Guidelines for monitoring bulk tank milk somatic cell and bacterial counts

This study was conducted to establish guidelines for monitoring bulk tank milk somatic cell count and bacterial counts, and to understand the relationship between different bacterial groups that occur in bulk tank milk. One hundred twenty-six dairy farms in 14 counties of Pennsylvania participated, each providing one bulk tank milk sample every 15 d for 2 mo. The 4 bulk tank milk samples from each farm were examined for bulk tank somatic cell count and bacterial counts including standard plate count, preliminary incubation count, laboratory pasteurization count, coagulase-negative staphylococcal count, environmental streptococcal count, coliform count, and gram-negative noncoliform count. The milk samples were also examined for presence of Staphylococcus aureus, Streptococcus agalactiae, and Mycoplasma. The bacterial counts of 4 bulk tank milk samples examined over an 8-wk period were averaged and expressed as mean bacterial count per milliliter. The study revealed that an increase in the frequency of isolation of Staphylococcus aureus and Streptococcus agalactiae was significantly associated with an increased bulk tank somatic cell count. Paired correlation analysis showed that there was low correlation between different bacterial counts. Bulk tank milk with low (<5000 cfu/mL) standard plate count also had a significantly low level of mean bulk tank somatic cell count (<200,000 cells/mL), preliminary incubation count (<10,000 cfu/mL), laboratory pasteurization count (<100 cfu/mL), coagulase-negative staphylococci and environmental streptococcal counts (<500 cfu/mL), and noncoliform count (<200 cfu/mL). Coliform count was less likely to be associated with somatic cell or other bacterial counts. Herd size and farm management practices had considerable influence on somatic cell and bacterial counts in bulk tank milk. Dairy herds that used automatic milking detachers, sand as bedding material, dip cups for teat dipping instead of spraying, and practiced pre-and postdipping had significantly lower bulk tank somatic cell and/or bacterial counts. In conclusion, categorized bulk tank somatic cell and bacterial counts could serve as indicators and facilitate monitoring of herd udder health and milk quality.     

Short communication: bulk tank total bacterial count in dairy sheep: factors of variation and relationship with somatic cell count

A total of 9,353 records for bulk tank total bacterial count (TBC) were obtained over 1 yr from 315 dairy ewe flocks belonging to the Sheep Improvement Consortium (CPO) in Castilla-León (Spain). Analysis of variance showed significant effects of flock, breed, month within flock, dry therapy, milking type and installation, and logSCC on logTBC. Flock and month within flock were important variation factors as they accounted for 22.0 and 22.1% of the variance, respectively. Considerable repeatability values were obtained for both random factors. Hand milking and bucket-milking machines elicited highest logTBC (5.31), whereas parlor systems with looped milkline (5.01) elicited the lowest logTBC. The implementation of dry therapy practice (5.12) showed significantly lower logTBC than when not used (5.25). Variability in logTBC among breeds ranged from 5.24 (Awassi) to 5.07 (Churra). However, clinical outbreaks of contagious agalactia did not increase TBC significantly. A statistically significant relationship was found between logTBC and logSCC, the correlation coefficient between the variables being r = 0.23. Programs for improving milk hygiene should be implemented for both total bacterial count and somatic cell count variables at the same time.     

The microbiome of Escherichia coli and culture-negative nonsevere clinical mastitis: Characterization and associations with linear score and milk production

Culture-negative and Escherichia coli cases are uncommonly treated in pathogen-based protocols for nonsevere mastitis. High-throughput 16S rRNA sequencing might reveal the presence of other pathogens and can provide information on microbial diversity. The objective was to explore the milk microbiome at the time of the mastitis event (enrollment) and its association with survival in the herd, milk production, and postevent linear score (LS) for cows with clinical mastitis characterized as negative or E. coli by culture. Fifty E. coli-positive and 35 culture-negative samples from cases were enrolled. No cases were treated with antimicrobials. All E. coli-positive quarters were characterized as transient; microbiological culture of samples taken 15 d postmastitis were negative for this organism. However, a difference in α-diversity (Shannon index) was present between enrollment and follow-up samples (3.8 vs. 5.1). When α-diversity was explored for enrollment E. coli samples, no relationship was observed between the Shannon indices of these samples and postmastitis LS. Alpha-diversity of the enrollment samples was lower for E. coli-positive cows that subsequently had greater losses in milk production. This difference was explained by a greater relative abundance of the family Enterobacteriaceae (67.8 vs. 38.4%) for cows that dropped in production. Analysis of composition of the microbiome identified one phylum, Proteobacteria, that differed between E. coli-positive cows that dropped in production and those that did not. Evaluation of β -diversity found no statistical relationship between postmastitis LS and the microbiome. When evaluating α- and β-diversities and composition of the microbiomes for culture-negative quarters, no associations were found for milk production changes and postmastitis LS. Three cows did not remain in the herd, limiting the ability to analyze survival. The findings suggest that a contributing factor to negative outcomes in E. coli-positive cows is relative abundance of this pathogen, and that no single or collective group of bacterial families is associated with milk production changes or postmastitis LS in culture-negative quarters. Although additional studies should be performed, the absence of associations between outcomes explored and microbial profiles in this study suggests that we are not missing opportunities by not treating nonsevere E. coli or culture-negative mastitis cases.     

Optimally achieving milk bulk tank somatic cell count thresholds

High somatic cell count in milk leads to reduced shelf life in fluid milk and lower processed yields in manufactured dairy products. As a result, farmers are often penalized for high bulk tank somatic cell count or paid a premium for low bulk tank somatic cell count. Many countries also require all milk from a farm to be lower than a specified regulated somatic cell count. Thus, farms often cull cows that have high somatic cell count to meet somatic cell count thresholds. Rather than naïvely cull the highest somatic cell count cows, a mathematical programming model was developed that determines the cows to be culled from the herd by maximizing the net present value of the herd, subject to meeting any specified bulk tank somatic cell count level. The model was applied to test-day cows on 2 New York State dairy farms. Results showed that the net present value of the herd was increased by using the model to meet the somatic cell count restriction compared with naïvely culling the highest somatic cell count cows. Implementation of the model would be straightforward in dairy management decision software.     

Short communication: Repeatability of differential goat bulk milk culture and associations with somatic cell count, total bacterial count, and standard plate count

The aims of this study were to assess how different bacterial groups in bulk milk are related to bulk milk somatic cell count (SCC), bulk milk total bacterial count (TBC), and bulk milk standard plate count (SPC) and to measure the repeatability of bulk milk culturing. On 53 Dutch dairy goat farms, 3 bulk milk samples were collected at intervals of 2 wk. The samples were cultured for SPC, coliform count, and staphylococcal count and for the presence of Staphylococcus aureus. Furthermore, SCC (Fossomatic 5000, Foss, Hillerød, Denmark) and TBC (BactoScan FC 150, Foss) were measured. Staphylococcal count was correlated to SCC (r = 0.40), TBC (r = 0.51), and SPC (r = 0.53). Coliform count was correlated to TBC (r = 0.33), but not to any of the other variables. Staphylococcus aureus did not correlate to SCC. The contribution of the staphylococcal count to the SPC was 31%, whereas the coliform count comprised only 1% of the SPC. The agreement of the repeated measurements was low. This study indicates that staphylococci in goat bulk milk are related to SCC and make a significant contribution to SPC. Because of the high variation in bacterial counts, repeated sampling is necessary to draw valid conclusions from bulk milk culturing.     

A cohort study of the effect of Streptococcus agalactiae on milk yield and somatic cell count in Norwegian dairy cows

The primary objective of the present study was to estimate the effect of Streptococcus agalactiae intramammary infection on milk production and somatic cell count (SCC) in Norwegian dairy cows. A secondary objective was to assess differences in the effect of common Strep. agalactiae sequence types (ST) found in Norwegian dairy herds. We performed a cohort study combining registry data with sequence-type data from Strep. agalactiae isolates. Herds in which Strep. agalactiae had been detected in individual animals (bacteriological culture or quantitative PCR) between 2012 and 2015 were included. We accessed monthly test-day milk yield records for the entire period to compare milk yield and SCC between cows that were Strep. agalactiae positive and all other cows, within each herd. The study sample consisted of 150 herds, 15,757 cows, 30,850 lactations, and 204,126 test days. We evaluated the effects of Strep. agalactiae on test-day milk yield and SCC using mixed linear regression models, controlling for clustering by herd, cow, and lactation. Multilocus sequence typing of Strep. agalactiae was available for isolates from 86 herds. Additional models were fit to a subset of herds (n = 59) in which ST1, ST23, ST103, and ST196 had been found, to compare the effects of ST on milk production and SCC. In the period 3 to 2 mo before diagnosis, Strep. agalactiae-positive cows produced an average of 1.3 kg more DIM-adjusted milk/d than their negative herd mates. At the time of diagnosis, production was on average 0.13 kg less DIM-adjusted milk/d in Strep. agalactiae-positive cows than in negative cows; 2 to 3 mo after diagnosis, they produced 1.24 kg less DIM-adjusted milk/d than negative cows. Losses persisted for the rest of the investigated period. Cows with ST23, ST103, and ST196 followed a similar pattern as the overall analysis with respect to milk production, whereas ST1-affected cows produced similar amounts of milk before diagnosis as the negative cows. Cows with ST1 experienced the largest milk loss 1 to 2 mo after diagnosis but then recovered to some extent; for cows with ST103, the severe milk loss persisted for the rest of the investigation period. The cow-associated ST103 elicited a lower response in peak SCC compared with ST23, ST103, and ST196. The results indicate an effect of Strep. agalactiae on milk production and SCC. Production was lowest 2 to 3 mo after a positive sample. Peak SCC was reached the month before diagnosis, with notable differences between sequence types.     

Factors of variation influencing bulk tank somatic cell count in dairy sheep

Between January and December 2002, a total of 21,685 records for bulk tank milk somatic cell count (BTSCC) were obtained from 309 dairy ewe herds belonging to the Sheep Improvement Consortium in Castilla-Leon, Spain. Based on the first statistical model, ANOVA detected significant effects of herd, breed, month within herd, dry therapy, type of milking, contagious agalactia, and installations within machine milking on logBTSCC. A second statistical model was used on herds with machine milking to study the effect of the vacuum level and pulsation rate on BTSCC. Herd and month within herd were important variation factors as they explained 48.4 and 16.1% of the variance in BTSCC. Variability in logBTSCC among breeds ranged from 5.84 (Castellana) to 6.09 (Awassi and Spanish Assaf). Implementing dry-ewe therapy (5.91) significantly reduced logBTSCC compared with when it was not implemented (6.10). Hand milking elicited greater logBTSCC (6.07) than machine milking (5.94). Machine milking of ewes in milking parlors (logBTSCC: 5.88 to 5.94) was associated with better udder health than was the use of bucket-milking machines (6.04). Reduced vacuum levels and elevated pulsation rate during machine milking optimized BTSCC. In all cases, clinical outbreaks of contagious agalactia increased BTSCC. As a result, dry therapy was proposed as the main tool to reduce BTSCC. Optimization of milking-machine standards and parlor systems also improved udder health in dairy sheep.     

Consequence of alternative standards for bulk tank somatic cell count of dairy herds in the United States

Noncompliance with current US and European Union (EU) standards for bulk-tank somatic cell count (BTSCC) as well as BTSCC standards recently proposed by 3 US organizations was evaluated using US Dairy Herd Improvement Association (DHI) herds and herds supplying milk to 4 Federal Milk Marketing Orders (FMO). Herds with 15 to 26 tests (frequently monthly) from January 2009 through October 2010 were included. Somatic cell scores (SCS) from 14,854 herds and 164,794 herd-tests were analyzed for DHI herds with ≥10 cows for all tests. Herd test-day SCC was derived as a proxy for BTSCC and was the basis for determining noncompliance and percentage of the milk it represented. For FMO herds, actual milk marketed and BTSCC were available from 27,759 herds and 325,690 herd-tests. A herd was noncompliant for the current EU BTSCC standard after 4 consecutive rolling 3-test geometric means (geometric method) were >400,000 cells/mL. A herd was noncompliant for the current US BTSCC standard after 3 of 5 consecutive monthly BTSCC shipments (frequency method) were >750,000 cells/mL. Alternative proposed standards (600,000, 500,000, or 400,000 cells/mL) also were examined. A third method designated noncompliance when a single 3-mo geometric mean of >550,000 or >400,000 cells/mL and a subsequent test exceeded the same level. Results were examined based on herd size or milk shipped by month. Noncompliance for the current US standard for the 12 mo ending October 2010 in DHI and FMO herds was 0.9 and 1.0%, respectively, compared with 7.8 and 16.1% for the current EU standard. Noncompliance was always greater for the frequency method than for the geometric method and was inversely related to herd size or milk shipped. Using the frequency method at 400,000 cells/mL, noncompliance was 19.1% for DHI herd-tests in herds with <50 cows compared with 1.1% for herds with ≥1,000 cows. For FMO herds shipping <900 t, noncompliance was 44.5% using the frequency method at 400,000 cells/mL compared with 8.0% for herds marketing >9,000 t. All methods proposed increased the percentages of herds and shipped milk that exceeded the regulatory limit. Producers will need to place more emphasis on reducing the incidence and prevalence of subclinical mastitis through known management practices such as proper milking techniques, well-functioning milking machines, postmilking teat disinfectant, dry cow treatment, and culling of problem cows to meet any of the proposed new standards.     

Impact of somatic cell count combined with differential somatic cell count on milk protein fractions in Holstein cattle

Udder health in dairy herds is a very important issue given its implications for animal welfare and the production of high-quality milk. Somatic cell count (SCC) is the most widely used means of assessing udder health status. However, differential somatic cell count (DSCC) has recently been proposed as a new and more effective means of evaluating intramammary infection dynamics. Differential SCC represents the combined percentage of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) in the total SCC, with macrophages (MAC) accounting for the remaining proportion. The aim of this study was to evaluate the association between SCC and DSCC and the detailed milk protein profile in a population of 1,482 Holstein cows. A validated reversed-phase HPLC method was used to quantify 4 caseins (CN), namely α_S1-CN, α_S2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin, which were expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, %N). A linear mixed model was fitted to explore the associations between somatic cell score (SCS) combined with DSCC and the protein fractions expressed quantitatively and qualitatively. We ran an additional model that included DSCC expressed as PMN-LYM and MAC counts, obtained by multiplying the percentages of PMN-LYM and MAC by SCC for each cow in the data set. When the protein fractions were expressed as grams per liter, SCS was significantly negatively associated with almost all the casein fractions and positively associated with the whey protein α-lactalbumin, while DSCC was significantly associated with α_S1-CN, β-CN, and α-lactalbumin, but in the opposite direction to SCS. We observed the same pattern with the qualitative data (i.e., %N), confirming opposite effects of SCS and DSCC on milk protein fractions. The PMN-LYM count was only slightly associated with the traits of concern, although the pattern observed was the same as when both SCS and DSCC were included in the model. The MAC count, however, generally had a greater impact on many casein fractions, in particular decreasing both β-CN content (g/L) and proportion (%N), and exhibited the opposite pattern to the PMN-LYM count. Our results show that information obtained from both SCS and DSCC may be useful in assessing milk quality and protein fractions. They also demonstrate the potential of MAC count as a novel udder health trait.     

Contagious agalactia monitoring in caprine herds through regular bulk tank milk sampling

Surveillance and control of Mycoplasma spp. responsible for contagious agalactia (CA) in caprine herds are important challenges in countries with a large small-ruminant dairy industry. In the absence of any clinical signs, being able to determine the potential circulation of mycoplasmas within a herd could help to prevent biosecurity issues during animal exchanges between farms and improve health management practices. The objective of this study was to determine whether regular sampling of bulk tank milk was suitable for such surveillance. Twenty farms were sampled once a month for 2 yr and CA-responsible mycoplasmas were detected by real-time PCR on DNA extracted from milk, using 3 different DNA extraction methods. The pattern of mycoplasma excretion in bulk tank milk was assessed over time and several herd characteristics were recorded together with any event occurring within the herds. In general, the results obtained with the different detection methods were comparable and mainly agreed with the culture results. Several patterns of excretion were observed but were not related to herd characteristics (size, breed, and so on). Recurrence of the same (sub)species and same pulsed-field gel electrophoresis subtype during the 2-yr period is indicative of the considerable persistence of mycoplasmas. This persistence was associated with intermittent excretion. In conclusion, bulk tank milk sampling could be valuable for controlling CA in caprine herds provided it is repeated several times, yet to be defined, per year and analyzed using an appropriate methodology and the right cut-off for interpretation.     

Correlation between standard plate count and somatic cell count milk quality results for Wisconsin dairy producers

The objective of this study was to determine if a correlation exists between standard plate count (SPC) and somatic cell count (SCC) monthly reported results for Wisconsin dairy producers. Such a correlation may indicate that Wisconsin producers effectively controlling sanitation and milk temperature (reflected in low SPC) also have implemented good herd health management practices (reflected in low SCC). The SPC and SCC results for all grade A and B dairy producers who submitted results to the Wisconsin Department of Agriculture, Trade, and Consumer Protection, in each month of 2012 were analyzed. Grade A producer SPC results were less dispersed than grade B producer SPC results. Regression analysis showed a highly significant correlation between SPC and SCC, but the R² value was very small (0.02–0.03), suggesting that many other factors, besides SCC, influence SPC. Average SCC (across 12 mo) for grade A and B producers decreased with an increase in the number of monthly SPC results (out of 12) that were ≤25,000 cfu/mL. A chi-squared test of independence showed that the proportion of monthly SCC results >250,000 cells/mL varied significantly depending on whether the corresponding SPC result was ≤25,000 or >25,000 cfu/mL. This significant difference occurred in all months of 2012 for grade A and B producers. The results suggest that a generally consistent level of skill exists across dairy production practices affecting SPC and SCC.     

Quantitatively determining the total bacterial count of raw goat milk using dielectric spectra

The objective of this study was to evaluate dielectric spectra as a means of quantitatively determining total bacterial count (TBC) of raw goat milk. The dielectric spectra, including dielectric constant (ε′) spectra and dielectric loss factor (ε″) spectra, and TBC of 154 raw goat milk samples were measured using network analyzer and plate count methods, respectively. Owing to the poor linear relationship between TBC in logarithm and permittivities at a single frequency, chemometrics was used to reduce noise, identify outliers, select effective variables, and divide sample sets. Several linear models, such as multiple linear regression, ridge regression, and least absolute shrinkage and selection operator, were established to determine TBC based on the effective spectra of ε′, ε″, and their combination (ε′+ε″). The results indicated that the models built using the spectra of ε′+ε″ and ε′ had excellent TBC prediction performance. The best model was multiple linear regression based on ε′+ε″ spectra with the residual predictive deviation of 3.26. This study shows that the dielectric spectra had great potential to quantitatively and rapidly determine TBC of raw milk.     

Factors associated with coliform count in unpasteurized bulk milk

The objective of this study was to identify factors associated with bulk milk coliform count (CC). Dairy farms (n = 10) were visited once weekly on sequential weekdays over a period of 10 wk. During each visit, in-line drip samplers were used to collect 1 milk sample from 2 points of the milk line (between the receiver jar and milk filters, and after the plate cooler). During the same period that in-line milk samples were collected, university personnel observed milking performance and hygiene and collected liner (n = 40) and teat skin swabs (n = 40). Coliform counts were determined for milk samples and swabs using Petrifilm CC plates (3M, St. Paul, MN). A mixed model was used to assess the association between in-line milk CC (ILCC) and several potential predictor variables. The mean duration of each visit was 73 min and the time between start of milking and beginning of milk sampling was 154 min. The mean number of cows milked during each visit was 236. For all milk samples (n = 181), geometric mean ILCC was 37 cfu/mL. In-line milk CC varied by farm, ranging from 5 to 1,198 cfu/mL. Rate of fall-offs, rate of cluster washes, outdoor and indoor temperature, indoor humidity, sampling duration, and parity group were unconditionally associated with ILCC but did not enter the final multivariate model. In-line milk CC was 4 times greater (115 cfu/mL) when milking machine wash failures occurred compared with ILCC after normal washes (26 cfu/mL). Pre-filter and post-cooler ILCC were not different when milk samples were collected at the beginning (<33% of herd milked) or at mid-milking (33 to 66% of the herd milked), whereas pre-filter ILCC was less than post-cooler for samples collected at the end of milking (>67% of the herd milked). Geometric mean ILCC (cfu/mL) increased 6.3% for every 10% increase in in-line milk SCC (cells/mL). Geometric mean ILCC increased 2.3% for every 10% increase in liner CC (cfu/mL). Results of this study provide novel information about farm factors associated with CC, as estimated in milk before storage in tankers or bulk tanks, and highlight the importance of proper and consistent milking machine washes in minimizing bulk milk coliform contamination. The nature of the associations between liner CC, rate of cluster washes, rate of milking units fall-offs, and ILCC indicates that managing and monitoring such events has the potential for improving bacteriological quality of farm bulk milk.     

Prevalence of Salmonella enterica,Listeria monocytogenes,and pathogenic Escherichia coli in bulk tank milk and milk filters from US dairy operations in the National Animal Health Monitoring System Dairy 2014 study

The dairy farm environment is a well-documented reservoir for zoonotic pathogens such as Salmonella enterica, Shiga-toxigenic Escherichia coli, and Listeria monocytogenes, and humans may be exposed to these pathogens via consumption of unpasteurized milk and dairy products. As part of the National Animal Health Monitoring System Dairy 2014 study, bulk tank milk (BTM, n = 234) and milk filters (n = 254) were collected from a total of 234 dairy operations in 17 major dairy states and analyzed for the presence of these pathogens. The invA gene was detected in samples from 18.5% of operations and Salmonella enterica was isolated from 18.0% of operations. Salmonella Dublin was detected in 0.7% of operations. Sixteen Salmonella serotypes were isolated, and the most common serotypes were Cerro, Montevideo, and Newport. Representative Salmonella isolates (n = 137) were tested against a panel of 14 antimicrobials. Most (85%) were pansusceptible; the remaining were resistant to 1 to 9 antimicrobials, and within the resistant strains the most common profile was resistance to ampicillin/clavulanic acid, ampicillin, cefoxitin, ceftiofur, ceftriaxone, chloramphenicol, streptomycin, sulfisoxazole, and tetracycline. Listeria spp. were isolated from 19.9% of operations, and L. monocytogenes was isolated from 3.0% of operations. Serogroups 1/2a and 1/2b were the most common, followed by 4b and 4a. One or more E. coli virulence genes were detected in the BTM from 30.5% of operations and in the filters from 75.3% of operations. A combination of stx₂, eaeA, and γ-tir genes was detected in the BTM from 0.5% of operations and in the filters from 6.6% of operations. The results of this study indicate an appreciable prevalence of bacterial pathogens in BTM and filters, including serovars known to infect humans.     

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.     

Characterization of the microbiota and resistome of bulk tank milk samples from Prince Edward Island dairy farms

Bulk tank milk (BTM) is regularly used for surveillance on dairy farms for disease conditions such as mastitis and Johne's disease. In this study, we used 16S rRNA sequencing and bait-capture enrichment to characterize the microbiota and resistome of BTM, and investigate potential differences between the cream or pellet fractions. A total of 12 BTM samples were taken from 12 Prince Edward Island dairy farms, in Atlantic Canada, in duplicates. The DNA was analyzed by high-throughput sequencing of the 16S rRNA gene and a suite of antimicrobial resistance (AMR) genes. Target-capture enrichment of AMR genes was conducted before shotgun sequencing. The bioinformatics pipelines QIIME 2 and AMR++ were used for microbiota and resistome analysis, respectively. Differences between microbiotae were evaluated qualitatively with nonmetric multidimensional scaling and quantitatively with permutational ANOVA of UniFrac distances. A total of 47 phyla were present across the BTM samples. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were the most abundant phyla. At the genus level, Corynebacterium, Acinetobacter, Lactobacillus, and Turicibacter were the most abundant. There was no significant difference in the Faith's phylogenetic diversity between the cream and pellet fraction. Faith's phylogenetic diversity differed marginally by stall type. There were 10,217 hits across 80 unique AMR genes, with tetracycline resistance being the most common class.     

Effect of intramammary infection with non-aureus staphylococci in early lactation in dairy heifers on quarter somatic cell count and quarter milk yield during the first 4 months of lactation

A longitudinal study was conducted to assess to what extent intramammary infection (IMI) with non-aureus staphylococci (NAS) within the first 4 d after calving in dairy heifers affects quarter milk yield (qMY) and quarter milk somatic cell count (qSCC) during the first 4 mo of lactation. In total, 324 quarters from 82 Holstein Friesian heifers from 3 commercial dairy herds equipped with an automatic milking system were included and followed from calving up to 4 mo in lactation. The automatic milking system allowed us to precisely determine the daily qMY. A milk sample from each quarter was collected in early lactation (between 1 and 4 d in milk) for bacteriological culturing and measurement of the qSCC. Subsequently, milk samples were taken on a biweekly basis for measurement of the qSCC. The milk prolactin level in early lactation was measured, and the relation with NAS IMI was determined. Overall, NAS IMI in early lactation caused only a slight but significant increase in qSCC compared with milk from noninfected quarters during the first 4 mo in lactation, whereas no significant difference in daily qMY was present between NAS-infected and noninfected quarters. The milk prolactin level in early lactation did not differ between NAS-infected and noninfected quarters either. Our data suggest that IMI with NAS (as a group) present shortly after calving do not have an adverse effect on later production. The milk prolactin concentrations were not dissimilar between NAS-infected and noninfected quarters and thus cannot explain why NAS-infected quarters do not produce less than noninfected quarters.     

Bacterial spore levels in bulk tank raw milk are influenced by environmental and cow hygiene factors

Sporeforming bacteria are responsible for the spoilage of several dairy products including fluid milk, cheese, and products manufactured using dried dairy powders as ingredients. Sporeforming bacteria represent a considerable challenge for the dairy industry because they primarily enter the dairy product continuum at the farm, survive processing hurdles, and subsequently grow in finished products. As such, strategies to reduce spoilage due to this group of bacterial contaminants have focused on understanding the effect of farm level factors on the presence of spores in bulk tank raw milk with the goal of reducing spore levels in raw milk, as well as understanding processing contributions to spore levels and outgrowth in finished products. The goal of the current study was to investigate sources of spores in the farm environment and survey farm management practices to identify variables using multimodel inference, a model averaging approach that eliminates the uncertainty of traditional model selection approaches, that affect the presence and levels of spores in bulk tank raw milk. To this end, environmental samples including feed, bedding, manure, soil, water, and so on, and bulk tank raw milk were collected twice from 17 upstate New York dairy farms over a 19-mo period and the presence and levels of various spore types (e.g., psychrotolerant, mesophilic, thermophilic, highly heat resistant thermophilic, specially thermoresistant thermophilic, and anaerobic butyric acid bacteria) were assessed. Manure had the highest level of spores for 4 out of 5 aerobic spore types with mean counts of 5.87, 5.22, 4.35, and 3.68 log cfu/g of mesophilic, thermophilic, highly heat resistant thermophilic, and specially thermoresistant thermophilic spores, respectively. In contrast, bulk tank raw milk had mean spore levels below 1 log cfu/mL across spore types. Multimodel inference was used to determine variables (i.e., management factors, environmental spore levels, and meteorological data from each sampling) that were important for presence or levels of each spore type in bulk tank raw milk. Analyses indicated that variables of importance for more than one spore type included the residual level of spores in milk from individual cows after thorough teat cleaning and forestripping, udder hygiene, clipping or flaming of udders, spore level in feed commodities, spore level in parlor air, how often bedding was topped up or changed, the use of recycled manure bedding, and the use of sawdust bedding. These results improve our understanding of how spores transfer from environmental sources into bulk tank raw milk and provide information that can be used to design intervention trials aimed at reducing spore levels in raw milk.     

Divergent selection on milk somatic cell count in goats improves udder health and milk quality with no effect on nematode resistance

Milk somatic cell count (SCC) is commonly higher in goats than in cattle and sheep. Furthermore, the ability of milk SCC to predict mastitis is considered lower in goats than in cattle and sheep, and the relevance of somatic cell score (SCS)-based selection in this species has been questioned. To address this issue, we created 2 divergent lines of Alpine goats using artificially inseminated bucks with extreme estimated breeding values for SCS. A total of 287 goats, 158 in high- and 129 in low-SCS lines, were scrutinized for mastitis infections. We subjected 2,688 milk samples to conventional bacteriological analyses on agarose and bacterial counts were estimated for positive samples. The SCS, milk yield, fat content, and protein content were recorded every 3 wk. Clinical mastitis was systematically noted. A subset of 40 goats (20 from each line) was subsequently challenged with Haemonchus contortus and monitored for anemia (blood packed cell volume) and fecal egg counts to see if SCS-based selection had an indirect effect on resistance to gastrointestinal nematodes. Milk production traits, including milk quantity, fat content, and protein content, were similar in both goat lines. In contrast, the raw milk SCC almost doubled between the lines, with 1,542,000 versus 855,000 cells/mL in the high- and low-SCS lines, respectively. The difference in breeding value for SCS between lines was 1.65 genetic standard deviation equivalents. The Staphylococcus spp. most frequently isolated from milk were S. xylosus, S. caprae, S. epidermidis, and S. aureus. The frequency of positive bacteriology samples was significantly higher in the high-SCS line (49%) than in the low-SCS line (33%). The highest odds ratio was 3.49 (95% confidence interval: 11.95–6.25) for S. aureus. The distribution of bacterial species in positive samples between lines was comparable. The average quantity of bacteria in positive samples was also significantly higher in high-SCS goats (69 ± 80 growing colonies) than in low-SCS goats (38 ± 62 growing colonies). Clinical cases were rare and equally distributed between high- (n = 4; 2.5%) and low-SCS (n = 3; 2.3%) lines. Furthermore, the larger the amounts of bacteria in milk the higher the SCS level. Conversely, goats with repeatedly culture-negative udders exhibited the lowest SCC levels, with an average of below 300,000 cells/mL. We therefore confirmed that SCS is a relevant predictor of intramammary infection and hygienic quality of milk in goats and can be used for prophylactic purposes. After challenge with H. contortus, goats were anemic with high fecal egg counts but we found no difference between the genetic lines. This result provides initial evidence that resistance to mastitis or to gastrointestinal nematodes infections is under independent genetic regulation. Altogether, this monitoring of the goat lines indicated that SCS-based selection helps to improve udder health by decreasing milk cell counts and reducing the incidence of infection and related bacterial shedding in milk. Selection for low SCC should not affect a goat's ability to cope with gastrointestinal nematodes.     

Interaction of somatic cell count and quarter milk flow patterns

Milk flow parameters at udder and quarter levels were studied in relation to somatic cell count (SCC) and other risk factors for mastitis (bimodality, duration of decline, and duration of overmilking phase). Thirty-eight Holstein cows in their first to sixth lactations were investigated during 10 mo of lactation. Monthly milk samples were collected for SCC during morning milking. Quarter and udder milk flows were recorded daily. A cow was included if one quarter was found to have an SCC higher than 200 × 10³ cells/mL. A total of 3,262 quarter milk flow curves and 804 udder milk flow curves from 22 cows (6 primiparous and 16 multiparous) were selected and evaluated. Selected data for milk flow profiles in relation to SCC represented 5 consecutive morning milkings around the time of milk sampling (sampling on d 3). A total of 661 milk samples were analyzed. At both the udder and quarter levels milk yield was reduced in groups with increased SCC. Quarters with high SCC (>500 × 10³ cells/mL) had lower peak flow rate and longer overmilking phases compared with quarters with low SCC (<200 × 10³ cells/mL). There was a tendency for a longer duration of the decline phase in quarters with high SCC but no effect was observed at the udder level. There were longer declines in bimodal milk flows at the quarter, but not at the udder, level. Also, quarters with bimodality had longer overmilking phases. The duration of the decline phases at the quarter level influenced all measured parameters except the duration of the increase phase. The quarters with a longer duration of the decline phase (≥80 s) had greater SCC and peak flow rate but had lower milk yield compared with quarters with a shorter duration of the decline phase (<27 s). Duration of the overmilking phase influenced all measured parameters except SCC. We conclude that for good udder health, the duration of the decline phase at the quarter level should be considered for milking parameters and udder preparation before milking.