRS50 strategies could allow for more than 3 extra milkings per day.     

Effects of an automatic milking system on milk yield and quality of Mediterranean buffaloes

A study was carried out to evaluate the effects of an automatic milking system (AMS) on milk yield and composition of buffalo (Mediterranean-type Bubalus bubalis) cows. Performed from January 2015 to December 2015 in an organic buffalo dairy farm equipped with both a traditional tandem milking parlor and an AMS, the study involved 90 primiparous buffaloes randomly allotted to a tandem or AMS group from 5 to 10 d of lactation onward. Number of milkings per day and daily milk yield of each cow were recorded, and individual milk sampling was carried out twice a month. Compared with the tandem, the AMS group showed significantly higher daily milk yield and persistence of lactation. Use of the AMS resulted in higher protein and casein contents, and lower somatic cell and total bacterial counts, whereas fat, freezing point, and pH were unaffected by the system. We conclude that, in terms of milk yield and quality, automatic milking may be a suitable alternative to conventional milking for buffaloes.     

Premilking teat preparation for Australian pasture-based cows milked by an automated milking system

Economic viability of automatic milking systems (AMS) within an Australian pasture-based farming system will be largely determined by the throughput (cows milked/h), which is the result of processes occurring while the cow is in the AMS milking crate. Premilking udder preparation is automated and optional on all AMS. Yet, very few conventional farms in Australia conduct premilking teat preparation regimens, with the majority (78%) strategically washing only visibly dirty teats before milking cup attachment. The objective was to determine the impact of udder preparation in an AMS on the total time spent by cows in the AMS milking unit (crate time). An experiment was conducted with 80 lactating Holstein-Friesian cows in a crossover design over two 5-wk periods to determine the effect of premilking teat preparation (no wash vs. wash) on milk yield, milk harvest rates, and total crate time per milking session in an AMS. Within this study there was no significant effect of treatment on quarter milk conductivity (no wash = 4,858 vs. wash = 4,829 ± SE = 17 μS/cm), milk blood concentration (no wash = 115.7 vs. wash = 112.3 ± 7.3 ppm) or test-day somatic cell counts (no wash = 2.044 vs. wash = 2.039 ± 0.025 log₁₀ SCC). There was similar total daily milk yield for the 2 treatments (no wash = 20.5 vs. wash = 20.1 ± 0.2 kg of milk), but a greater mean quarter milk flow rate resulting from the wash treatment (no wash = 0.950 vs. wash = 0.981 ± 0.013 kg of milk/min). The faster milking was not sufficient to counter the time associated with washing, resulting in longer crate time (no wash = 6.02 vs. wash = 7.12 ± 0.08 min/milking session) and therefore, lower harvest rate (no wash = 2.08 vs. wash = 1.74 ± 0.02 kg of milk/min crate time). Not washing teats would allow more efficient AMS utilization by potentially allowing more cows to be milked per machine, which would likely have a positive effect on the economic viability of this technology. The results indicate that a longer term study, investigating the effect of washing teats on udder health and milk quality, is warranted.     

Milk quality and automatic milking: fat globule size, natural creaming, and lipolysis

Thirty-eight Italian Friesian first-lactation cows were allocated to 2 groups to evaluate the effect of 1) an automatic milking system (AMS) vs. milking in a milking parlor (MP) on milk fat characteristics; and 2) milking interval (≤480, 481 to 600, 601 to 720, and >720 min) on the same variables. Milk fat was analyzed for content (% vol/vol), natural creaming (% of fat), and free fatty acids (FFA, mEq/100 g of fat). Distribution of milk fat globule size was evaluated to calculate average fat globule diameter (d₁), volume-surface average diameter (d₃₂), specific globule surface area, and mean interglobular distance. Milk yield was recorded to calculate hourly milk and milk fat yield. Milking system had no effect on milk yield, milk fat content, and hourly milk fat yield. Milk from AMS had less natural creaming and more FFA content than milk from MP. Fat globule size, globular surface area, and interglobular distance were not affected by milking system per se. Afternoon MP milkings had more fat content and hourly milk fat yield than AMS milkings when milking interval was >480 min. Milk fat FFA content was greater in AMS milkings when milking interval was ≤480 min than in milkings from MP and from AMS when milking interval was >600 min. Milking interval did not affect fat globule size, expressed as d₃₂. Results from this experiment indicate a limited effect of AMS per se on milk fat quality; a more important factor seems to be the increase in milking frequency, generally associated with AMS.     

Factors affecting milk flow traits in dairy cows: results of a field study

The study of milk flow curves provides useful information for enhancing milking efficiency and protecting udder health by adapting milking machine and milking procedures to the physiological requirements of the cow. The aim of this experiment was to investigate, using field data, the relationships among traits of the milk flow curves, their sources of variation, and milking performances in terms of milk production, machine-on time, and udder health. A total of 2,486 milk flow curves of the whole udder were collected in 82 Italian Holstein-Friesian dairy herds in the Lombardy region of Italy. Approximately one-third (35.1%) of milk flow curves were classified as bimodal. Most flow characteristics were influenced by lactation number, days in milk, and peak flow but also strongly affected by premilking operations. Proper udder preparation, including forestripping and predipping, resulted in better milking performances compared with poor preparation, with greater milk yield per milking, shorter milking time, and lesser bimodality. Premilking delay time, between the start of teat stimulation and cup attachment, affected milking time significantly: The shortest milking time was obtained for a range of delay time between 1 and 60 s. As the delay time increased, the percentage of bimodality dropped significantly. Increasing the number of clusters per operator led to greater percentages of bimodal curves. The greater somatic cell count of cows with bimodal curves supports the hypothesis of the negative effect of bimodality on udder health and indicates the importance of avoiding its occurrence using proper pre-milking procedures.