The phenotypic relations between somatic cell counts and milk constituents of clinical and non-clinical mastitis milk of dairy cows

This study was carried out to determine the prevalence of coagulase-negative staphylococci in clinical and subclinical mastitis in commercial and small-scale farms in Zimbabwe. Thirty five quarter milk samples from clinical mastitis cases and 371 quarter milk samples from cows with subclinical mastitis were cultured for bacterial pathogens. The most frequent pathogens isolated in clinical mastitis were the enteric bacteria (31.4 %), followed by coagulase negative staphylococci (22.9 %) and then Staphylococcus aureus (17.1 %), whereas in subclinical mastitis S. aureus (34.2 %) and coagulase-negative staphylococci were (33.2 %) the most common. Bacillus species were only isolated in milk samples from subclinical mastitis. Coagulase-negative staphylococci were observed in mixed infections with other bacteria in only 2.2 % of the 406 milk samples from clinical and subclinical mastitis where they were isolated together with Bacillus species in 6 of the 9 mixed infection cases. About 95 % of the milk samples from which 131 coagulase-negative staphylococci were isolated had correspondingly high somatic cell counts. The coagulase-negative staphylococci isolated most frequently were S. chromogenes (7.9 %), S. epidermidis (7.4 %) and S. hominis (5.9 %). They were all associated with high somatic cell counts. All the coagulase-negative staphylococci isolates were susceptible to cloxacillin and erythromycin, and more than 90 %of the isolates were susceptible to neomycin, penicillin and streptomycin. The highest resistance was to tetracycline (17.6 %), followed by lincomycin (13.7 %). About 8 % of the isolates were resistant to both penicillin and streptomycin.

Download Full-text

Prediction of total quarter milk somatic cell counts based on foremilk sampling

Journal of Dairy Research ◽

10.1017/s0022029909004166 ◽

2009 ◽

Vol 76 (3) ◽

pp. 326-330 ◽

Cited By ~ 5

Author(s):

Olga Wellnitz ◽

Marcus G Doherr ◽

Marta Woloszyn ◽

Rupert M Bruckmaier

Keyword(s):

Dairy Cows ◽

Somatic Cell ◽

Cell Count ◽

Somatic Cell Count ◽

Practical Reasons ◽

Udder Health ◽

Somatic Cell Counts ◽

Cell Numbers ◽

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.

Download Full-text

Intramammary infections and risk factors for clinical mastitis in herds with low somatic cell counts in bulk milk

Veterinary Record ◽

10.1136/vr.125.15.393 ◽

1989 ◽

Vol 125 (15) ◽

pp. 393-396 ◽

Cited By ~ 27

Author(s):

Y. Schukken ◽

D. Van de Geer ◽

F. Grommers ◽

J. Smit ◽

A. Brand

Keyword(s):

Risk Factors ◽

Somatic Cell ◽

Clinical Mastitis ◽

Intramammary Infections ◽

Somatic Cell Counts ◽

Cell Counts ◽

Bulk Milk

Download Full-text

A multicomponent herbal feed additive improves somatic cell counts in dairy cows ‐ a two stage, multicentre, placebo‐controlled long‐term on‐farm trial

Journal of Animal Physiology and Animal Nutrition ◽

10.1111/jpn.13297 ◽

2020 ◽

Vol 104 (2) ◽

pp. 439-452 ◽

Cited By ~ 1

Author(s):

Michael Walkenhorst ◽

Florian Leiber ◽

Ariane Maeschli ◽

Alexandra N. Kapp ◽

Anet Spengler‐Neff ◽

...

Keyword(s):

Dairy Cows ◽

Somatic Cell ◽

Feed Additive ◽

Two Stage ◽

Somatic Cell Counts ◽

Cell Counts ◽

On Farm

Download Full-text

Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle

The Scientific World JOURNAL ◽

10.1155/2013/603897 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 8

Author(s):

Nazira Mammadova ◽

İsmail Keskin

Keyword(s):

Support Vector Machine ◽

Dairy Cattle ◽

Somatic Cell ◽

Control Period ◽

Subclinical Mastitis ◽

Clinical Mastitis ◽

Support Vector ◽

Cross Sectional ◽

Somatic Cell Counts ◽

Cell Counts

This study presented a potentially useful alternative approach to ascertain the presence of subclinical and clinical mastitis in dairy cows using support vector machine (SVM) techniques. The proposed method detected mastitis in a cross-sectional representative sample of Holstein dairy cattle milked using an automatic milking system. The study used such suspected indicators of mastitis as lactation rank, milk yield, electrical conductivity, average milking duration, and control season as input data. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the control period. Cattle were judged to be healthy or infected based on those somatic cell counts. This study undertook a detailed scrutiny of the SVM methodology, constructing and examining a model which showed 89% sensitivity, 92% specificity, and 50% error in mastitis detection.

Download Full-text