This study hypothesizes that higher in-line milk lactose concentrations are indicative of enhanced dairy cow behaviors—including increased rumination, feeding, and locomotion activities—reflecting superior overall health and well-being. It posits that fluctuations in milk lactose levels have a substantial impact on the physiological and behavioral responses of dairy cows, thereby affecting their milk yields and compositions. Each cow’s milk lactose, fat, protein, and fat-to-protein ratio were continuously monitored using the BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). The RumiWatch noseband sensor (RWS; ITIN + HOCH GmbH, Fütterungstechnik, Liestal, Switzerland) was employed to measure the biomarkers of the rumination, feeding, and locomotion behavior. The measurements were recorded over 5 days at the same time (during morning milking). A total of 502 cows were examined. During these 5 days, 2510 measurements were taken. Based on the lactose content in their milk, the cows were divided into two categories: the first group consisted of cows with milk lactose levels below 4.70%, while the second group included cows with milk lactose levels of 4.70% or higher. Our study showed that cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (16.14% increase) but had a lower milk protein concentration (5.05% decrease) compared to cows with lower lactose levels. These cows also exhibited changes in rumination and feeding behaviors, as recorded by the RWS: there was an increase in the mastication and rumination behaviors, evidenced by a 14.09% rise in other chews and a 13.84% increase in rumination chews, along with a 16.70% boost in bolus activity. However, there was a notable 16.18% reduction in their physical activity, as measured by the change in time spent walking.

Heat stress has a major impact on dairy cow health and productivity, especially during early lactation. Conventional heat stress monitoring methods frequently rely on single indicators, such as the temperature–humidity index (THI), which may miss subtle physiological and metabolic responses. This study presents a novel threshold-based classification framework that integrates biologically meaningful combinations of environmental, behavioural, and physiological variables to detect early-stage heat stress responses in dairy cows. Six composite heat stress conditions (C1–C6) were developed using real-time THI, milk temperature, reticulorumen pH, rumination time, milk lactose, and milk fat-to-protein ratio. The study applied and assessed five supervised machine learning models (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF0, Neural Network (NN), and an Ensemble approach) trained on daily datasets gathered from early-lactation dairy cows fitted with intraruminal boluses and monitored through milking parlour sensor systems. The dataset comprised approximately 36,000 matched records from 200 cows monitored over 60 days. The highest classification performance was observed for RF and NN models, particularly under C1 (THI > 73 and milk temperature > 38.6 °C) and C6 (THI > 74 and milk temperature > 38.7 °C), with AUC values exceeding 0.90. SHAP analysis revealed that milk temperature, THI, rumination time, and milk lactose were the most informative features across conditions. This integrative approach enhances precision livestock monitoring by enabling individualised heat stress risk classification well before clinical or production-level consequences emerge.

This study focused on assessing whether the inclusion of probiotics (B. subtilis) as feed additives during the preweaning stage can enhance the body weight and metabolic condition of neonatal calves. A total of 50 Holstein calves, all born on the same farm, were randomly divided into two homogeneous treatment groups after birth. The calves in the control group (CG) were fed a milk replacer (n = 25) (13 females and 12 males) and those in the B. subtilis-supplement-treated group (TG), (n = 25) (13 females and 12 males) were fed a milk replacer with 7.5 mL/calf/day of B. subtilis probiotic (complied with the manufacturer’s guidelines). The probiotic was administered 24 h post-birth, signifying the start of the experimental period. It took one month to collect the animals. Body weight was measured at birth for all animals. A local veterinarian, working on the farm, conducted daily health checks of the calves, recording health parameters and any antibiotic treatments. Blood samples were collected from each calf at birth and 30, 60, and 90 days by puncturing the jugular vein using 10 mL evacuated serum tubes before morning feeding. Significant differences in body weight were observed between the CG and the TG at 30, 60, and 90 days of age. At 30 days, the TG had a 4.11% higher average body weight than the CG (54.38 kg vs. 52.71 kg). At 60 days, the TG’s average weight was 3.75% higher (79.21 kg vs. 76.34 kg), and at 90 days, the TG had a 2.91% higher average weight (112.87 kg vs. 109.67 kg). At 30 days of age, the TG showed significantly lower AST activity, with a 41.12% decrease compared to the CG (51.02 IU/L vs. 72.00 IU/L). Conversely, GGT activity was significantly higher in the TG by 64.68% (40.64 IU/L vs. 14.35 IU/L). Phosphorus concentration at 30 days was also significantly higher in the TG by 9.36% (3.27 mmol/L vs. 2.99 mmol/L). Additionally, the TG had a significantly lower total protein concentration, with a 21.63% decrease at 30 days (46.32 g/L vs. 56.34 g/L) and a 20.28% decrease at 60 days (48.32 g/L vs. 58.12 g/L) compared to the CG. These findings indicate that dairy calves given conventional milk replacer along with a daily dose of 7.5 mL of B. subtilis probiotic experienced enhanced growth performance and a more favourable metabolic profile during the first 90 days of their lives.

The early lactation phase in dairy cows is characterised by significant metabolic and inflammatory changes. This study aimed to evaluate the relationship between serum nonesterified fatty acids (NEFAs), a marker of negative energy balance (NEB), and serum amyloid A (SAA), an indicator of systemic inflammation. Blood samples were collected from 71 Holstein cows during the transition period 17 (±3) DIM, and serum concentrations of NEFAs and SAA were measured. The results revealed a significant negative correlation between NEFAs and SAA (r = −0.441, p < 0.001), suggesting that increased fat mobilisation may suppress the inflammatory response, thereby increasing the susceptibility to metabolic and infectious diseases. The emerging research indicates a negative association between SAA levels and milk fat-to-protein ratio in dairy cows, particularly under inflammatory conditions. The research indicates that elevated levels of SAA, which is an inflammatory biomarker, are frequently associated with alterations in milk composition, including a reduced fat-to-protein ratio. This study examined the correlations among serum NEFAs, SAA, milk composition, and dairy cattle health. A strong positive correlation was identified between serum NEFAs and milk fat content (r = 0.459, p < 0.001), as well as between serum NEFAs and the milk fat-to-protein ratio (r = 0.516, p < 0.001). Cows with elevated serum NEFA levels (classified as II-NEFA) exhibited significantly higher milk fat content (4.20%) and milk fat-to-protein ratios (1.33) compared to cows with lower serum NEFA levels (I-NEFA class; 3.81% and 1.17, respectively). The data indicate that elevated serum NEFA levels are associated with an increased milk fat synthesis, likely driven by enhanced fat mobilisation during NEB. A significant negative correlation was observed between SAA and both milk fat content (r = −0.426, p < 0.001) and the milk fat-to-protein ratio (r = −0.535, p < 0.001), indicating that inflammation may impair milk fat production. Elevated SAA levels were also associated with increased cow activity (r = 0.382, p < 0.001), suggesting that inflammation may lead to behavioural changes driven by discomfort. Our findings suggest that milk composition reflects the metabolic and inflammatory status of dairy cows and could serve as a non-invasive alternative to blood sampling for assessing energy balance and health. NEB, which typifies early lactation, promotes fat mobilisation, resulting in elevated serum NEFA levels and an increased risk of metabolic disorders such as fatty liver syndrome and ketosis. Moreover, high serum NEFA levels adversely affect immune function, increasing vulnerability to infections such as mastitis. Monitoring milk composition may enable the early detection of NEB and inflammatory conditions, thereby supporting proactive health management. However, further research is necessary to elucidate the role of NEFAs and inflammation in the development of metabolic diseases in cattle.

This study investigates the relationship between lameness, milk composition, and rumination behaviour in dairy cows by leveraging sensor-based data for automated monitoring. Lameness was found to significantly impact both rumination and milk production. Lameness was assessed in 24 multiparous Holstein dairy cows throughout early lactation (up to 100 days postpartum), utilising a 1-to-5 scale. Lameness was found to significantly impact both rumination and milk production. On the day of diagnosis, rumination time decreased by 26.64% compared to the pre-diagnosis period (p < 0.01) and by 26.06% compared to healthy cows, indicating the potential of rumination as an early health indicator. The milk yield on the day of diagnosis was 28.10% lower compared to pre-diagnosis levels (p < 0.01) and 40.46% lower than healthy cows (p < 0.05). These findings suggest that lameness manifests prior to clinical signs, affecting productivity and welfare. Milk composition was also influenced, with lame cows exhibiting altered fat (+0.68%, p < 0.05) and lactose (−2.15%, p < 0.05) content compared to healthy cows. Positive correlations were identified between rumination time and milk yield (r = 0.491, p < 0.001), while negative correlations were observed between milk yield and milk fat, protein, and the fat-to-protein ratio (p < 0.001). Additionally, lameness was associated with elevated somatic cell counts in the milk, although sample size limitations necessitate further validation. This study highlights the critical role of rumination and milk performance metrics in identifying subclinical lameness, emphasising the utility of automated systems in advancing dairy cow welfare and productivity. The findings underscore the importance of early detection and management strategies to mitigate the economic and welfare impacts of lameness in dairy farming.

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms.