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Optimal milking management leads to an increase in production efficiency while respecting animal welfare and health. Not all farms can equip themselves with high level technological systems for economic, generational and structural reasons. The aim of the study was to assess milking performances, cow behaviour and microclimatic condition in farm areas using an integrated data collection system based on surveys and low-cost sensors. The use of video recordings and mobile electronic flowmeters highlighted significant differences between farms that milked two and three times a day. Milking efficiency parameters (cow/hour; cows/stall per hour; milk/hour; milk/stall per hour) were calculated. In particular, the milk/stall per hour parameter emerged as the most interesting. Animal stay time, defined as the time running from when cows are moved from the housing to the beginning of the milking, was calculated and the highest value (76.3 minutes) was observed in farms with more than 10 stalls/row. Hobo pendant sensors were used in order to evaluate cows lying behaviour: no differences were observed between farms that milked two and three times a day (11.3 vs 11.0 h/d of lying). Microclimatic conditions were influenced by both the season (p

Mastitis by non-aureus staphylococci (NAS) is a significant issue in dairy buffalo farming. In a herd with subclinical NAS mastitis, we identified Staphylococcus microti as the predominant species. To assess milk protein integrity and investigate potential disease markers, we characterized 12 NAS-positive and 12 healthy quarter milk samples by shotgun peptidomics combining peptide enrichment and high-performance liquid chromatography/tandem mass spectrometry (LC-MS/MS). We observed significant changes in the milk peptidome. Out of 789 total peptides identified in each group, 49 and 44 were unique or increased in NAS-positive and healthy milk, respectively. In NAS-positive milk, the differential peptides belonged mainly to caseins, followed by milk fat globule membrane proteins (MFGMP) and by the immune defense/antimicrobial proteins osteopontin, lactoperoxidase, and serum amyloid A. In healthy milk, these belonged mainly to MFGMP, followed by caseins. In terms of abundance, peptides from MFGMP and immune defense protein were higher in NAS-positive milk, while peptides from caseins were higher in healthy milk. These findings highlight the impact of NAS on buffalo milk quality and mammary gland health, even when clinical signs are not evident, and underscore the need for clarifying the epidemiology and relevance of the different NAS species in this dairy ruminant.

This observational study determined the lipidome of cow milk during subclinical intramammary infection (IMI) by non-aureus staphylococci (NAS), also defined as coagulase-negative staphylococci, using an untargeted approach. Among the pathogens causing bovine IMI, NAS have become the most frequently isolated bacteria from milk samples. Although the application of system biology approaches to mastitis has provided pivotal information by investigating the transcriptome, proteome, peptidome, and metabolome, the milk lipidome during mammary gland inflammation remains undisclosed. To cover this gap, we determined the milk lipidome of 17 dairy cows with IMI caused by NAS (NAS-IMI), and we compared the results with those of healthy quarter milk from 11 cows. The lipidome was determined following a liquid chromatography-quadrupole time-of-flight mass spectrometry approach. Sixteen subclasses of lipids were identified in both groups of animals. From 2,556 measured lipids, the abundance of 597 changed more than 10-fold in quarter milk with NAS-IMI compared with healthy quarters. The results demonstrate the influence of NAS-IMI on the milk lipidome, implying significant changes in lipid species belonging to the family of triacylglycerols and sphingomyelins, and contribute to the understanding of inflammatory processes in the bovine udder, highlighting potential novel biomarkers for improving mastitis diagnostics.

Coagulase-negative staphylococci (CNS) are the most prevalent microorganisms isolated from cow milk and are associated with subclinical mastitis and persistent increases in the bulk milk somatic cell count (BMSCC) of low BMSCC herds. By combining peptide enrichment, LC-ESI-MS/MS, and statistical analysis, we investigated the influence of subclinical mastitis and CNS infection on the milk peptidome. Quarter milk samples from clinically healthy Holstein cows were subjected to bacteriological culture (BC) and somatic cell counting (SCC) for two consecutive samplings and 28 (including 11 negatives and 17 positives) were selected for peptidomic analysis. The study identified 1363 different endogenous peptides and highlighted a significant increase of peptides in CNS-positive milk, mainly represented by casein fragments. Milk peptidome changes increased with the SCC, as also demonstrated by protein electrophoresis and densitometry. Peptides significantly different in CNS or CONTROL samples were identified and characterized. Our results indicate that subclinical mastitis by CNS can induce significant changes in the milk peptidome, opening the way to future studies for the identification of a biomarker panel as well as for the understanding of their consequences for the technological and sensorial characteristics of cow milk and dairy products. SIGNIFICANCE: This is the first investigation on the impact of subclinical CNS mastitis on the bovine milk peptidome. The peptide enrichment strategy combined with a highly sensitive MS/MS analysis enabled the compilation of a very large peptide dataset for healthy and mastitic milk. The comparison of CNS and Control samples, also considering SCC classes, highlighted several peptides with potential for understanding milk protein and peptide dynamics in subclinical mastitis, with possible implications for its detection.

This study aims to determine the untargeted lipidome of cow milk during non-staphylococcal sub-clinical mastitis. Dairy cow mastitis severely impacts the dairy industry by reducing milk yield and quality and increasing culling rate. Among the pathogens that cause bovine sub-clinical mastitis, non-aureus staphylococci (NAS) have become the most frequently isolated bacteria from milk samples. The molecular mechanisms regulating the mammary gland inflammatory responses to NAS are unclear. Although the application of system biology approaches to mastitis has provided pivotal information by investigating the transcriptome, proteome, and metabolome, the milk lipidome remains undisclosed. We determined the lipidome of 17 dairy cows with NAS sub-clinical mastitis (SM) and we compared the results with the lipidome of the milk of 13 healthy cows. The study was carried out following a liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) approach. Sixteen subclasses of lipids were identified in both groups of animals. From 2,506 measured lipids, 631 were significantly changed more than 10 folds (FDR < 0.01 in milk from cows with mastitis as compared to healthy cows. Our results point out the significant influence of NAS on the milk lipidome, contribute to the understanding of inflammatory processes in the bovine udder, and highlight potential novel biomarkers for improving mastitis diagnosis. Part of this work was carried out in “OMICs”, an advanced mass spectrometry platform established by the Università degli Studi di Milano.

The aim of this study was to assess the effect of alternate and simultaneous pulsation on milking performance (milk yield, machine stripping milk, milking duration, and average milk flow rate) of Alpine goats milked at two different system vacuum levels (38 and 42 kPa). To this end, a field study was carried out in a commercial farm of Northern Italy. A pre-experimental period was carried out and 164 goats with a similar parturition date were selected and distributed into two similar subplots of 82 goats according to their values of milk yield and milking duration. The experiment lasted 24 d and was laid out as a split-plot design. Each treatment lasted 3 days. Once every combination (2 pulsation types × 2 vacuum levels) was tested (Experiment 1), the same experimental design was repeated in reverse order (cross-over design) to control the effect of lactation progress on the results (Experiment 2). Milk traits regarding milking performance (milk yield, machine stripping milk, milking duration, and average milk flow rate) and variables related to vacuum level in the milking cluster (vacuum in the short milk tube and vacuum fluctuation in the short milk tube) were recorded during morning and afternoon milkings. Results showed that the use of alternate pulsation reduced the vacuum fluctuations and increased the average vacuum in the short milk tube. Moreover, when a higher system vacuum level (42 kPa) was set, the use of alternate pulsation reduced the milking duration. We concluded that the use of alternate pulsation offers some advantages compared to simultaneous pulsation in high milk flow conditions.

The objective of the present study was to investigate the effect of pre-milking mechanical teat stimulation on milk yield and milking performance of dairy buffaloes in early lactation. For this purpose, twenty-two healthy Italian Mediterranean buffaloes in their first to third lactation and in early lactation (

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0–60 Days In Milking (DIM), 61–120 DIM, 121–180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC &lt, 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC &gt, 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS × HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland’s health status. Results also explained how the milk EC signals’ pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors.

The objective of this study was to evaluate the response of buffaloes to automatic milking, examining the relationships between milking interval, milk production, and milking time for this species. A total of 7,550 milking records from an average of 40 buffaloes milked by an automatic milking system (AMS) were analyzed during a 3-mo experimental period at a commercial farm with Italian Mediterranean buffaloes in southern Italy. Date and time of animal identification, milk yield, milking duration, milking interval, and average milk flow rate were determined for each milking. The results were also used to predict the maximum number of milkings per day and the optimal number of buffaloes per AMS for different levels of milk production. The average interval period between 2 consecutive milkings was 10.3 h [standard deviation (SD) 3.3]. Overall, 3.4 and 25.7% of the milkings had an interval of ≤6 h or >12 h, respectively. Milking duration averaged 8.3 min per buffalo per milking (SD 2.7). The average milk flow rate was 1.3kg/min (SD 0.5) at a milk yield of 2.8kg per milking (SD 1.4). Assuming that the milking station is occupied 80% of the time, the number of milkings ranged from 136 to 152 per day and the optimal number of buffaloes per AMS ranged from 59 to 66 when the production level increased from 2 to 5kg of milk per milking. Automatic milking systems are suitable for buffalo, opening new options for the management of dairy buffalo farms.

Mastitis in small ruminants has a major effect in reducing both yield and quality of milk leading to strong economic losses. In addition, mastitis is one the most frequently cited reason for culling. In dairy cows electrical conductivity (EC) of milk has been introduced as an indicator trait for mastitis. When the measurement of electrical conductivity (EC) of milk is based on time-series analysis of historical data, comparing values from different quarters within milking, satisfactory results in mastitis detection can be achieved. On the contrary, mastitis detection models based on EC measurement of milk within milking are not available in literature for dairy goats. Aim of the study was to evaluate the usefulness of milk EC, measured within milking from halves of each udder, to predict mastitis status in dairy goats at farm level. For this purpose, a group of 8 Saanen goats was involved in a 225-day study. Two foremilk samples were daily collected during morning milking from halves of each udder for a total of 4 samples per animal/day. Results of bacteriological analyses were used to distinguish between healthy (absence of mastitis agents) and infected (presence of contagious or environmental mastitis agents) udder halves. EC was measured in milk from halves of each udder during every morning milking at 1-s intervals using four experimental milking units equipped with conductimetric sensors. The average of the 20 highest EC values (X20) and the variation of the 20 highest EC values ( σ 20 2 ) within milking were calculated for each udder halves. In addition 4 EC traits based on X20 and σ 20 2 were computed: the highest half udder X20 value within goat and milking (Max_X20), the highest half udder σ 20 2 value within goat and milking ( Max _ σ 20 2 ), the inter-udder halves ratio between the udder halves X20 values within goat and milking (IHR_X20), and the inter-udder halves ratio between udder halves σ 20 2 value within goat and milking ( IHR _ σ 20 2 ). Values for the traits were calculated for every milking throughout the whole lactation. Proc Mixed of SAS® 9.1 (SAS Institute Inc., Cary, NC, USA, 2008) was performed to test for significant differences in EC traits between healthy and infected udder halves and between healthy (absence of infected udder halves) and infected (presence of at least one infected udder half) goats. Results highlighted that the average of the 20 highest electrical conductivity half udder values recorded within milking in early lactation and the highest half udder X20 value within goat and milking in mid lactation could be potentially used in monitoring udder health of dairy goats.

Accurate milk yield measurement is important to maintain production efficiency and improve farm management: yield data enable to implement and evaluate feed rations for individual cows or groups, calculate actual lactation curves and identify potential health problems proactively. Nowadays, many electronic milk metering devices, based on different methods of measurement, are available on the market. Vacuum drop across the meter, accuracy over a wide range of flow rates and farm conditions (e.g. foaming of milk) are the key problems of currently available electronic milk meters. Aim of this project was the development of a free-flow milk meter, able to work with different flow rates and different combination of air-milk flows, with a measuring chamber essentially made by a portion of linear pipe, horizontally disposed and with no mechanical movement parts, to minimize the interferences with the milk flow and on the electric conductivity (EC), to calculate the milk yield, in order to obtain a low-cost device. A prototype was built up and laboratory and farm tests were carried out during 2004 and 2005, in order to evaluate the accuracy and reproducibility of the new device. Laboratory and farm tests were carried out following the guidelines of the International Committee for Animal Recording (ICAR). Laboratory tests showed a bias of 1.4% and a standard deviation of 5.0%, while farm tests showed a bias of -4.5% and a standard deviation of 12.7%.