Automatic milking systems (AMSs) are revolutionizing the dairy industry by boosting herd efficiency, primarily through an increased milk yield per cow and reduced labor costs. The performance of milking machines, whether traditional or automated, can be evaluated using advanced vacuum meters through dynamic testing. This process involves scrutinizing the system and milking routine to identify critical points, utilizing the VaDia™ logger (BioControl AS, Rakkestad, Norway). Vacuum recordings were downloaded and analyzed using the VaDia Suite™ software under the guidance of a milking specialist. Access to data from AMSs across various manufacturers and herds facilitated a retrospective study aimed at describing and comparing key milk emission parameters for different AMS brands while identifying potential mastitis risk factors. Using the proper statistical procedures of SPSS 29.1 (IBM Corp., Armonk, NY, USA), researchers analyzed data from 4878 individual quarter milkings from cows in 48 dairy herds. Results indicated a significant variability in milking parameters associated with quarter milk yield and AMS brand. Notably, despite AMSs standardizing teat preparation and stimulation, this study revealed a surprisingly high frequency of two major mastitis risk factors—bimodality and irregular vacuum fluctuations—occurring more frequently than in conventional milking systems. This study, one of the few comparing different AMS brands and their performance, highlights the crucial role of dynamic testing in evaluating AMS performance under real-world conditions.

Several studies have demonstrated an association between impaired innate immunity and metabolic parameters, particularly during the periparturient period. However, to our knowledge, no study has been conducted under field conditions investigating the link between low milk polymorphonuclear neutrophil (PMN) levels and increased disease frequency. In an attempt to address this knowledge gap, this study examined 6209 cows from 20 dairy herds in Lombardy that were enrolled in a monthly individual dairy herd improvement milk testing program. Analyses of milk test record samples (MTR) included somatic cell count (SCC) and differential cell count (DSCC). A third variable, PLCC (polymorphonuclear neutrophil leukocyte cell count), was calculated by multiplying SCC × DSCC, thus representing PMN cells/mL. A database including compulsory records of all antimicrobial treatments applied in each herd was used as a proxy for disease frequency. In total, 58,090 valid MTR and 12,014 antimicrobial treatments (AMT) were considered for this study. Statistical analyses showed a significant association between the prevalence of cows with a low number of milk PMN and the prevalence of AMT. These results allow routine identification of whether the number of cows with low PLCC exceeds an alarm level within a herd. This threshold was calculated using an ROC curve with a cut-off point of 6% for AMT. This threshold was estimated at 2%, providing 78% accuracy in identifying herds at risk of an increasing treatment rate. This study confirms that cellular markers measured within MTR systems are useful in identifying herds at risk of impaired cellular immunity, thus paving the way for further studies assessing herd and cow immune status with routine milk sampling.

Automatic Milking Systems (AMS) are revolutionizing the dairy industry by boosting herd effi-ciency, primarily through increased milk yield per cow and reduced labor costs. The performance of milking machines, whether traditional or automated, can be evaluated using advanced vacuum meters through dynamic testing. This process involves scrutinizing the system and milking rou-tine to identify critical points, utilizing the VaDia™ logger (BioControl AS, Rakkestad, NO). Vacuum recordings were downloaded and analyzed using the VaDia Suite™ software under the guidance of a milking specialist. Access to data from AMS across various manufacturers and herds facilitated a retrospective study aimed at describing and comparing key milk emission parameters for different AMS brands while identifying potential mastitis risk factors. Using proper statistical procedures of SPSS 29.1 (IBM Corp, USA), researchers analyzed data from 4,878 individual quarter milkings from cows in 48 dairy herds. Results indicated significant variability in milking pa-rameters associated to quarter milk yield and AMS brand. Notably, despite AMS standardizing teat preparation and stimulation, the study revealed a surprisingly high frequency of two major mastitis risk factors—bimodality and irregular vacuum fluctuations—occurring more frequently than in conventional milking systems. This study, one of the few comparing different AMS brands and their performance, highlights the crucial role of dynamic testing in evaluating AMS perfor-mance under real-world conditions.

Several studies have demonstrated an association between impaired innate immunity and metabolic parameters, particularly during the periparturient period. However, to our knowledge, no study has been conducted under field conditions investigating the link between low milk polymorphonuclear leukocytes neutrophils cells (PMN) levels and increased disease frequency. In an attempt to address this knowledge gap, this study examined 6,209 cows from 20 dairy herds in Lombardy that are enrolled in a monthly individual dairy herd improvement milk testing program. Analyses of milk test record samples (MTR) included somatic cell count (SCC) and differential cell count (DSCC). A third variable, PLCC, was calculated by multiplying SCC x DSCC, thus representing PMN cells/mL. A database including compulsory records of all antimicrobial treatments applied in each herd was used as a proxy for disease frequency. In total, 58,090 valid MTR and 12,014 antimicrobial treatments (AMT) were considered for this study. Statistical analyses showed a significant association between prevalence of cows with a low number of milk PMN and the prevalence of AMT. These results allow to routinely identify whether the number of cows with low PLCC exceeds an alarm level within a herd. This threshold was calculated using a ROC curve with a cut-off point of 6% for AMT. This threshold was estimated at 2%, providing 78% accuracy in identifying herds at risk of an increasing treatment rate. This study confirms that cellular markers measured within MTR systems, are useful in identifying herds at risk of impaired cellular immunity, thus paving the way to further studies assessing herd and cow immune status with routine milk samplings.

Evaluating bovine udder physiological status requires reliable diagnostic markers that remain consistent across varying farm management practices. This study aimed to characterize milk fatty acid (FA) patterns and total polymorphonuclear neutrophils (PMN) to determine their potential as combined markers for udder health and physiological status. A total of 31,463 milk test records from 18 commercial dairy herds were analyzed using Fourier transform mid-infrared spectroscopy and automated flow cytometry to evaluate milk FA composition, somatic cell counts, and differential somatic cell counts. Mixed linear models assessed the effects of season, parity, days in milk, and milk yield on FA and PMN variability. Measuring de novo FA as a relative proportion reduced herd-level variance from 23.5% to 1.8% compared to concentration measurements. De novo FA proportions peaked at 91–120 days in milk and reached their lowest levels during summer. Among healthy cows (somatic cell count ≤ 50,000 cells/mL), a lower de novo FA proportion (≤ 22%) significantly correlated with elevated total PMN counts in spring and summer. Integrating relative de novo FA proportions with PMN counts may provide a robust, farm-independent diagnostic tool to assess udder physiological status and immune capability for improved herd management.