Effects of supplementation with a phytobiotics-rich herbal mixture on performance, udder health, and metabolic status of Holstein cows with various levels of milk somatic cell counts

Determination of somatic cell count (SCC) is used worldwide in dairy practice to describe the hygienic status of the milk and the udder health of cows. When SCC is tested on a quarter level to detect single quarters with high SCC levels of cows for practical reasons, mostly foremilk samples after prestimulation (i.e. cleaning of the udder) are used. However, SCC is usually different in different milk fractions. Therefore, the goal of this study was the investigation of the use of foremilk samples for the estimation of total quarter SCC. A total of 378 milkings in 19 dairy cows were performed with a special milking device to drain quarter milk separately. Foremilk samples were taken after udder stimulation and before cluster attachment. SCC was measured in foremilk samples and in total quarter milk. Total quarter milk SCC could not be predicted precisely from foremilk SCC measurements. At relatively high foremilk SCC levels (>300×103 cells/ml) foremilk SCC were higher than total quarter milk. At around (50–300)×103 cells/ml foremilk and total quarter SCC did not differ considerably. Most interestingly, if foremilk SCC was lower than 50×103 cells/ml the total quarter SCC was higher than foremilk SCC. In addition, individual cows showed dramatic variations in foremilk SCC that were not very well related to total quarter milk SCC. In conclusion, foremilk samples are useful to detect high quarter milk SCC to recognize possibly infected quarters, only if precise cell counts are not required. However, foremilk samples can be deceptive if very low cell numbers are to be detected.

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Fat and protein contents, acidity and somatic cell counts in bulk milk of Holstein cows in the Khorasan Razavi Province, Iran

International Journal of Dairy Technology ◽

10.1111/j.1471-0307.2008.00451.x ◽

2009 ◽

Vol 62 (1) ◽

pp. 19-26 ◽

Cited By ~ 8

Author(s):

MASOUD NAJAF NAJAFI ◽

SEYED ALI MORTAZAVI ◽

ARASH KOOCHEKI ◽

JAFAR KHORAMI ◽

BOULBABA REKIK

Keyword(s):

Somatic Cell ◽

Holstein Cows ◽

Somatic Cell Counts ◽

Cell Counts ◽

Bulk Milk

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Effect of dietary supplementation of poly-herbal mixture and butyric acidon milk production, milk quality and somatic cell counts ofpostpartum Murrah buffaloes

Indian Journal of Animal Research ◽

10.18805/ijar.9646 ◽

2016 ◽

Author(s):

Subhash Chandra ◽

P. S. Oberoi ◽

M. Bhakat ◽

R. K. Yogi ◽

Archana Yadav ◽

...

Keyword(s):

Somatic Cell ◽

Milk Yield ◽

Butyric Acid ◽

Post Partum ◽

Dietary Supplementation ◽

Control Group ◽

Somatic Cell Counts ◽

Herbal Mixture ◽

Murrah Buffaloes ◽

Cell Counts

This study evaluated the effect of dietary supplementation of poly-herbal mixture and butyric acid on the milk yield, milk quality and somatic cell counts in Murrah buffaloes up to 90 days of lactation. Thirty six Murrah buffaloes were divided into four groups viz.; T0 control (n=9; Body Weight (BW)=666.22±31.30 kg, Most Probable Production Ability (MPPA)=1834 kg, Parity (P)=3.44) without any supplementation, T1 (n=9; BW=661.89±42.13 kg, MPPA=1860 kg, P=3.56) poly-herbal mixture, T2 (n=9; BW=664.22±14.81, MPPA=1907 kg, P=3.33) poly-herbal mixture + butyric acid and T3 (n=9; BW=672.00±17.97, MPPA=1891 kg, P=3.44) butyric acid on the basis of MPPA and P. In T1 group poly-herbal mixture was supplemented for seven days postpartum and in T2 group poly-herbal mixture was supplemented for seven days post-partum along with butyric acid for 30 days pre-partum and 30 days post-partum. In T3 group only butyric acid was supplemented for 30 days during pre-partum and 30 days post-partum periods. The results depicted that milk yield (T1-9.91±1.10, T2-9.72±1.18, T3-9.47±1.38 and T0-8.62±0.97 kg/day), fat corrected milk yield (6%) (T1-18.23±2.03, T2-18.45±2.28, T3-17.79±2.59 and T0-15.59±1.77 kg/day) and average total solid (T1-17.34±0.3, T2-17.80±0.40, T3-17.43±0.29 and T0-6.74±0.25) were significantly higher (P<0.05) in supplemented (T1, T2 and T3) groups as compared to control group (T0). No significant change in milk protein, lactose and SNF but the values was on higher side in treatment group. Somatic cell count (SCC) was significantly (P<0.05) lower in poly-herbal mixture and butyric acid supplemented groups as compared to control group. From the present study it was concluded that poly-herbal mixture and butyric acid supplementation during transition period has beneficial effect in improving milk production and udder health.

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A pathogen-specific approach towards udder health management in dairy herds: Using culture and somatic cell counts from routine herd investigations

Onderstepoort Journal of Veterinary Research ◽

10.4102/ojvr.v83i1.1146 ◽

2016 ◽

Vol 83 (1) ◽

Cited By ~ 3

Author(s):

Inge-Marié Petzer ◽

Joanne Karzis ◽

Edward F. Donkin ◽

Edward C. Webb

Keyword(s):

Somatic Cell ◽

Health Management ◽

Bacterial Species ◽

False Negative ◽

Dairy Herds ◽

Udder Health ◽

Somatic Cell Counts ◽

Milk Samples ◽

Negative Results ◽

Cell Counts

A dedicated udder health diagnostic programme was developed and used over a 15-year period in South Africa to analyse milk samples based on microbiological and cytological patterns within various groups and for individual cows and udder quarters in dairy herds. These pathogen-specific analyses are utilised for pro-active improvement and management of udder health in South African commercial dairy herds. The programme acts as a monitoring tool and identifies management areas at risk and individual cows with udder disease and uses both quarter and composite milk samples. Intra-mammary infection (IMI) is a dynamic situation and depending on the time a milk sample is taken, false-negative results may be obtained. A new IMI and an infection that is curing may both have low somatic cell counts (SCCs), masking the true bacterial status. SCC in individual infected udder quarters may differ greatly depending on the causative bacterial species, its pathogenicity, the host immune status and the environmental factors involved. A pathogen-specific udder health approach was followed with repeated herd tests to take account of these udder health dynamics. The results of the herd IMI investigation are applied in practice to assist veterinarians, udder health consultants and managers to make informed and specific detailed decisions at both a herd and on an individual cow basis regarding udder health.

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Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Scientific Reports ◽

10.1038/s41598-021-93056-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tania Bobbo ◽

Stefano Biffani ◽

Cristian Taccioli ◽

Mauro Penasa ◽

Martino Cassandro

Keyword(s):

Neural Network ◽

Machine Learning ◽

Health Status ◽

Somatic Cell ◽

Somatic Cell Count ◽

Machine Learning Algorithms ◽

Udder Health ◽

Somatic Cell Counts ◽

Machine Learning Methods ◽

Cell Counts

AbstractBovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow’s milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.

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