Antanaitis, Ramūnas

Rumination time is considered a sensitive behavioral indicator of physiological and metabolic status in dairy cows, yet its relationships with biochemical and milk quality parameters under commercial robotic milking conditions remain insufficiently described. This study combined precision monitoring technologies, serum biochemical profiling, and in-line milk analysis to evaluate physiological differences among early-lactation Holstein cows according to rumination time. A total of 88 cows were classified into three rumination time categories (>527, 412–527, and <412 min/day). Milk production traits, milk quality indicators, and blood biochemical parameters were compared among groups, and univariable regression analysis was performed to identify variables associated with rumination time. Cows in the low rumination group showed higher milk temperature, electrical conductivity, and somatic cell count, as well as lower milk protein percentage. They also showed higher concentrations of total protein, urea, gamma-glutamyl transferase, and lactate dehydrogenase, while triglyceride concentrations were lower. Regression analysis identified electrical milk conductivity, creatinine, magnesium, potassium, and chloride as variables associated with rumination time. These findings indicate that reduced rumination time is associated with changes in milk quality and biochemical parameters in early-lactation dairy cows, suggesting that rumination monitoring may provide useful information for identifying cows experiencing physiological and metabolic challenges under commercial farming conditions.

This study evaluated the impact of essential oil supplementation, primarily containing seed oil from coriander, along with eugenol, geranyl acetate and geraniol, on the blood metabolic profile and overall health of dairy cattle during the transition period. Milking was done using a milking robot, all cows were given total mixed ration (TMR) twice a day, at 07:00 a.m. and 07:00 p.m. A total of 140 multiparous Holstein cows were divided into two groups: a test group (n=70) receiving essential oil supplementation (1 g/cow/day) and a control group (n=70). The cows were monitored from 30 days before calving to 90 days post-calving. Results showed that cows in the test group produced 4.5% to 7% more milk compared to the control group across different lactation periods (5-90 days in milk). Milk composition was also improved with higher milk fat and protein percentages. Essential oil supplementation positively influenced feed efficiency and metabolic indicators such as albumin. These results suggest that essential oil supplementation enhances milk yield, composition, and efficiency during the critical transition period.

Lactate dehydrogenase (LDH) is widely used as a nonspecific marker of tissue damage and cellular turnover and has been associated with metabolic and inflammatory processes, but its relationship with automated monitoring data and blood biochemical indicators in early-lactation dairy cows is still not well described. The aim of this study was to evaluate associations between LDH activity, blood biochemical parameters, and automated monitoring indicators in early-lactation Holstein cows. A total of 91 clinically healthy cows were classified into two groups according to LDH activity: Group 1 (LDH < 1364 U/L; n = 53) and Group 2 (LDH ≥ 1364 U/L; n = 38). Blood samples were collected once per cow during early lactation, whereas automated monitoring parameters were continuously recorded and daily averages corresponding to the sampling day were used for analysis. Cows with higher LDH activity had significantly higher aspartate aminotransferase (AST) activity and moderate increases in albumin (ALB), creatinine (CREA), gamma-glutamyl transferase (GGT), calcium (Ca), phosphorus (PHOS), and iron (Fe). Correlation analysis showed a strong positive association between LDH and AST (r = 0.799, p < 0.001), while moderate positive correlations were observed with ALB, alanine aminotransferase (ALT), CREA, Ca, GGT, Fe, and PHOS. Receiver operating characteristic (ROC) analysis showed the best discrimination ability for AST, while CREA, ALB, Fe, PHOS, Ca, and GGT showed moderate classification performance. Automated monitoring parameters did not differ significantly between groups; however, cows with higher LDH activity tended to show lower rumination time together with higher milk electrical conductivity, higher milk yield, higher fat-to-protein ratio (FPR), and higher somatic cell count (SCC). Overall, the results indicate that LDH is more closely related to systemic biochemical variation than to immediate changes in production or behavioral indicators, and support the use of biochemical markers together with automated monitoring data when evaluating physiological adaptation during early lactation.

Kalbėjimas apie dirbtinį intelektą gyvūnų sveikatos srityje dar prieš kelis dešimtmečius būtų atrodęs kaip fantastika. Šiandien tai jau reali ir sparčiai besiplečianti praktika, kuri iš esmės keičia gyvulininkystės sektorių.

SCC is a standard indicator of udder inflammation, but it reflects only part of the broader physiological changes occurring in the mammary gland. This study aimed to evaluate associations between SCC, in-line milk traits, and blood biochemical markers in Holstein dairy cows. Based on SCC and California Mastitis Test (CMT) results, 59 cows (20–100 DIM) were divided into three groups: Group 1 (SCC < 200,000 cells/mL; n = 20), Group 2 (SCC 200,000–500,000 cells/mL; n = 19), and Group 3 (SCC > 500,000 cells/mL; n = 20). The Lely Astronaut® A3 system was used to record milk parameters and behavioral data, while blood samples were collected for biochemical analysis. While there were negative relationships with milk yield (r = −0.266, p < 0.05) and creatinine (r = −0.291, p < 0.05), there was a significant positive correlation between SCC and milk electrical conductivity (EC) (r = 0.330, p < 0.05), gamma-glutamyl transferase (GGT) (r = 0.424, p < 0.001), and lactate dehydrogenase (LDH) (r = 0.285, p < 0.05). Potassium and chloride concentrations varied between groups, indicating slight electrolyte imbalances linked to higher SCC even though they remained within physiological bounds. Receiver operating characteristic (ROC) analysis further showed that milk EC (area under the curve (AUC) = 0.770) and blood potassium (AUC = 0.707) demonstrated the highest diagnostic accuracy for distinguishing healthy and mastitic cows. These results show that integrating SCC data with automated in-line monitoring and blood biochemical profiling can help identify novel complementary indicators for the detection of mastitis in dairy cows and offer a deeper understanding of udder health.

This study aimed to investigate the relationship between enteric methane (CH4) emissions and metabolic, physiological, and behavioural factors in early lactation Holstein cows. Forty-two cows were observed over a span of five consecutive weeks (0–100 days in lactation). CH4 concentration (ppm) was quantified with a portable laser detector, whereas rumination duration, temperature, and water consumption were documented using intraruminal boluses. Weekly blood samples were examined for beta-hydroxybutyrate (BHB), C-reactive protein (CRP), urea (UREA), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) levels. The evaluation of milk yield and composition was conducted utilising in-line infrared sensors. Cows were classified against clinical reference intervals, and associations were tested via group comparisons, correlations, multiple linear regression, linear mixed models (cow ID random effect), ROC analysis, and by relating CH4 to dry matter intake (DMI). Cows with elevated BHB (≥1.2 mmol/L) emitted 87.8% more CH4 than cows within range and showed higher CH4 yield per kg DMI; elevated GGT was likewise associated with higher CH4 (+25.2%). CH4 correlated positively with BHB (r = 0.54, p < 0.01), and negatively with rumination (r = −0.38, p < 0.05). Regression explained 30.2% of CH4 variance (adjusted R2 = 0.302): BHB was a positive predictor (β = 0.55, p = 0.047), whereas LDH was negative (β = −0.21, p = 0.033). A three-way interaction (BHB group × AST × GGT) was significant in the mixed model (F = 6.91, p = 0.002). For discrimination of high emitters, BHB achieved AUC = 0.889; among on-farm traits, milk yield (AUC = 0.823) and lactose (AUC = 0.701) performed best. DMI related inversely to CH4 yield (r = −0.69, p = 0.058). The findings indicate that enteric methane production during early lactation is not exclusively influenced by diet but is significantly associated with systemic metabolic health. Integrating physiological and production characteristics may improve precision-driven methane monitoring and mitigation strategies in dairy systems.

Objectives: This studyʼs primary objective was to investigate the connection between the milk fat-toprotein ratio (FPR) and the metabolic changes associated with subclinical ketosis and low-grade ruminal acidosis in Holstein dairy cows during early lactation. The researchers aimed to validate FPR as a non-invasive indicator for these metabolic disorders by correlating it with specific blood biochemical markers. Methods: Twenty-seven cows, ranging from 9 to 59 days postpartum, were sorted into three distinct groups based on their FPR values: • Low-grade ruminal acidosis (FPR < 1.2) • Metabolically healthy (FPR 1.2–1.5) • Subclinical ketosis (FPR > 1.5) Milk composition was continuously monitored using a Brolis HerdLine in-line analyzer. To assess metabolic health, weekly blood samples were collected and tested for various parameters, including iron, glucose, and non-esterified fatty acids (NEFA). Results: The analysis revealed significant differences among the groups: • Cows with subclinical ketosis showed significantly higher NEFA levels compared to the ruminal acidosis group (p < 0.01), a finding consistent with the enhanced fat mobilization that occurs during a negative energy balance. • Serum iron levels were significantly lower in the ketosis group than in the ruminal acidosis group (p < 0.01). Furthermore, the ruminal acidosis group also had significantly lower serum iron than the metabolically healthy cows (p < 0.01), highlighting that iron metabolism is disrupted in both conditions. • Correlation analysis supported these findings: FPR positively correlated with NEFA (r = 0.281, p < 0.01) and negatively correlated with serum iron (r = –0.308, p < 0.01). Conclusions: The study concluded that FPR is a reliable indicator of metabolic health in early-lactation dairy cows. It accurately reflects key metabolic alterations, such as elevated NEFA in ketosis and reduced serum iron in both ketosis and ruminal acidosis. These results confirm the utility of FPR as a simple and practical tool for the early detection and management of subclinical metabolic disorders in dairy cows.

Subclinical hypocalcemia (SCH) is one of the most prevalent metabolic disorders in early-lactation dairy cows, yet its multifaceted physiological effects are often overlooked due to the absence of clinical symptoms. This study aimed to characterize SCH through an integrative assessment of blood biochemical markers, in-line milk composition, and sensor-derived behavioral traits. Seventy-five Holstein cows between 2 and 21 days in milk were classified into hypocalcemic (group 1) (Ca < 2.0 mmol/L; n = 20) and healthy (group 2) groups (n = 55). Blood samples, milk data, and rumination metrics were evaluated, and group differences were analyzed using Welch’s t-test and Pearson correlations. Cows with SCH exhibited significantly lower concentrations of Ca, PHOS, Mg, ALB, TP, GLUC, and Fe, indicating disruptions in mineral balance, protein metabolism, and energy status. Hepatic indicators (AST, ALT, GGT) did not differ between groups, whereas CREA was significantly lower in hypocalcemic cows, suggesting altered muscle metabolism rather than impaired renal function. Although differences in milk yield, composition, and rumination time did not reach statistical significance, hypocalcemic cows showed consistent biological tendencies toward reduced milk components and lower milk temperature. Correlation analysis revealed strong physiological linkages among Ca, ALB, P, TP, and Fe, underscoring the interconnected nature of mineral and protein metabolism in early lactation. These findings demonstrate that SCH is associated with coordinated biochemical and behavioral changes even in the absence of clinical signs. Integrating blood biomarkers with real-time sensor data provides a more comprehensive understanding of calcium-related metabolic challenges and highlights the potential of precision-livestock technologies for early detection. Future studies incorporating ionized calcium and longitudinal sampling are needed to refine diagnostic thresholds and improve predictive monitoring of SCH in dairy herds.

This study assessed five supervised machine learning (ML) models. Automated devices that continuously captured milk composition and behavioral data were used to monitor 206 Holstein cows from two commercial dairy farms. Milk yield, fat, protein, lactose, fat-to-protein ratio (FPR), somatic cell count (SCC), rumination time (RT), and body temperature were among the parameters that were noted. Cows were categorized as clinically healthy (n = 45), subclinical ketosis (n = 91), subclinical mastitis (n = 28), or clinical mastitis (n = 42) based on clinical examination, blood β-hydroxybutyrate (BHB) concentration, and milk indicators. Random Forest achieved the highest classification accuracy (0.857), followed by Gradient Boosting and Logistic Regression (0.833), while Decision Tree and Multilayer Perceptron reached 0.810. Compared to clinically healthy cows (4.45 ± 0.54%; 477.0 ± 36.0 min/day), subclinical ketosis cows had a greater milk fat content (5.21 ± 0.72%) and a shorter RT (336.9 ± 94.2 min/day). In comparison to clinically healthy cows (64.0 × 103 cells/mL; 4.63 ± 0.16%), cows with clinical mastitis showed significantly greater SCC (416.8 × 103 cells/mL) and lower lactose levels (4.56 ± 0.24%). These results demonstrate that integrating sensor-derived milk and behavioral data with ML algorithms enables early, non-invasive detection of health disorders, supporting proactive herd management.

This study aimed to evaluate the economic benefits of using the in-line milk analyzer “Brolis HerdLine” (Brolis Sensor Technology, Vilnius, Lithuania) for the early detection of negative energy balance (NEB) and to assess the value of preventive treatment in dairy cows at high risk of NEB. A total of 52 Holstein cows were selected and paired based on lactation number, days in milk, and fat-to-protein ratio. The pairs were randomly allocated into two treatment groups: Control group (CON, n = 26) and Test group (TE, n = 26). Cows in the TE group received a single 32.4 g monensin (Kexxtone®) controlled-release capsule, while CON cows received no treatment. Milk composition was monitored using the in-line analyzer, and energy-corrected milk (ECM) was used to evaluate performance. Over a 200-day period, the TE group produced 9,916 kg more ECM than the CON group, resulting in an additional €3,056 profit after subtracting treatment costs. On day 20 post-treatment, TE cows showed significantly higher lactose levels (4.64%) than CON cows (p = 0.03). The successful insemination rate was 8.11% higher in the CON group. These results imply that early intervention through the use of an in-line milk analyzer to identify cows at high risk of NEB improves economic performance. The study emphasizes how milk analyzers can be used to make decisions in real time when managing dairy herds.