Journal of Dairy Science最新文献

Lacticaseibacillus rhamnosus Probio-M9 (Probio-M9), a probiotic strain sourced from healthy breast milk, is recognized for its resilience to gastric and bile acids, along with its potential health benefits for infants. Its unique origin may influence its metabolic properties and effectiveness, garnering increasing interest within the scientific community in recent years. However, research on its efficacy as a starter culture for milk fermentation and the associated metabolic shifts remains limited. This study aimed to track the dynamic metabolomic changes of Probio-M9 during the fermentation process. We utilized Probio-M9 as the sole inoculant for milk fermentation, collecting samples at 1.5 to 3-h intervals to monitor the fermentation progression. Moreover, the metabolomics changes of the fermented milk were investigated after a 28-d storage period to evaluate post-storage stability. Metabolite profiles were generated using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS^E). A total of 34 representative differential metabolites were identified, primarily comprising peptides and saccharides, followed by acids, amino acids, alcohols, aldehydes, ketones, and intermediate metabolites. Notably, substantial alterations in metabolite levels were observed between 10.5 to 12 h into the fermentation process. Functional metabolites like syringaldehyde, leucic acid, and gentianose accumulated toward the end of fermentation, while peptides emerged as the main differential metabolites after the 28-d storage period. The study offers novel insights into the metabolic dynamics of Probio-M9 during fermentation and storage, which may inform the optimization of Probio-M9 fermented milk production processes. However, it is important to note that the focus of this study on a single starter strain may limit the generalizability of these findings.

Targeted reproductive management (TRM) aims to improve the fertility efficiency of the dairy herd by applying group-level management strategies based on expected reproductive performance. Key to the utility of TRM is the accuracy with which an animal's reproductive performance can be predicted. Automatic milking systems (AMS) allow for the collection of data relating to milk quantity, quality, and robot visit behavior throughout the transition period. In addition to this, auxiliary data sources such as rumination and activity monitors, as well as historical cow-level data are often readily available. The utility of this data for the prediction of fertility has not been previously explored. The objective of this study was first, to assess the accuracy with which the likelihood of expression of oestrus between 22 and 65 d in milk (DIM) and conception to first insemination between 22 and 80 DIM could be predicted using data collected by AMS from 1 to 21 DIM. Our second objective was to assess the change in model performance following the addition of 2 auxiliary data sources. Using data derived solely from the AMS (RBT data set) a binary random forest classification model was constructed for both outcomes of interest. The performance of these models was compared with models constructed using AMS data in conjunction with 2 auxiliary sources (RBT+ data set). Expression of oestrus was classified with an area under the receiver operator curve (AUC-ROC) of 0.6 and 0.65, conception to first insemination with an AUC-ROC of 0.56 and 0.62 for the RBT and RBT+ data sets respectively. No statistically significant improvement in classification accuracy was achieved by the addition of auxiliary data sources. This is the first study to report the utility of data collected by AMS for the prediction of reproductive performance. Though the performance described is comparable with previously reported models, their utility for the implementation of TRM is limited by poor classification accuracy within key sub-groups. Of note within this study is the failure of the addition of auxiliary data sources to increase the accuracy of prediction over models built using AMS data alone. We discuss the advantages and limitations the integration of additional data sources imposes on model training and deployment and suggest alternative methods to improve performance while preserving model parsimony.

Weibull proportional hazards model was used to identify the non-genetic effects that affect length of productive life (LPL) of the Italian Mediterranean Buffalo (IMB). Data were provided by the Italian National Association of Buffalo Breeders (ANASB) and included records of reproductive, productive, and linear type traits from 59,943 buffalo with first calving from 2002 to 2019. Data were divided into 4 geographical regions to determine if the relationship between the investigated effects and LPL varied by region. LPL was defined as the number of days from the first calving to culling for those buffaloes that were culled (uncensored) or to the date of the last test-day for those that are still alive (censored). The Weibull model included time-dependent effects of herd-year-season of calving, parity and stage of lactation, production as within-herd deviations, as well as time-independent effects of age at first calving, year of birth, type of reproduction (natural mating vs artificial insemination), classifier and ten linear type traits. The average duration of productive life was 1,604 days. Both year of birth and production level had a significant effect on culling risk. Moreover, culling risk decreased linearly across parities. The reference point for age at first calving relative to other classes was set at 35 months. A significant relationship between linear type traits and relative culling risk was also observed, being highest for buffaloes with the lowest linear scores. The results show that the Weibull model provides consistent and robust risk estimates. Therefore, this model would be recommended for future implementation of the first genetic evaluation of LPL in the Italian Mediterranean Buffalo.

Elevated concentrations of free fatty acids (FFA) in bulk tank milk are a milk quality concern in the dairy industry. Hydrolysis of triacylglycerols (TAG) yields FFA, and milk with ≥ 1.20 mmol FFA/ 100 g of milk fat is associated with undesirable characteristics, such as off-flavor, rancidity, reduced foam stability, and inhibited cheese-milk coagulation. Research on FFA is limited and absent in North America, but research out of European regions indicates that high FFA are multifactorial. This study aimed to identify farm-level FFA risk factors in Canadian dairy herds. A cross-sectional study was conducted on 293 Canadian dairy farms in Ontario (n = 238) and British Columbia (n = 55). Over 2 years, selected farms were visited once to complete a survey, assess milking systems, and gather data on the diet of lactating cows. Bulk tank FFA values for each farm 15 d before and 15 d after the research visit were obtained from the corresponding province's milk marketing board. Using these values, a monthly FFA average was calculated for each farm and used as the outcome variable. Seventy-one farms were tie stall, 109 were free-stall with milking parlors, and 113 were free-stall with automated milking systems (AMS). The mean bulk tank FFA was 0.84 mmol/ 100 g of fat (SD = 0.40, range 0.26 to 3.67), and 10% (n = 29) of herds had an elevated monthly average FFA (≥1.20 mmol/ 100 g of fat). In the final multivariable linear regression model, milking frequency ≥3x/d (times per day) compared with <3x/d was associated with a greater FFA concentration in AMS (β = 0.27, 95% CI: 0.12, 0.41) and tie stall milking systems (β = 1.17, 95% CI: 0.76-1.59). Regardless of milking frequency, none of the parlor farms visited had FFA ≥1.20 mmol/ 100 g of fat. For farms milking ≥3x/d, not changing the milk filter at least 2x/d was associated with greater FFA concentrations (β = 0.27, 95% CI: 0.10, 0.44). The absence of pre-cooling was also associated with higher FFA levels (β = 0.16, 95% CI: 0.02, 0.3). The final model adjusted R² of 29% indicates that more factors associated with bulk tank milk FFA still need to be identified, which may include seasonal, milk composition, and individual cow factors.

Among various farm procedures, an appropriate milking routine is crucial for reducing mastitis and enhancing milk safety and quality. However, inadequate compliance with milking routines remains a primary factor contributing to variability of milk quality among herds. Training farm workers is essential for ensuring compliance with milking routines, which directly impacts milk quality and udder health. In this study, we aimed to assess the impact of a training session on dairy farm workers' understanding of the milking routine, behavioral changes during milking, and subsequent effects on milk quality and udder health. Farm workers (n = 112) from 16 commercial dairy farms in Michigan and Ohio participated in the project. The training session was delivered using an active learning approach prioritizing group discussions, and focused on the pathogenesis, identification, and risk factors for mastitis, and the rationale and importance of each step in the milking routine. Pre- and post-training evaluations were conducted to measure changes in knowledge, parlor evaluations were performed to determine behavioral change, and data from clinical mastitis cases and bulk tank somatic cell count were used to estimate udder health and milk quality. Segmented and linear mixed regression analyses were used to assess the impact of the training intervention. Participants were mostly milking technicians (61.1%) with 70% of them having less than 1 year of experience and a median tenure in their jobs of 9 mo. Knowledge of the milking routine increased from 49.3% to 67.6% of correct answers after training. Based on parlor evaluations, the pre-milking disinfectant contact time increased by 9 s per cow. The percentage of milkings with inadequate preparation time decreased from 69% (before training) to 48% (after training). The proportion of insufficient teat coverage during post-milking disinfection decreased from 9.8% (77 out of 782) before training to 5.9% (34 out of 572) after training. There was a significant increase in clinical mastitis cases during the immediate week after the training. Additionally, the upward trend in bulk tank somatic cell count was halted. Our results suggest an improvement in overall mastitis identification and milk quality, underscoring the importance of targeted training programs for farm workers in improving milking routine compliance, enhancing milk quality, and promoting better udder health.

Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, adulterants in milk can result in severe health risks, potentially leading to fatal diseases. Detecting and categorizing milk adulteration is crucial for consumer safety and the dairy industry. This research is divided into 2 breakthroughs, destructive and non-destructive methods. In the destructive method, the Lactoscan system was used for qualitative analysis: (Solid Not Fat (SNF), density, fat, lactose, conductivity, solids, protein, temperature, and pH level). The research also examines non-distractive hyperspectral imaging (HSI) through HSI Specim Fx-10 (397-1003 nm) analysis to detect various phases of milk adulteration for accurate and user-friendly imaging-based adulterants detection and categorization. Preprocessing involves radiometric correction, image resizing, region of interest (ROI) selection for feature extraction, and empirical line method (ELM) to calculate spectral reflectance signature. Machine learning techniques (Logistic Regression (LRs), Decision Tree (DTs), Support Vector Machine (SVMs), and Linear Discriminant Analysis (LDAs)), are employed, with LDA excelling in adulteration identification by learning the spectral signatures. These algorithms are trained and validated using a developed milk adulteration data set. Training, testing, and validation accuracy, precision, recall, F1-score, Kappa, and Matthew's correlation coefficient (MCC) metrics showcase the effectiveness of the proposed pipeline, outclassing numerous state-of-the-art approaches with a validation accuracy of 100%. In conclusion, this study established a multiclass model capable of detecting milk adulterant behavior, showing significant practical application for milk quality assessment.