Herd-level variables associated with delayed milk ejection in Michigan dairy herds

The objective of this study was to determine which herd-level variables were associated with delayed milk ejection (bimodal milk let-down) in 64 Michigan dairy herds. Median herd size was 294 cows (range 59 to 2,771 cows). For each herd, milking protocols were observed and milk flow dynamics were estimated by use of digital vacuum recorders. Surveys were also administered to the producers to measure mastitis management practices and attitudes. Milk flow dynamics were recorded for a total of 3,824 cow milkings, with a mean of 60 milkings per herd (range of 11 to 154). Backward multivariable analysis was used to determine which of the 47 herd-level milking and management variables were associated with delayed milk ejection (cows with milk let-down periods between milking cluster attachment and the incline phase of milk flow of >30 s). Delayed milk ejection occurred in an average of 25% of the cows in each herd (range 0 to 75%). A multivariable model found that the proportion of cows in a herd with delayed milk ejection was negatively associated with mean total time of tactile stimulation during premilking routines and positively associated with herd size.     

Short communication: Herd-level variables associated with overmilking in Michigan dairy herds

The objective of this study was to determine the herd-level variables that are associated with overmilking in 64 Michigan dairy herds with a mean herd size of 451 cows (range: 59–2,771 cows). Participating producers completed surveys to indicate their mastitis management practices and attitudes. Additionally, milking protocols were observed and milk flow dynamics for 3,824 cows were estimated using digital vacuum recorders. The median duration of overmilking was 47 s (95% confidence interval, CI: 38.6 to 55.9 s), with a mean of 55% (95% CI: 49.5 to 61.1%) of cows within each herd overmilked by at least 30 s. Median milking time for all herds was 324 s (95% CI: 302 to 346 s) and was found to be positively correlated with median duration of overmilking (r = 0.670). Backward multivariate analysis was used to determine which of 45 herd-level milking and management variables were associated with median duration of overmilking. Median duration of overmilking was negatively associated with the duration of time needed to complete 1 milking for the entire herd (adjusted R² = 0.13). Herds that operate milking facilities below maximum daily capacity may be prone to overmilking. Given the low coefficient of determination, variables unaccounted for in this study, such as equipment function or manual detachment by milking operators, are likely the most important risk factors for overmilking.     

Adoption and consistency of application of premilking preparation in Ontario dairy herds

Milking management practices that affect udder health have been widely studied, leading to a variety of evidence-based recommendations. Lack of adoption or inconsistency in milking practices can interfere with efforts to prevent mastitis in the herd. The study objective was to assess the variation in adoption and application consistency of important milk harvest practices between and within farms over time. During the summer of 2013, 50 herds in southern Ontario were visited twice within a month, at milking time, and a single person observed and time-recorded premilking preparation procedures. A generalized mixed model was used to partition the variance for predisinfectant contact time and preparation lag time (time between the first contact with the teats and cluster attachment), and determine the proportion of variation attributable to farms, milkers, visits, and characteristics of a cow milking. Using logistic regression, models were built to assess factors affecting adequate contact time and adequate preparation lag time, respectively. Farm, the person(s) milking, and visit number were used as random effects in both instances. In both models, farm-to-farm differences and variations between cows during a specific milking accounted for the largest part of the variability seen in both contact time (47 and 44%, respectively) and preparation lag time (40 and 36%, respectively). For both outcomes, milkers were consistent in their routines over the 2 visits (only 9 and 3.1% of total variance for contact and preparation lag time, respectively). Parlors were more likely to meet the recommended contact time than tie-stalls; increased number of milkers at milking time and having contact times under 30 s had negative effects on meeting recommended preparation lag time. The majority of farms in the study complied with the recommendations for adequate milking practices; however, most did not follow a consistent timed protocol. There are several potential sources of variation in the milking routine on a dairy farm. To improve milk quality and udder health, it is important to identify whether best management practices are being implemented on each farm. Producers appeared to be consistent in the application of milking procedures across time, regardless of whether or not they were correct. Hence, with corrective education and training, improvements in these practices could be experienced and maintained to promote better udder health.     

Herd-level risk factors associated with cow mortality in Swedish dairy herds

An increase in on-farm mortality (euthanasia and death) in dairy herds has been reported in several countries in the last decade. This does not only imply possible problems with animal welfare, but it also causes economic losses to the farmer. The objective of this study was to evaluate time trends in on-farm dairy cow mortality in Sweden and identify potential herd-level risk factors. Data were retrieved on all Swedish dairy herds enrolled in the milk recording scheme between 2002 and 2010. Herds with a herd size of <20 cows or a mortality rate (MR) of >40 dead or euthanized cows per 100 cow-years were excluded. Two different models were used: 1 multiple-year analysis, which included 6,898 herds during the period 2002 to 2010 and 1 single-year analysis including 4,252 herds for the year 2010, where other variables that were not present during the entire multiple year study were analyzed. The outcome variable was the number of euthanized and dead cows per year and season. A negative binomial regression model, adjusted for clustering within herd, was applied to both models. Fixed effects in the multiple-year analysis were breed, calving interval, herd size, milk yield, region, season, pasture period, and year. The fixed effects in the single-year analysis were breed, calving interval, conventional versus organic farming, herd size, housing system, milk yield, region, and season. The results demonstrated that MR gradually increased from 5.1 to 6.6 events per 100 cow-years during the study period. Swedish MR are consequently on par with, or even greater than, MR among dairy herds in other comparable countries. Higher mortality was associated with larger herd size, longer calving intervals, and herds that had Swedish Holstein as the predominant breed. Lower mortality was observed in herds with a higher herd average milk yield, during the fall and winter, and in organically managed herds. There were regional differences in mortality. An interaction between herd size and season was found in both models. Also, an interaction between housing system and milk yield was found in the single-year analysis. This first assessment of on-farm mortality in Swedish dairy herds confirmed that the MR has increased over the last few years. The study also identified some herd-level risk factors.     

Effect of mechanical premilking stimulation on milking duration in late lactation

This study documents the effect of mechanical prestimulation on the milking duration of pasture-based cows in late lactation to better harness increased capacity of automation in the milk harvesting process. Premilking stimulation, provided via manual or mechanical means, has been shown to promote the milk letdown reflex and assist in achieving quick, comfortable, and complete milk removal from the udder. The literature is lacking knowledge on the effect of mechanical premilking stimulation on milking duration, especially in late lactation and in pasture-based systems, and many pasture-based farms do not practice a full premilking routine because of a lack of labor availability. The current study addresses this gap in knowledge. In this study, we tested 2 treatments: (1) the No Stim treatment used normal farm milking settings with no premilking preparation and (2) the Stim treatment used 60 s of mechanical premilking stimulation, with a rate of 120 cycles per minute and a pulsator ratio of 30:70 on cluster attachment. Once the 60 s of stimulation had elapsed, normal milking settings resumed for the remainder of the milking. Sixty cows were enrolled in the study, which ran for 20 d. The effect of treatment on a.m. milking duration was significant, a.m. milking duration for Stim was 12 s shorter than that of No Stim. The effect of treatment on p.m. milk duration was not significant. Treatment had no effect on a.m./p.m. milk yields, average milk flowrates or peak milk flowrates. Significant differences emerged between treatments on a.m. and p.m. dead time (time from cluster attachment to reach a milk flowrate of 0.2 kg/min). The a.m. and p.m. dead times were 6 s shorter for Stim compared with No Stim. The time taken to achieve peak milk flowrate (time to peak) at morning milking was 7 s shorter for Stim compared with No Stim, and treatment yielded no significant effects on time to peak at p.m. milkings. Treatment also had no significant effect on log₁₀ somatic cell count. Although the percentage of congested teat-ends and teat-barrels was numerically lower for Stim versus No Stim, no statistical differences were detected across these measures. Based on the results of the study, we found merit in applying 60 s of mechanical pre-stimulation at a.m. milking from a milking duration perspective. However, the strategy was not as successful for the p.m. milking. Analysis of the milk flowrate profiles recorded during the study suggest potential utility in employing different machine settings for various milkings based on anticipated yield and level of udder fill.     

Short communication: Milking order consistency of dairy cows in large Australian herds

We used on-farm records from dairy infrastructure to examine the consistency of the milking order over 150 d in 5 Australian dairy herds that were milking more than 500 cows as a single group. Within a single day the difference in milking order rank position was less than 20 percentage points for 72% of cows. The correlation coefficient comparing milking rank position in the morning and afternoon was 0.72, with the position of cows at the beginning and end of the milking order being more consistent than cows toward the middle of the milking order. Over a period of 150 d, cows with a mean position in the first and last 20% of the milking order maintained their position more consistently than cows in the middle of the milking order. Milking position of cows between one month and the next was highly correlated (r = 0.88). In large herds, subpopulations of cows are regularly milked toward the beginning and the end of the milking order. It is common for cows to be collected from the paddock as a group, to wait as a group in the dairy yard to be milked, and to return individually to the paddock or feed pad immediately after they have been milked. Thus, cows milked later in the milking order are likely to be away from the paddock for several hours longer than cows milked earlier in the milking order. This may affect their welfare though differences in time available for lying down, equality of pasture eaten, and time spent standing in the dairy yard.     

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.     

Herd-level associations between somatic cell counts and economic performance indicators in Brazilian dairy herds

The aims of the present study were to provide a portrait of the techno-economic status of dairy herds in Minas Gerais, Brazil, particularly with respect to bulk-tank somatic cell count (BTSCC) data, and to examine the herd-level associations of BTSCC with various economic performance indicators (EPI). Data from 543 herds, 1,052 herd-year records in total, spread over 3 years (2015–2017), from the South and Southwest mesoregions of Minas Gerais State were provided by the Brazilian Support Agency to Micro and Small Companies Division Minas Gerais (SEBRAE). Herds had an average of 82 lactating cows per herd, milk yield of 17 L/cow per day, and availability of financial information via routine monthly economic surveys. The EPI data (revenue, gross margin, GM; net margin, NM; profit; break-even point; and operational profitability) of each herd was measured monthly by SEBRAE personnel, and herd-year averages of all variables were computed. Bulk-tank data (SCC, total bacterial count, content of crude protein and fat) taken by producers or dairy processors were recorded by SEBRAE personal; and corresponding herd-year averages were calculated and included in the SEBRAE database. There were 209 selected herds, which passed all edit checks, and which had data for all 3 years. The EPI (all expressed on a per-cow basis, $/cow per year) were analyzed, including the effects of region, year, log (ln) BTSCC, production level, and herd size, together with the random effect of herd nested within region. A high proportion of herds (94.6%) presented data records (herd-years) with an average BTSCC > 200 × 10³ cells/mL: 37.8% of herd-year records had BTSCC between >200 and ≤400, 14.5% with BTSCC between >400 and ≤500, 25% with BTSCC between >500 and ≤750, and 17.3% with BTSCC >750. For each unit increase in ln BTSCC, revenue declined by $228.5/cow per year, GM by $155.6/cow per year, and profit by $138.6/cow per year. Herds with cows of lower production (<14 kg/d) presented lower GM ($286.8/cow per year) compared with herds containing cows producing ≥14 kg/d (≥14 and <19 kg/d = $446.5, and ≥19 kg/d = $601.9). The small-scale milk producers (<39 lactating cows) presented lower revenue ($1,914.9/cow per year) and GM ($274.5/cow per year) and consequently a negative profit (?$224.1/cow per year) compared with other herd size categories (≥39 lactating cows). The reduction in milk yield was 641 L/cow per lactation for each unit increase in ln BTSCC; this represented 9.4% of the milk yield per lactation, assuming an average milk production of 6,843.3 L/cow per lactation of cows from herds that had BTSCC ≤ 200 × 10³ cells/mL. Consequently, we found a negative association of BTSCC with profit; profit declining from $227.0 to ?53.1/cow per year when the BTSCC increased from 100 to 750 × 10³ cell/mL. In short, the lower the BTSCC, the greater the revenue, GM and NM, profit, and operational profitability of the herds. The reduction of milk yield was the main factor associated with higher BTSCC.     

Factors associated with milking characteristics in dairy cows

Milking characteristics, and in particular milking duration, are a known contributor to costs in dairy production systems. Results from previous studies suggest that higher-yielding animals, on average, milk for a longer duration. Culling or selection for reduced milking duration alone may, therefore, reduce milk yield. Here, we propose 2 new traits, residual milking duration (RMD) and residual milking duration including somatic cell score (RMDS). Residual milking duration is represented by the residuals from a least squares regression of milking duration on milk yield; RMDS is represented by the residuals from a least squares regression of milking duration on both milk yield and somatic cell score [i.e., logarithm (base 10) of somatic cell count]. The mathematical properties of least squares regression ensure than the residual traits are independent from the regressor variables, or, in other words, RMDS is not correlated with either milk yield or somatic cell score. Both RMD and RMDS were defined using electronically measured individual cow milking duration from 235,036 part-day milking events from 74,607 cows from 1,075 Irish dairy herds. Twenty-four percent of the variation in milking duration was explained by the multiple regression model containing both milk yield and somatic cell score. The phenotypic standard deviation of RMD and RMDS was 102.2 and 98.2 s, respectively, suggesting large variation in milking duration independent of milk yield (and somatic cell score). The correlation of RMD and RMDS with average milk flow rate, which may also be considered a measure of milking efficiency, was −0.74 and −0.75, respectively. Neither RMD nor RMDS was correlated with somatic cell score. However, average milk flow rate was correlated with milk yield (0.57) and milking duration (−0.38). Both RMD and RMDS are useful traits, which exhibit considerable variation and, therefore, can be used by farmers to identify phenotypically slower milking animals irrespective of milk yield (and somatic cell score). However, because of the lack of a correlation between RMD and somatic cell score in the sample population used in the present study, RMD and RMDS values per milking were almost identical.     

Relationship between glucocorticoids and prolactin during mammary gland stimulation in dairy cows

The objectives of this study were to determine the role of glucocorticoids in the regulation of prolactin (PRL) release induced by mammary gland stimulation and to investigate whether the milk depression induced by glucocorticoids in dairy cows is due to a decrease in PRL release. In experiment 1, 8 dairy cows were used in a 4 × 4 Latin square design. Four hours after the morning milking, the cows received 1 of the following treatments: (1) a 5-min manual stimulation of the mammary gland; (2) an i.v. injection of 1 mg of dexamethasone; (3) 2 infusions of 2.5 g of metyrapone (an inhibitor of cortisol biosynthesis) in the omasum 4 and 2 h before a 5-min stimulation of the mammary gland; or (4) no treatment. Sixty minutes later, the mammary gland of each cow was stimulated for 5 min. Blood samples were collected from 20 min before to 120 min after the start of the treatment. When the mammary gland was stimulated twice in 60 min, less PRL and cortisol were released during the second stimulation. Metyrapone did not affect PRL or cortisol release. Dexamethasone decreased serum cortisol concentration but did not affect PRL concentration. In experiment 2, 16 cows were used in a crossover experimental design consisting of 2 experimental weeks separated by 1 resting week. During the first week, cows were treated as follows: (1) 4 cows were injected with 0.5 g of domperidone (a PRL secretagogue) in canola oil on d 1 and 2 and 20 mg of dexamethasone on d 1; (2) 4 cows were injected with 0.5 g of domperidone on d 1 and 2; (3) 4 cows were injected with canola oil on d 1 and 2 and with 20 mg of dexamethasone on d 1; and (4) 4 cows were injected with canola oil on d 1 and 2. During the second experimental week, the same 4 treatments were repeated, except the cows that did not receive dexamethasone in the first week received it on d 1 of the second week, and cows that did receive it in the first week did not receive it in the second week. On d 1 and 2 of each week, blood samples were collected during morning milking for PRL determination. Dexamethasone reduced milk production and decreased both basal and milking-induced PRL release. It also increased milk fat and protein percentages and decreased milk lactose content. Domperidone increased basal PRL levels in serum and milk but did not affect milk yield. Although we cannot rule out the possibility that inhibition of PRL secretion or reduction of mammary gland PRL responsiveness play a role in the inhibition of milk production by glucocorticoids, the fact that enhancement of PRL secretion by domperidone could not prevent the depression of milk yield suggests that other mechanisms are involved.     

Housing,management characteristics,and factors associated with lameness,hock lesion,and hygiene of lactating dairy cattle on Upper Midwest United States dairy farms using automatic milking systems

The objectives of this cross-sectional study were to describe housing and management practices on farms using automatic milking systems (AMS) in 2 states of the upper Midwest and to evaluate the association of various housing and management factors with 3 measures of animal welfare: prevalence of lameness, severe hock lesions, and dirty cows. Fifty-four farms were visited once to collect facility measurements and observations, interview the dairy producer, and score cows for locomotion, hock lesions, and hygiene. Median number of AMS units/farm was 2 (interquartile range = 1; range = 1 to 8). Factors concerning labor were the most commonly cited reason by dairy producers for making the transition to the AMS; additional commonly cited factors were an improvement in lifestyle and human health. Number of cows fetched per AMS, or manually brought to the AMS if not milked voluntarily, was 4.7 ± 2.3 cows/AMS per day (8% of cows) for free traffic flow farms and 3.3 ± 1.8 cows/AMS per day (5% of cows) for guided traffic flow farms. Cow resting surface was significantly associated with prevalence of lameness and severe lameness. Farms with sand-bedded freestalls (17.2%) and bedded packs (17.4%) had significantly lower lameness prevalence (score ≥3 on a 1 to 5 scale, with 1 = normal locomotion) than farms with mattress freestalls (30.5%), waterbeds (25.0%), and mattresses with access to pasture (22.6%). Farms with mattresses and access to pasture had similar lameness prevalence to farms with waterbeds, but were lower than farms with mattresses only. A somewhat similar result was found for severe lameness prevalence (score ≥4 on a 1 to 5 scale, with 1 = normal locomotion); farms with sand-bedded freestalls (2.8%), bedded packs (0.0%), and mattress freestalls with access to pasture (1.5%) had significantly lower prevalence than farms with mattresses (7.1%) or waterbeds (10.8%). Severe hock lesion prevalence (score = 3 on a 1 to 3 scale, with 1 = normal, 3 = swelling) in herds with sand-bedded freestalls, waterbeds, and bedded packs were similar and significantly lower than the prevalence in mattress-based freestalls. Cows housed in sand-bedded freestalls had significantly lower prevalence of dirty cows (score = 3 or 3.5 on a 1 to 5 scale, with 1 = clean) than those housed on mattresses and waterbeds, and had significantly lower prevalence of severely dirty cows (score = 4, 4.5 or 5 on a 1 to 5 scale, with 1 = clean) than all other housing systems except waterbeds, which was similar. Manure removal system (manual, automatic, or slatted floor) was significantly associated with prevalence of severely dirty cows; farms with manual scraping had lower prevalence of severely dirty cows than farms where alley scraping was practiced automatically or slatted floors were used. Dairy producers using AMS appeared to be successful with a variety of facility designs and management practices. Cow resting surface in AMS herds was associated with some animal health and welfare measurements.     

Trends in somatic cell count deteriorations in Dutch dairy herds transitioning to an automatic milking system

Udder health is at risk when herds transition from a conventional milking system (CMS) to an automatic milking system (AMS). Somatic cell counts (SCC) are generally elevated for several months following a transition. However, such observations were made in studies conducted in the early 2000s. Technical improvements to AMS have likely been made since then, and farm management may have improved, learning from past experiences. This longitudinal observational study quantified national trends in SCC deteriorations in dairy herds that transitioned from a CMS to an AMS. Census data from the Dutch test day recording was used to determine these trends. It consisted of all cow-level SCC measurements conducted in the Netherlands from January 1, 2007 to December 31, 2019. Three udder health indicators, including the natural logarithm of the yield-corrected bulk-milk SCC, the proportion of cows having a composite SCC ≥200,000 cells/mL, and the proportion of cows having a new elevated SCC, were evaluated using multivariable population-averaged generalized estimation equations. Predicted values resulting from these statistical models were interpreted to determine trends in SCC deteriorations from 12 mo before and 12 mo after the transition to an AMS. Decreasing SCC deteriorations were observed during the study period for all 3 udder health indicators. Udder health deteriorations around the transition to an AMS were still observed, but they decreased in magnitude over the course of the study period. Bulk-milk SCC deteriorations were, for instance, 2.5 times lower in 2019 compared with those observed in 2007. Therefore, the effect of transitioning to an AMS on udder health became less severe in more recent years. However, deteriorations in the proportion of new SCC elevations were still evident toward the end of the study period. Efforts to lower udder health deteriorations in herds that transition to an AMS should therefore be continued and should intensify on factors lowering the proportion of cows having a new elevated SCC.     

Farm-level risk factors for bovine mastitis in Dutch automatic milking dairy herds

Automatic milking systems (AMS) are installed on a growing number of dairy farms worldwide. Management to support good udder health might be different on farms with an AMS compared with farms milking with a conventional milking system, as risk factors for mastitis on farms using an AMS may differ. The aim of this study was to identify farm level factors associated with mastitis on Dutch dairy farms using an AMS. In 2008, risk factor data were collected using a questionnaire combined with on-farm recordings of cow, stall, and AMS hygiene on 135 farms. These risk factor data were linked to 4 udder-health-associated dependent variables: average herd somatic cell count (HeSCCav), variance of the average herd somatic cell count (SCC) on test days (HeSCCvar), the average proportion of new high SCC cases (NHiSCC), and the farmer-reported annual incidence rate of clinical mastitis (IRCM). We employed regression models using multiple imputation to deal with missing values. Due to the high dimensionality of the risk factor data, we also performed nonlinear principal component analysis (NLPCA) and regressed the dependent variables on the principal components (PC). Good hygiene of cows and of AMS were found to be related to a lower HeSCCav and less NHiSCC. Effective postmilking teat disinfection was associated with a lower NHiSCC. A higher bulk tank milk SCC threshold for farmers' action was related to more NHiSCC. Larger farm size was related to lower HeSCCvar but higher NHiSCC. Negative attitude of farmers to animal health, higher frequency of checking AMS, and more time spent on viewing computer data were all positively related to higher IRCM. An NLPCA with 3 PC explained 16.3% of the variance in the risk factor variables. Only the first 2 PC were associated with mastitis. The first PC reflected older and larger farms with poor cow hygiene and AMS hygiene, and was related to higher HeSCCav and NHiSCC, whereas the second PC reflected newly built smaller farms with poor cow hygiene and low milk production, and was associated with higher HeSCCvar and NHiSCC, but lower IRCM. Our study suggests that many of the risk factors on conventional milking system farms are applicable to AMS farms, specifically concerning hygiene of the cows and the milking machine, but on large AMS farms, udder health may need more attention than on smaller AMS farms. Multiple imputation is instrumental to deal with missing values and NLPCA is a useful technique to process high dimensional data in our study.     

Survey of preweaning dairy calf-rearing practices in Czech dairy herds

It is important to describe weaknesses in rearing calves not only to improve their welfare, but also to detect areas where current scientific knowledge is poorly integrated into practice. A survey of preweaning calf-rearing practices was conducted using a farmer questionnaire. The survey included 136 farms, representing 11.9% of all dairy cows in the Czech Republic. Mean herd size (± standard deviation) was 326 ± 131.4 cows, and mean milk production 7,413 ± 1,389.5 kg per cow per year. We evaluated 59 farms with Holsteins (H) and 77 with the Czech Fleckvieh breed (C). The survey revealed that (1) calving in group pens predominated (67.6% of farms); (2) no disinfection of calf navels occurred on 11.8% of herds; (3) pooled colostrum was fed on 15.4% of farms; (4) colostrum quality was controlled on only 44.1% of farms, and only 73.5% of farms had reserve colostrum stocks; (5) nonmarket waste milk was fed in 64.7% of herds but it was pasteurized in only in 6.8% of herds and acidified in 35.2% of herds; (6) milk replacer was mixed with nonmarket waste milk on 52.9% of farms; (7) 58.8% of farms enabled calves to obtain milk by sucking and 41.2% by drinking from a bucket; (8) the main criterion in weaning was calf age (61.7%), followed by acceptance of starter and concentrated feed (19.9%) and lack of housing capacity (18.4%); and (9) newborn calves were individually housed on 96.7% of farms and group-housed on 3.3% of farms. The most marked differences in calf-rearing management between Holstein and Czech Fleckvieh farms were (1) a higher proportion of operations calving in tie-stalls or stanchions in C (6.5%) versus H (1.7%) farms; (2) a higher proportion of untreated navels on C (15.6%) versus H (6.8%) farms; (3) a lower proportion of C (11.7%) versus H (20.4%) farms feeding pooled colostrum; (4) a lower proportion of C (39%) versus H (50.9%) farms monitoring colostrum quality; (5) sucking milk from nipple buckets predominated (61%) on C farms, whereas drinking from an open bucket was most common (64.4%) on H farms; (6) age was the main criterion in weaning calves of both breeds (C farms: 55.8%, H farms: 69.5%), whereas the second most important criterion was lack of housing capacity (28.6% of farms) on C farms and the amount of consumed starter (25.4%) on H farms. We observed a difference in duration of colostrum period between C herds (median 5 d) and H herds (median 4 d). A tendency was observed for age of calves at weaning (C herds: median at 9.1 wk, H herds: median at 10 wk).     

Manageable risk factors associated with bacterial and coliform counts in unpasteurized bulk milk in Flemish dairy herds

Associations between herd management practices and both bacterial counts (BC) and coliform counts (CC) from 254 and 242 dairy herds in Flanders (Belgium), respectively, were studied. Data were analyzed using multivariable, multilevel linear regression analysis, allowing variance components analyses. Both BC and CC fluctuated throughout the year, although the milk quality parameters followed an opposite pattern. Bacterial count values decreased with each increase of the cleaning frequency of the cubicles (once per week, once per day, twice per day, or more than twice per day) between January and March. Herds with a conventional milking parlor had substantially lower BC than herds where the cows were milked using an automatic milking system. Lower BC were observed when the milking parlor was equipped with an automatic cluster removal system, when premilking teat disinfection was applied, when the dry cows were supplemented with a mix of minerals and vitamins, and when the teats were prepared either first wet and dried or via an automatic milking system. Milking cows with a high-pipeline milking parlor setup or with an automatic milking system was associated with substantially higher CC values. Herds where prepartum heifers were often treated with antimicrobials before calving had a lower CC than farms where heifers were either not or only rarely treated. Most variation in BC and CC resided at the herd level rather than at the observation level, indicating that management is important in the control of both BC and CC. Still, only a small proportion of the total variance was explained by factors capturing information related to the milking, herd health, and dry cow management, which suggests that the bacteriological milk quality and, in particular, CC is primarily driven by other factors than the ones included in this study.     

Effect of enhanced hygiene on transmission of Staphylococcus aureus,Streptococcus agalactiae,and Streptococcus dysgalactiae in dairy herds with automatic milking systems

The objective of the study was to evaluate the effect of hygiene measures in automatic milking units on the transmission of 3 mastitis pathogens considered to be mainly or partly transmitted from cow to cow during milking events. Two studies were conducted as within-herd experimental trials in 2 Danish commercial dairy herds (A and B) with automatic milking systems. Interventions to enhance hygiene were implemented on the automatic milking units. The 2 studies evaluated separate interventions. In herd A, the hygiene interventions were manual wash with the Lely foam unit and adjustments on the brush-mediated teat cleaning procedure. In herd B, the hygiene intervention included automatic disinfection spray on the upper surface of the brush motor and daily change of brushes. Composite milk samples were collected longitudinally at 3- or 4-wk intervals from all lactating cows. Additional milk samples were taken from cows entering or leaving the study groups. Milk samples were analyzed with quantitative PCR. A hidden Markov model implemented within a Bayesian framework was used to estimate the transmission probability. For analysis, 701 samples from 156 cows were used for herd A, and 1,349 samples from 390 cows were used for herd B. In the intervention group in herd B, transmission of Streptococcus agalactiae was reduced to 19% (95% posterior credibility interval: 0.00–64%) of the transmission in the control group, whereas transmission of Streptococcus dysgalactiae was reduced to 17% (95% posterior credibility interval: 0.00–85%) of transmission in the control group. This suggests that automatic spray on the upper surface of the brush motor with disinfectant along with daily change of brushes collectively reduced transmission of Strep. agalactiae and Strep. dysgalactiae. Results on Staphylococcus aureus in herd B and results on manual foam cleaning and brush-mediated teat cleaning adjustments in herd A were inconclusive.     

Farm business and operator variables associated with bulk tank somatic cell count from dairy herds in the southeastern United States

Mastitis is a worldwide problem in dairy cows and results in reduced milk production, the culling of cows, and other economic losses. Bulk tank somatic cell count (BTSCC) over 200,000 cells/mL often indicates underlying subclinical mastitis in dairy herds. Several preventative measures that can be implemented to help improve the incidence of mastitis exist, but surveys find these practices not fully adopted by producers. The goal of this research was to analyze the farm and operator characteristics associated with BTSCC in dairy herds by analyzing a survey of dairy producers in the southeastern United States. We examined this region because it has experienced a decline in the number of dairy farms, dairy cows, and milk production over the past 2 decades. The southeast region is also associated with higher BTSCC levels than the national average. Dairy farms in Georgia, Mississippi, Kentucky, North Carolina, South Carolina, Tennessee, and Virginia were surveyed. Producers were asked questions about the BTSCC at which they take action to address BTSCC, the information sources they use to learn about and manage BTSCC, farm structure and management characteristics, and attitudinal variables associated with profitability, managerial control, and planning horizon. Least squares regression was used to determine how these factors were associated with BTSCC levels across the 7-state region. Concern over mastitis, financial consequences of mastitis, and increased previous-year BTSCC were associated with higher current BTSCC levels. Obtaining information about mastitis from veterinarians and extension personnel, taking action against mastitis at a BTSCC less than 300,000 cells/mL, and perceived ability to control processes and mastitis incidence were associated with reduced BTSCC. We found average BTSCC was lower in North Carolina and Virginia. These results suggest that proactive producers (i.e., those that perceive they can control BTSCC and seek information from reliable sources), were more likely to report lower BTSCC. As a result, it may be possible to achieve improved milk quality, evident from lowered BTSCC, across the region.     

Producer estimates of prevalence and perceived importance of lameness in dairy herds with tiestalls,freestalls, and automated milking systems

Lameness is one of the most important welfare and productivity concerns in the dairy industry. Our objectives were to obtain producers' estimates of its prevalence and their perceptions of lameness, and to investigate how producers monitor lameness in tiestall (TS), freestall with milking parlor (FS), and automated milking system (AMS) herds. Forty focal cows per farm in 237 Canadian dairy herds were scored for lameness by trained researchers. On the same day, the producers completed a questionnaire. Mean herd-level prevalence of lameness estimated by producers was 9.0% (±0.9%; ±SE), whereas the researchers observed a mean prevalence of 22.2% (±0.9%). Correlation between producer- and researcher-estimated lameness prevalence was low (r = 0.19) and mean researcher prevalence was 1.6, 1.8, and 4.1 times higher in AMS, FS, and TS farms, respectively. A total of 48% of producers thought lameness was a moderate or major problem in their herds (TS = 34%; AMS =53%; FS = 59%). One third of producers considered lameness the highest ranked health problem they were trying to control, whereas two-thirds of producers (TS = 43%; AMS = 63%; FS = 71%) stated that they had made management changes to deal with lameness in the past 2 yr. Almost all producers (98%) stated they routinely check cows to identify new cases of lameness; however, 40% of producers did not keep records of lameness (AMS = 24%; FS = 23%; TS = 60%). A majority (69%) of producers treated lame cows themselves immediately after detection, whereas 13% relied on hoof-trimmer or veterinarians to plan treatment. Producers are aware of lameness as an issue in dairy herds and almost all monitor lameness as part of their daily routine. However, producers underestimate lameness prevalence, which highlights that lameness detection continues to be difficult in in all housing systems, especially in TS herds. Training to improve detection, record keeping, identification of farm-specific risk factors, and treatment planning for lame cows is likely to help decrease lameness prevalence.     

Short communication: Assessing the accuracy of inline milk fat-to-protein ratio data as an indicator of hyperketonemia in dairy cows in herds with automated milking systems

The objective of this study was to evaluate the accuracy of inline milk fat-to-protein (F:P) data to detect hyperketonemia (HYK) in herds with automated milking systems (AMS). The F:P ratio has been investigated as a tool for detecting HYK with moderate accuracy in past studies, but inline F:P data in AMS may also be useful for HYK screening. To assess the accuracy of these data in commercial settings, we monitored 484 cows from 9 AMS herds for their first 3 wk of lactation, taking blood samples once per week (n = 1,427). Positive cases of HYK were defined by whole-blood β-hydroxybutyrate (BHB) concentrations ≥1.2 or ≥1.4 mmol/L. Milk data were collected from the AMS software on each farm for each cow and converted into 4 different F:P values: (1) value from the same day as the BHB test; (2) 5-d centered-moving average (CMA); (3) 5-d backward-moving average (BMA); (4) 5-d forward-moving average (FMA). In linear regression models, all 4 values were associated with BHB, but slope estimates varied and R² were low: same day (slope = 0.95, R² = 0.07), CMA (slope = 1.05, R² = 0.07), BMA (slope = 0.65, R² = 0.04), and FMA (slope = 1.23, R² = 0.09). In logistic regression models, the odds of having HYK (BHB ≥1.2 mmol/L) increased with every 0.1-unit increase from the mean F:P ratio (1.16) using same-day values (odds ratio = 1.35, 95% confidence interval = 1.25–1.47) and CMA (odds ratio = 1.39, 95% confidence interval = 1.27–1.51). The same increase in F:P from mean BMA (1.14) and FMA (1.17) was associated with 1.22 and 1.49 times the odds of HYK, respectively. For all 4 F:P variations, we evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of different F:P thresholds with HYK status. As the F:P threshold increased from 1.17 to 1.50, sensitivity decreased (range: 77 to 9%) but specificity increased (range: 58 to 96%). Same-day and CMA F:P cutoffs at which a balance was reached between sensitivity and specificity ranged from 1.18 to 1.22; however, even at these values we found high rates of false positives and negatives (range: 31–39%). These results suggest that inline milk F:P data from inconsistently calibrated sensors should not be used alone to detect HYK in AMS herds.     

Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance.