Search

[Display omitted]

AbstractThirty-six multiparous Holstein mid-lactating cows were housed in 6 pens (6 cows/pen) and allocated in two groups (3 pens/group), receiving a control total mixed ration (CTR), or the same diet supplemented with a feed additive (TRT). Between 15 and 35 days from enrolment (DFE) the dry matter intake (DMI) of each pen, and the individual milk yield (MY), rumination time (RT), the compositions of milk and rumen fluid and the metabolic profile of plasma were monitored regularly. At 35 DFE, cows started a 1-week wash-up period prior to the changeover. Data were analysed with a mixed model using repeated measures. Cows receiving the additive had higher DMI and MY (p < .01), a longer RT between 22 and 29 DFE (p < .05), a higher total VFA concentration and a lower pH in the rumen fluid (p < .01 and p = .04, respectively). At 15 DFE, TRT cows had a higher concentration of lactose in their milk, and lower concentrations of BHB, ceruloplasmin and haptoglobin, paired with higher concentrations of albumin, paraoxonase and aspartate aminotransferase in their plasma (p < .01). These outcomes suggest a positive effect of the additive on increasing DMI and MY, possibly reflecting that it had a combined action on the rumen and the liver.HighlightsThe additive increased feed intake, rumination time and milk yield.It improved rumen fermentations and liver activity, and mitigated ketogenesis and inflammatory conditions.Beneficial actions of the active components on rumen and liver functions could be hypothesised.

In the present study, cheeses coagulated with different milk-clotting enzymes were investigated. In detail, calf rennet, microbial coagulant, and a kiwifruit enzymatic extract were used in three independent cheesemaking processes. Microbiological, chemical, and sensory profiles, by using E-Eye, E-Nose, and E-Tongue platforms, were evaluated. Based on microbiological data, mesophilic bacteria, Lactobacilli, and Lactococci showed similar viable count in cheeses clotted with animal rennet and with vegetable coagulant. Differently, higher value of mesophilic bacteria and lower count of both Lactobacilli and Lactococci were detected in samples obtained using microbial coagulant. Additionally, as revealed by the fatty acid composition, the kiwifruit enzymatic extract allowed to obtain a final product with improved health properties. Concerning the artificial senses, E-Eye, E-Nose, and E-Tongue, a similar sensory profile was revealed among cheeses clotted using the kiwifruit enzymatic extract and the calf rennet whereas a different sensory profile was observed in microbial clotted cheeses. This work demonstrated that the kiwifruit extract is a valid coagulant alternative to calf rennet and microbial coagulant.

AbstractAn unsupervised machine learning approach (ML) of rumination time (RT) data was used to evaluate metabolic and lactation performance in a cohort of Simmental dairy cows (29) around parturition. The k-means clustering (unsupervised ML) was used to generate clusters based on RT (Hr-Tags; SCR by Allflex) over 24 h from 1 to 7-d post-calving. Two large clusters were obtained: high (High-RT, n = 13) and low (Low-RT, n = 12). Milk production was recorded until 42 d in lactation and milk samples collected weekly. Blood samples were collected from −21 to +28 d relative to calving. Data were subjected to PROC MIXED of SAS. Compared with Low-RT, High-RT cows had lower plasma BHB (3, 7, and 14 d), haptoglobin (3 and 7 d), and non-esterified fatty acids (1 and 3 d). High-RT cows had greater tocopherol from 3 to 28 d, fructosamine and albumin at 7 d, and retinol at 3 and 7 d after calving than Low-RT cows. Compared with multiparous Low-RT, milk production was greater in multiparous High-RT cows, but no differences were detected for primiparous. Only for multiparous, High-RT cows displayed a greater LFI than Low-Rt cows. The close relationship between RT and the physiological state at the onset of lactation supports the use of RT as an indicator of metabolic and inflammatory adaptations to the negative energy balance of cows after parturition. At the farm level, these outcomes provide information to farmers that can be helpful in management decisions for cow health, complementing traditional methods.HIGHLIGHTSThe unsupervised ML applied was able to group cows with different RT increase rates after calving based on differences in plasma biomarkers of energy metabolism, inflammatory response, and liver functionality, particularly in multiparous cows.A quicker increase in RT after calving was associated with a lower inflammatory response, lower lipid mobilisation, and greater milk production.At the farm level, the fine-tuning of specific algorithm in the actual sensors considering the rate of increase of RT after calving can be helpful in management decisions for cow health, complementing traditional methods to better monitor early lactation dairy cows.