Bovine subclinical mastitis can cause great harm to dairy herds because of its negative impact on milk production and quality and cow health. Improved diagnostic tools are needed to maximise the control of subclinical mastitis distribution and ensure the high quality of milk as an industrial product. Between 2015 and 2020, seventy-two dairy herds were screened for bovine subclinical mastitis causative agents to identify the relationship between seasons, lactose levels and subclinical mastitis infection. The predominant species found in the milk samples were mixed microbiota, coagulase-negative Staphylococcus and Staphylococcus aureus. Yeasts were found exclusively in autumn, while Enterococcus spp. and Escherichia coli were only found in summer and autumn. A negative correlation was detected between milk lactose and number of somatic cells in milk (−0.471; p < 0.001). The lactose levels in milk were closely associated with the prevalence (%) of subclinical mastitis pathogens, such as Streptococcus agalactiae (y = −1.8011x + 10.867, R² = 0.9298), Staph. aureus (y = −3.5216x + 25.957, R² = 0.8604) and other Streptococci (y = −0.5956x + 7.6179, R² = 0.6656). These findings suggest that milk lactose may be used as a biomarker of suspected udder inflammation in modern health prevention programmes for cows to reduce the prevalence of subclinical mastitis pathogens in dairy cattle herds.

Starch-rich diets can cause subacute ruminal acidosis (SARA) in dairy cows with potentially different susceptibility according to lactation number. We wanted to evaluate the bacterial community and the fermentation end products in feces to study susceptibility to hindgut acidosis and dysbiosis. Sixteen dairy cows received a medium-concentrate diet (MC, 40% concentrate, 18.8% starch) for one week and a high-concentrate diet (HC, 60% concentrate, 27.7% starch, DM) for four weeks. Milk yield, dry-matter intake, chewing activity, ruminal pH, milk constituents, and fecal samples for short-chain fatty acids (SCFA), pH, and 16S rRNA-gene sequencing were investigated. The HC feeding caused a reduction in fecal pH, bacterial diversity and richness, an increase in total SCFA, and a separate phylogenetic clustering of MC and HC samples. Ruminal and fecal pH had fair correlation (r = 0.5). Cows in the second lactation (2ndL) had lower dry matter intake (DMI) than cows of third or fourth or more lactations (3rdL; ≥4 L), whereas DMI/kg body weight was lower for ≥4 L than for 2ndL and 3rdL cows. The mean ruminal pH was highest in ≥4 L, whereas the time spent below the SARA threshold was highest for 3rdL cows. The latter also had higher total SCFA in the feces. Our results suggest that hindgut dysbiosis is caused by increased substrate flow to the hindgut, but further investigations are needed to define hindgut acidosis. The 3rdL cows were most susceptible to rumen acidosis and hindgut dysbiosis due to high DMI level, but missing counter regulations, as suggested happening in 2ndL and ≥4 L cows.

In this study, cow reticulorumen content pH and temperature together with the activity were registered using smaXtec boluses, specially designed for animal care. Body weight, rumination time, milk fat/protein ratio, milk yield, milk lactose, milk electrical conductivity, milk somatic cell count and consumption of concentrated feed were registered by Lely Astronaut^® A3 milking robots. The cows in this study were assigned into two groups according to the milk lactose concentration: group 1—milk lactose < 4.70% (n = 20), group 2—milk lactose ≥ 4.70% (n = 15). The following cows were further classified according to milk fat and protein ratio: F/P < 1.2 (class 1), F/P = 1.2 (class 2) and F/P > 1.2 (class 3). According to our results, we can conclude that inline registered milk lactose concentration can be used to indicate the health status and reproductive success of fresh dairy cows. Cows with an increased lactose concentration (≥4.70%) showed more activity (54.47%) and had less risk of mastitis (determined by lower milk electrical conductivity (EC) and somatic cell counts (SCC)) and metabolic disorders, determined by milk F/P. A higher glucose concentration was also apparent in the cows with higher lactose concentration. Registered lower levels of milk lactose can be used for early identification of metabolic disorders and mastitis (set at milk SCC ≥ 100 thousand/mL). Lactose levels in cows’ milk were positively associated with their reproductive success.

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = −0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis.

Heat stress (HS) significantly impacts dairy farming, prompting interest in precision dairy farming (PDF) for gauging its effects on cow health. This study assessed the influence of the Temperature–Humidity Index (THI) on rumination, eating, and locomotor activity. Various parameters, like rumination time, drinking gulps, chews per minute, and others were analyzed. The hypothesis was that precision dairy farming technology could help detect HS. Nine healthy Lithuanian Black-and-White cows were randomly selected for the trial. RumiWatch noseband sensors recorded behaviors, while SmaXtec climate sensors monitored THI. The data collection spanned from 14 June to 30 June. Cows in the THI class ≥ 72 exhibited reduced drinking time (51.16% decrease, p < 0.01), fewer chews per minute (12.9% decrease, p < 0.01), and higher activity levels (16.99% increase, p < 0.01). THI showed an inverse correlation with drinking time (r = −0.191, p < 0.05) and chews per bolus (r = −0.172, p < 0.01). Innovative technologies like RumiWatch are effective in detecting HS effects on behaviors. Future studies should explore the impact of HS on RWS biomarkers, considering factors such as lactation stage, number, yield, and pregnancy.

The aim of this study was to assess the relationship between temperament and milk performance in cows at different stages of lactation, describing their productivity, metabolic status and resistance to mastitis. This study showed that with increasing lactation, cows’ temperament indicators decreased (p < 0.001) and they became calmer. The highest temperament score on a five-point scale was found in cows between 45 and 100 days of lactation. In the group of pregnant cows, we found more cows (p = 0.005) with a temperament score of 1–2 compared with non-pregnant cows A normal temperament was usually detected in cows with lactose levels in milk of 4.60% or more and when the somatic cell count (SCC) values in cow milk were <100,000/mL and 100,000–200,000/mL, with a milk fat-to-protein ratio of 1.2. A larger number of more sensitive and highly aggressive cows was detected at a low milk urea level. In contrast to a positive phenotypic correlation (p < 0.05), this study showed a negative genetic correlation between the temperament of cows and milk yield (p < 0.001). Positive genetic correlations between temperament scores and milk somatic cells (p < 0.001) and milk fat-to-protein ratio (p < 0.05) were found to indicate a lower genetic predisposition in cows with a calmer temperament to subclinical mastitis and ketosis. On the other hand, the heritability of temperament (h² = 0.044–0.100) showed that only a small part of the phenotypic changes in this indicator is associated with genetic factors.