Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry provides accurate species-level identification of many, microorganisms retrieved from bovine milk samples. However, not all those microorganisms are pathogenic. Our study aimed to: (1) determine the species-specific prevalence of microorganisms identified in bovine milk of apparently healthy lactating quarters vs. quarters with clinical mastitis (CM); and (2) map current information and knowledge gaps on udder health relevance of microorganisms retrieved from bovine milk samples. A mixed study design (meta-analysis and mapping review) was chosen. We gathered several large Canadian, US and Brazilian data sets of MALDI-TOF results for organisms cultured from quarter milk samples. For meta-analysis, two datasets (apparently healthy quarters vs. CM samples) were organized. A series of meta-analyses was conducted to determine microorganisms’ prevalence. Then, each species reported was searched through PubMed to investigate whether inflammation (increased somatic cell count (SCC) or signs of CM) was associated with microorganism’s recovery from milk. A total of 294 different species of microorganisms recovered from milk samples were identified. Among 50,429 quarter-milk samples from apparently healthy quarters, the 5 most frequent species were Staphylococcus chromogenes (6.7%, 95% CI 4.5–9.2%), Aerococcus viridans (1.6%, 95% CI 0.4–3.5%), Staphylococcus aureus (1.5%, 95% CI 0.5–2.8%), Staphylococcus haemolyticus (0.9%, 95% CI 0.4–1.5%), and Staphylococcus epidermidis (0.7%, 95% CI 0.2–1.6%). Among the 43,924 quarter-milk CM samples, the 5 most frequent species were Escherichia coli (11%, 95% CI 8.1–14.3%), Streptococcus uberis (8.5%, 95% CI 5.3–12.2%), Streptococcus dysgalactiae (7.8%, 95% CI 4.9–11.5%), Staphylococcus aureus (7.8%, 95% CI 4.4–11.9%), and Klebsiella pneumoniae (5.6%, 95% CI 3.4–8.2%). When conducting the PubMed literature search, there were 206 species identified by MALDI-TOF for which we were not able to find any information regarding their association with CM or SCC. Some of them, however, were frequently isolated in our multi-country dataset from the milk of quarters with CM (e.g., Citrobacter koseri, Enterococcus saccharolyticus, Streptococcus gallolyticus). Our study provides guidance to veterinarians for interpretation of milk bacteriology results obtained using MALDI-TOF and identifies knowledge gaps for future research.

Milk production may be reduced before dry-off to decrease the risk of cows developing intramammary infections during the dry period. Such reductions in milk may be possible in automated milking systems (AMS) where milking frequency and feed allocation at the AMS can be controlled at the cow level. This study investigated the effect of dry-off management of cows milked in AMS on milk yield, milking behavior, and somatic cell count (SCC). Using a 2 × 2 factorial arrangement of treatments, applied from d 14 to 1 before dry-off, 445 cows from 5 commercial dairy farms in Quebec, Canada, were assigned within farm to either (1) reduced feed [RF; allowed a maximum of 0.75 kg/d of AMS pellet for the first week (14 to 8 d before dry-off) of treatment, and 0.50 kg/d for the second week (7 to 1 d before dry-off) of treatment], or (2) nonreduced feed (NF; allowed up to 2 kg/d of AMS pellet), and either (1) reduced milking (RM; reduced to 2 milkings/d or as many times as required to yield 17 kg/milking), or (2) nonreduced milking (NM; allowed up to 6 AMS milkings/d) and no maximum production. Feed and milking behavior data, as well as milk yield and SCC were collected from the AMS software. The RF cows had lower AMS feed delivered during the treatment period, as per the experimental design. Across the treatment period, the NF-NM cows had the highest milking frequency (2.7 times/d), followed by the RF-NM cows (2.4 times/d), and then both of the RM groups (1.8 times/d), which did not differ from each other. All cows, except the NF-NM cows, were gradually milked less frequently as dry-off approached. Across the entire 2-wk treatment period before dry-off, cows with RM allowance experienced a higher reduction in milk yield compared with the cows with no milking allowance restrictions (-4.8 vs. -3.6 kg). Similarly, cows with a RF allocation tended to have a higher reduction in milk yield than cows with NF (-4.6 vs. -3.7 kg). As result, those cows with both reduced milking permissions and feed allocation at the AMS experienced the greatest drop in milk production before dry-off. There were no differences between treatments for milking frequency or yield in the next lactation. Somatic cell score (calculated from SCC) was not different between treatments in the 2-wk or day before dry-off, nor in the first month after calving. Overall, these data suggest that reducing both milking frequency and feed quantity in the AMS is the most efficient method to decrease milk yield before dry-off, without negatively influencing milking frequency or yield in the next lactation, as well as without affecting milk quality.

Mastitis is the most common disease affecting dairy cattle and is associated with substantial milk loss. Somatic cell count (SCC) has been widely used as an indicator of udder inflammation (e.g., subclinical mastitis). More recently, differential somatic cell count (DSCC) has become available as an auxiliary tool for milk quality control, with the potential to indicate different stages of inflammation when combined with SCC. This paper aimed to investigate the association of somatic cell score (SCS) and DSCC with milk yield and milk components at the quarter-level. A convenience sample of 5 dairy herds using an automated milking system (AMS) was selected and visited every other week for milk sample collection. Fat, protein, and lactose content were analyzed at Lactanet (Canadian Network for Dairy Excellence, Sainte-Anne-de-Bellevue, Quebec, Canada) by mid-infrared spectroscopy using a MilkoScan FT6000. Determination of SCC and DSCC was also performed by Lactanet using a CombiFoss 7 DC instrument. Milk yield data was retrieved from the AMS. Given the hierarchical structure of the data, a linear mixed model was built with either milk yield or milk components as the outcomes. The results showed that elevated somatic cell score (SCS), in combination with lower proportions of DSCC, was associated with the highest milk loss for primiparous and multiparous cows. For instance, the estimated milk loss for a quarter with a SCS of 7 and a DSCC of 55% was 1.45 kg/d compared with a quarter with a SCS of 2 and DSCC of 65% in multiparous cows. The association was similar when the outcome was the lactose content. Quarters with elevated SCS and lower DSCC had the lowest lactose percentage. No significant changes in fat content were observed across different SCS levels in multiparous cows, and quarters with higher DSCC had the lowest fat percentage in primiparous and multiparous cows. Protein content tended to be lower in quarters with increased SCS and low DSCC. In quarters from primiparous cows with DSCC levels above 70%, protein content showed slight variation across SCS levels. For multiparous cows, however, protein content remained relatively stable across different SCS and DSCC levels. In conclusion, our findings revealed that the combination of elevated SCS and low DSCC was associated with the most substantial milk loss. These results could be used to optimize udder health management.

Several studies have described variations in lactose content (LC) in dairy cows during udder quarter health disorder or negative energy balance (NEB). However, their joint effects on LC have never been described. This was the aim of a longitudinal observational study performed on 5 Quebec dairy farms using automatic milking systems. Quarter milk samples were collected every 14 d from 5 to 300 DIM. Quarter health status was described by combining SCC level (SCC^- or SCC⁺: < or ≥100,000 cells/mL, respectively) and infectious status (Patho^- or Patho⁺: absence or presence of pathogens on a milk culture, respectively). Cows with NEB in early lactation (DIM <70) were identified using milk BHB content: 0.19 mM = BHB++. A total of 14,505 quarter cisternal milk samples were collected from 380 lactating cows. The quarter LC was analyzed using a mixed linear regression model with the following fixed effects: quarter health status, parity, time interval between last milking and sampling, quarter milk yield (in kg/d), DIM, and herd. A random quarter intercept with a repeated measures correlation structure and a cow random intercept were also specified. The LC of SCC⁺ quarters was lower (-0.17 ± 0.013 percentage points) compared with LC of SCC^- quarters for both primiparous and multiparous cows. Of the 162 bacterial species identified, only 8 species had a prevalence greater than 4.0%, and just 5 of them were associated with a reduction in LC: Staphylococcus aureus, Staphylococcus chromogenes, Streptococcus dysgalactiae, Staphylococcus epidermidis, and Staphylococcus simulans. Cows identified as BHB+ and BHB++ in early lactation had a lower LC (-0.05 ± 0.019 and -0.13 ± 0.020 percentage points, respectively) compared with BHB- cows. For BHB++ cows, in both parity groups the decrease in LC (-0.20 ± 0.025 percentage points) was higher in SCC⁺ quarters compared with SCC^- quarters. Moreover, the additive effect of the quarter health status and NEB on milk LC was greater with larger increases in BHB. Our findings highlight the necessity to jointly take into consideration both quarter health status and milk BHB concentration when using LC as a biomarker for NEB.

Mastitis poses significant economic challenges for dairy farms. Therefore, enhancing the accuracy of diagnostic methods for detecting intramammary infections (IMI) can potentially improve prevention, control and treatment strategies. The somatic cell count (SCC) is a well-established parameter for identifying inflammation resulting from IMI. Given the recent introduction of differential somatic cell count (DSCC) for routine milk sample screening, limited research has been conducted to assess its additional benefits for diagnosing IMI. Therefore, our main objective was to evaluate the diagnostic accuracy of SCC, DSCC and SCC-DSCC combinations in detecting IMI caused by any pathogen or by major pathogens using quarter milk samples. Five dairy herds using automated milking systems were selected using convenience sampling in Québec, Canada. Determination of SCC and DSCC was performed by Lactanet (Ste-Anne de Bellevue, QC, Canada) using a CombiFoss 7 DC instrument. A 5-populations 2-tests Bayesian latent class models was used, with bacteriological culture employed as the imperfect reference test. Posterior estimates for sensitivity (Se), specificity (Sp), and the predictive values for 2 hypotheticals IMI prevalences due to any pathogen or major pathogens were computed. The proportion of quarters positive for any pathogen or major pathogen using milk culture was 31.7% (5,125/16,176) and 5.4% (871/16,176), respectively. For the detection of IMI by any pathogen, using a serial interpretation for the combination of SCC ≥100,000 and DSCC at ≥65% increased the Sp from 0.71 (95BCI: 0.70, 0.72) to 0.84 (95BCI: 0.83, 0.86) compared with SCC alone at the cut-off ≥100,000 cells/mL, although resulting in a slight decrease in Se from 0.49 (95BCI: 0.43, 0.54) to 0.46 (95BCI: 0.42, 0.50). Moreover, for detecting IMI caused by major pathogens, combining SCC at the threshold of ≥100,000 cells/mL and DSCC at the threshold of ≥65% using serial interpretation increased the Sp from 0.68 (95BCI: 0.67, 0.69) to 0.80 (95BCI: 0.79, 0.81) compared with SCC alone at the ≥100,000 cells/mL threshold. Our findings suggest that DSCC could be combined with SCC to provide a modest improvement in Sp with minimal compromise in Se for identifying IMI caused by any or by major pathogens. In addition, DSCC combined with SCC provided a small improvement in Se for detecting any pathogen using the parallel interpretation. However, no improvements in Se were observed when using the combination of SCC and DSCC for detecting major pathogens.

Microbial infections of the mammary gland often cause mastitis, and it can lead to substantial economic losses within the dairy industry due to its direct negative impact on milk production and composition and the associated treatment costs. Somatic cell count has emerged as a critical indicator in monitoring udder health, and recently, the large-scale availability of differential cell count analysis potentially offers new insights into underlying physiological processes. Therefore, the main objective of this study was to estimate the variation of DSCC and SCC of individual quarter-level milk samples of cows according to 1) their intramammary infectious status; 2) parity of the cow; 3) quarter location; and 4) DIM at the time of sampling. A convenience sample of 5 dairy herds using an automated milking system was selected and visited every 2 weeks for milk sample collection. The determination of SCC and DSCC was performed by Lactanet using a CombiFoss 7 DC instrument. The bacteriological culture was performed according to the National Mastitis Council standards. The different types of colonies (up to 10 colonies) were counted and identified MALDI-TOF. Given the hierarchical structure of the data, a 4-level, linear mixed model with herd, cow, and quarter as random intercepts was built with either SCS or DSCC as the outcome. Differential SCC varied broadly in the SCS range 2 to 12 but tended to have a narrower variation at higher SCS levels. The effect of DIM on DSCC depended on the parity. Early in lactation, primiparous cows tended to have lower DSCC than older cows. Following 230 DIM, the DSCC in primiparous exhibited an upward trend, whereas, in older cows, it tended to decline. The quarter position did not affect either DSCC or SCS. Quarters infected with S. chromogenes, S. dysgalactiae, S. aureus, S. epidermidis, "other major," and "other minor," had an increase in DSCC by approximately 10.2%, 9.9%, 9.8%, 9.2%, 6.0%, and 4.9% when compared with quarters with no growth. Our findings highlighted that IMI, parity, and DIM influenced DSCC. Therefore, these parameters should be considered when interpreting DSCC.