The objective of this study was to determine characteristics and associations among bulk milk quality indicators from a cohort of dairies that used modern milk harvest, storage, and shipment systems and participated in an intensive program of milk quality monitoring. Bulk milk somatic cell count (SCC), total bacteria count (TBC), coliform count (CC), and laboratory pasteurization count (LPC) were monitored between July 2006 and July 2007. Bulk milk samples were collected 3 times daily (n = 3 farms), twice daily (n = 6 farms), once daily (n = 4 farms), or once every other day (n = 3 farms). Most farms (n = 11) had direct loading of milk into tankers on trucks, but 5 farms had stationary bulk tanks. The average herd size was 924 cows (range = 200 to 2,700), and daily milk produced per herd was 35,220 kg (range = 7,500 to 105,000 kg). Thresholds for increased bacterial counts were defined according to the 75th percentile and were >8,000 cfu/mL for TBC, >160 cfu/mL for CC, and >or=310 cfu/mL for LPC. Means values were 12,500 (n = 7,241 measurements), 242 (n = 7,275 measurements), and 226 cfu/mL (n = 7,220 measurements) for TBC, CC, and LPC, respectively. Increased TBC was 6.3 times more likely for bulk milk loads with increased CC compared with loads containing fewer coliforms. Increased TBC was 1.3 times more likely for bulk milk with increased LPC. The odds of increased TBC increased by 2.4% for every 10,000-cells/mL increase in SCC in the same milk load. The odds of increased CC increased by 4.3% for every 10,000-cells/mL increase in SCC. The odds of increased CC increased by 1% for every 0.1 degrees C increase in the milk temperature upon arrival at the dairy plant (or at pickup for farms with bulk tank). Laboratory pasteurization count was poorly associated with other milk quality indicators. Seasonal effects on bacterial counts and milk temperature varied substantially among farms. Results of this study can be used to aid the interpretation and analysis of indicators of milk quality intensively produced by dairy processors' laboratories.

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The objective of this study was to determine the risk of clinical mastitis in the first 120 d in lactation based on previous somatic cell count (SCC) history in a herd with a very low prevalence of contagious pathogens. A total of 218 cows from a university herd were enrolled at dry-off. Duplicate quarter milk samples were collected from all quarters at dry-off, postcalving (2 to 9 d in milk), and before treatment of all first cases of clinical mastitis that occurred during the first 120 d of the subsequent lactation. Quarter SCC statuses across the dry period were defined based on comparison of quarter SCC between the date of dry-off and the postcalving sampling periods. The relationship between the probability of developing clinical mastitis in the first 120 d of lactation and SCC status across the dry period and other explanatory variables was assessed using logistic regression. In the first 120 d postcalving, 68 first cases of clinical mastitis occurred in 47 cows. Of quarters that experienced a microbiologically positive clinical case, the same microorganism was never isolated from milk samples obtained at dry-off or consistently isolated from milk samples collected at all sampling periods. Coagulase negative staphylococci were the most prevalent pathogens isolated from subclinical intramammary infection, whereas gram-negative pathogens were the most common pathogen associated with clinical cases. Quarters that had at least 1 case of mastitis during the previous lactation were 4.2 times more likely to have a first case of clinical mastitis in the current lactation than quarters that did not have clinical mastitis in the previous lactation [odds ratio (OR) = 4.2 (1.8, 10.0)]. Quarters of cows of greater than fourth parity were 4.2 times more likely to have a first case of clinical mastitis than quarters of cows of second parity [OR = 4.2 (1.4, 10.0)]. Quarters with SCC > or =200,000 cells/mL at dry-off and postcalving were 2.7 times more likely to experience a first case of mastitis than quarters with SCC <200,000 cells/mL at both periods [OR = 2.7 (0.97, 7.67)].

The aim of this study was to obtain further knowledge on electrical conductivity (EC) of milk as a tool for detecting mastitis in goats. The effect of farm, parity, stage of lactation, and health status of the glands on EC, and the somatic cell count (SCC) of milk was analyzed. Additionally, relationships between EC and chemical composition and SCC were studied. Finally, characteristics of EC and SCC (sensitivity and specificity) as diagnostic tests used to detect mastitis were studied. One hundred and five Murciano-Granadina goats were enrolled in the study. Milk samples (by gland) were collected monthly for 7 mo on 3 farms in the southeastern Spain. To establish the health status, milk samples were aseptically collected before milking by gland. Foremilk (by gland) was collected to analyze EC, SCC, and chemical composition. Glands were classified according to the health status as free of mastitis, bacterial mastitis, or unspecific mastitis. The effects of farm, parity, and stage of lactation, as well as the interactions between health status and parity, parity and stage of lactation, and health status and stage of lactation were associated with EC. Changes in the milk's chemical composition (particularly of chloride ions) explained most of the variance in EC (R(2)=0.91). The strongest association between EC and SCC was found at SCC >2×10(6) cells/mL (r=0.42). The use of a single EC threshold for all animals and farms for detecting mastitis led to limited results for mastitis detection, which, in any case, favors negative predictive values over positive predictive values. This study revealed that factors, other than the health status, affecting EC hamper the use of an EC threshold for mastitis detection with sufficient specificity on all animals. Any detection system based on EC of milk should consider these factors, as well as specific variations for each of the animals.

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.

The objectives of this study were to estimate the prevalence and incidence of subclinical mastitis (SM) in a large population of Brazilian dairy herds and to describe how these indices changed over time. A data set comprising individual cow somatic cell counts (SCC) from 18,316 test days (TD) of 1,809 herds that participated in a Dairy Herd Improvement Association (DHIA) program between January 2011 and May 2015 was available for analysis. Only tests that had ≥10 lactating cows and that were performed at 30 ± 10-d intervals were used for analysis. The final data set included 8,285 TD from 517 herds located in 5 regions of the country. Prevalence (%) of SM was defined as the number of cows with SCC ≥200,000 cells/mL divided by the total number of tested cows on a given TD. The incidence of SM was defined as the number of cows whose SCC increased from <200,000 to ≥200,000 cells/mL over 2 consecutive TD divided by the sum of each cow's days at risk during this interval, expressed as new cases per cow month at risk. Prevalence and incidence of SM were compared among years, regions, herd size categories, and frequency of DHIA testing during the study period. The overall mean prevalence and incidence of SM including all tests performed during the study period was 46.4% and 0.17 new cases per cow month at risk, respectively. The prevalence of SM varied little from 2011 to 2015, and an increasing trend was observed over the years. Prevalence was lower in herds that performed ≥60 DHIA tests during the study period than in those that performed fewer tests and was not different among regions or herd size categories. Incidence of SM varied little over the years and was not different among the regions studied. Prevalence and incidence of SM in the 517 herds studied were high and did not improve over the years. These trends were observed across all herd size categories and regions studied. Producers who had more DHIA tests performed per herd during the study period had lower prevalence of SM. Results of this study highlight the need to establish large-scale milk quality programs in Brazil.

The role of particular virulence factors of Trueperella pyogenes that determine different pyogenic infections among domestic animals is poorly understood. Eight putative virulence genes and genotype profiles of 71 isolates were investigated among different clinical manifestations in domestic animals. The most common genes were plo (71/71 = 100·0%), fimA (70/71 = 98·6%), nanP (56/71 = 78·9%), fimE (53/71 = 74·6%), fimC (46/71 = 64·8%) and nanH (45/71 = 63·4%), whereas plo/fimA/fimE/fimC/nanH/nanP (17/71 = 23·9%), plo/fimA/fimE/nanH/nanP (13/71 = 18·3%), and plo/fimA/fimE/fimC/nanP (11/71 = 15·5%) were the most frequent genotypes. Studies involving virulence factors are critical in the investigation of molecular epidemiology, pathogenicity, and hypothetical differences in the virulence among T. pyogenes strains from different geographical areas.

a b s t r a c tThe primary objective of this study was to identify compost bedding characteristics associated with mastitis epidemiologic indexes, cow cleanliness, and concentration of selected bacterial populations found in bulk tank milk. Secondary objectives were to monitor the occurrence of environmental mastitis outbreaks, and to describe the profile of pathogens isolated from mastitis cases of cows housed in the CBP system. Three dairies were visited monthly during 1 year. On each visit day, milk samples were collected from the bulk tank and from a sample of mammary quarters for microbiological examination. Milk samples were collected from all cases of clinical mastitis. Flank, leg, udder, and teat cleanliness were assessed using a score chart based on a 4-point scale (1 ¼ clean to 4 ¼very dirty). Bedding samples were collected to estimate concentrations of total bacteria, streptococci, and coliforms, moisture, organic matter, carbon-nitrogen ratio, pH, and density. Mixed models were used to identify factors associated with incidence and prevalence of mastitis, and cow cleanliness. Except for farm A, on which contagious pathogens caused most cases, Escherichia coli, coagulase-negative staphylococci, and environmental streptococci were the most frequent pathogens isolated from clinical mastitis cases. Corynebacterium bovis was the most frequent pathogen isolated from subclinical cases of farms B (17.6) and C (26.0%). Environmental pathogens were isolated from 17.2%, 10.1%, and 14.8% of all subclinical cases of farms, A, B, and C, respectively. No outbreaks of environmental mastitis were observed during the course of the study. Bedding moisture, carbon-nitrogen ratio, pH, and dry density were unconditionally associated with the incidence of environmental clinical mastitis. Nonetheless, bedding moisture remained as a sole predictor in the final model. The odds of a case of environmental clinical mastitis increased 5.7% for each one-unit increase in bedding moisture. The odds of a new case of subclinical mastitis, and of a cow having SCC Z200,000 cells/mL increased 32% and 16% for each one-unit increase in leg cleanliness score, respectively. Overall means for udder, teat, flank, and leg hygiene scores were less than 2.1 for all farms and did not vary among seasons of the year. Bedding wet density was positively associated with all cleanliness scores and bulk milk concentration of total bacteria. Results suggest that managing bedding to remain dry and loose will result in cleaner animals with decreased risk of mastitis.