scholarly journals Effect of Udder Health Status and Lactation Phase on the Characteristics of Sardinian Ewe Milk

2004 ◽  
Vol 87 (8) ◽  
pp. 2401-2408 ◽  
Author(s):  
L. Bianchi ◽  
A. Bolla ◽  
E. Budelli ◽  
A. Caroli ◽  
C. Casoli ◽  
...  
Keyword(s):  
Health Status ◽  
Udder Health ◽  
Lactation Phase

scholarly journals Infrared thermography: A potential noninvasive tool to monitor udder health status in dairy cows

2016 ◽  
Vol 9 (10) ◽  
pp. 1075-1081 ◽  
Author(s):  
M. Sathiyabarathi ◽  
S. Jeyakumar ◽  
A. Manimaran ◽  
G. Jayaprakash ◽  
Heartwin A. Pushpadass ◽  
...  
Keyword(s):  
Health Status ◽  
Dairy Cows ◽  
Infrared Thermography ◽  
Udder Health

Evaluation of the udder health status in subclinical mastitis affected dairy cows through bacteriological culture, somatic cell count and thermographic imaging

2015 ◽  
Vol 18 (4) ◽  
pp. 799-805 ◽  
Author(s):  
A. Bortolami ◽  
E. Fiore ◽  
M. Gianesella ◽  
M. Corrò ◽  
S. Catania ◽  
...  
Keyword(s):  
Health Status ◽  
Dairy Cows ◽  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Subclinical Mastitis ◽  
Bacteriological Culture ◽  
Udder Health ◽  
Streptococcus Uberis ◽  
Thermographic Imaging

Abstract Subclinical mastitis in dairy cows is a big economic loss for farmers. The monitoring of subclinical mastitis is usually performed through Somatic Cell Count (SCC) in farm but there is the need of new diagnostic systems able to quickly identify cows affected by subclinical infections of the udder. The aim of this study was to evaluate the potential application of thermographic imaging compared to SCC and bacteriological culture for infection detection in cow affected by subclinical mastitis and possibly to discriminate between different pathogens. In this study we evaluated the udder health status of 98 Holstein Friesian dairy cows with high SCC in 4 farms. From each cow a sample of milk was collected from all the functional quarters and submitted to bacteriological culture, SCC and Mycoplasma spp. culture. A thermographic image was taken from each functional udder quarter and nipple. Pearson’s correlations and Analysis of Variance were performed in order to evaluate the different diagnostic techniques. The most frequent pathogen isolated was Staphylococcus aureus followed by Coagulase Negative Staphylococci (CNS), Streptococcus uberis, Streptococcus agalactiae and others. The Somatic Cell Score (SCS) was able to discriminate (p<0.05) cows positive for a pathogen from cows negative at the bacteriological culture except for cows with infection caused by CNS. Infrared thermography was correlated to SCS (p<0.05) but was not able to discriminate between positive and negative cows. Thermographic imaging seems to be promising in evaluating the inflammation status of cows affected by subclinical mastitis but seems to have a poor diagnostic value.

scholarly journals First Evaluation of Infrared Thermography as a Tool for the Monitoring of Udder Health Status in Farms of Dairy Cows

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 862 ◽  
Author(s):  
Mauro Zaninelli ◽  
Veronica Redaelli ◽  
Fabio Luzi ◽  
Valerio Bronzo ◽  
Malcolm Mitchell ◽  
...  
Keyword(s):  
Health Status ◽  
Dairy Cows ◽  
Infrared Thermography ◽  
Udder Health

The effect of dry cow antibiotic with and without an internal teat sealant on udder health during the first 100 d of lactation: a field study with matched pairs

2012 ◽  
Vol 79 (4) ◽  
pp. 477-484 ◽  
Author(s):  
Katja Mütze ◽  
Wilfried Wolter ◽  
Klaus Failing ◽  
Bärbel Kloppert ◽  
Heinz Bernhardt ◽  
...  
Keyword(s):  
Health Status ◽  
Field Study ◽  
Post Partum ◽  
Clinical Mastitis ◽  
Intramammary Infections ◽  
Udder Health ◽  
California Mastitis Test ◽  
Promising Tool ◽  
Antibiotic Group ◽  
Long Acting

The objective of this field study was to compare the udder health status as well as the clinical mastitis rate during the first 100 d of lactation in cows that received long-acting dry cow antibiotic alone (group AB) or in combination with an internal teat sealant (group AB + OS). The study was conducted during a 9-month period and included 136 Holstein cows from 12 dairy farms in Hessia, Germany. Between days 1 and 5 after calving a California mastitis test (CMT) was performed. Milk-samples were collected for bacteriological culture before drying off, between days 6 and 14 and days 35 and 56 of lactation. Additionally the cows were monitored for the occurrence of clinical mastitis events until 100 d post partum. Within the 12 herds cow-pairs were formed on the basis of age, milk yield and SCC. A cow-pair consisted of one cow from group AB and one cow from group AB + OS. For statistical analysis within every cow-pair one quarter that has been dried off with internal teat sealant and dry cow antibiotic (group AB + OS) was compared with one quarter that has been dried off with dry cow antibiotic (group AB) alone. As criterion for the matching process of udder quarters the cytobacteriological udder health status before drying off was used. A total of 544 quarters (136 cows) were used in this analysis. In the first 5 d after calving, group AB had significantly more quarters with a positive CMT reaction than group AB + OS (85 vs. 57; P <0·001), and in the first 100 d of lactation, group AB had more quarters with clinical mastitis than group AB + OS (25 vs. 15; P = 0·03). In the time periods 6–14 and 35–56 d of lactation, there were fewer quarters in group AB + OS populated with Corynebacterium spp. (days 6–14, P = 0·05; days 35–56, P = 0·02) and aesculin-positive streptococci (days 35–56, P = 0·02). The internal teat sealant was a promising tool for the prevention of new intramammary infections (IMI) of dry cows with environmental udder pathogens as expressed during early lactation.

scholarly journals Influence of some factors on milk flow and udder health status in two different cattle breeds

2016 ◽  
Vol 19 (Special issue) ◽  
pp. 142-144
Author(s):  
Eva Strapakova ◽  
Peter Strapák ◽  
Iveta Szencziová
Keyword(s):  
Health Status ◽  
Udder Health ◽  
Cattle Breeds ◽  
Milk Flow

Use of milk amyloid A in the diagnosis of subclinical mastitis in dairy ewes

2013 ◽  
Vol 80 (4) ◽  
pp. 496-502 ◽  
Author(s):  
Arianna Miglio ◽  
Livia Moscati ◽  
Gabriele Fruganti ◽  
Michela Pela ◽  
Eleonora Scoccia ◽  
...  
Keyword(s):  
Health Status ◽  
Subclinical Mastitis ◽  
Farm Income ◽  
Control Group ◽  
Bacteriological Examination ◽  
Udder Health ◽  
Elisa Kit ◽  
Amyloid A ◽  
Milk Samples ◽  
Dairy Ewes

Subclinical mastitis (SM) is one of the most important diseases affecting dairy ewes worldwide, with negative impact on the animal health, farm income and public health. Animals with SM often remain untreated because the disease may not be revealed. Increase in somatic cell count (SCC) and positive bacteriology for mastitis pathogens in milk samples are indicative of SM but the evidence of only one of these alterations must suggest an uncertain SM (UM). UM is defined when positive bacteriological examination (Latent-SM) or SCC>500 000 cells/ml (non-specific-SM) are detected in milk. Nevertheless, SCC and bacteriological examination are expensive, time consuming and are not yet in use at the farm level in dairy ewes. Recently, a sensitive acute phase protein, amyloid A, displaying multiple isoforms in plasma and different body fluids including mammary secretion (milk amyloid A-MAA), has been investigated as a marker of mastitis in cows and, in a few studies, in sheep. The aim of this trial was to compare the concentration of MAA of single udder-halves in ewes with healthy udder-halves (HU-control group) and naturally occurring subclinical mastitis, both confirmed (SM group) and uncertain (UM groups: Latent-SM and non-specific-SM), for monitoring udder health. The reliability of a specific ELISA kit for the measurement of MAA was also tested. During a 3-month trial period, 153 udder halves were assigned to the experimental groups based on their health status: 25 with SM, 40 with UM (11 with latent-SM and 29 with non-specific-SM) and 88 HU. SCC and bacteriological analysis were performed to establish the control and subclinical mastitis groups. MAA concentrations in milk samples were measured using a specific commercially milk ELISA kit. The data were submitted to statistical analysis. Significant (P<0·05) differences among the groups SM, non-specific-SM and HU were detected with the SM having the highest level and HU the lowest. MAA concentration is affected by the udder health status and is a useful indicator of subclinical mastitis and increased SCC in sheep.

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

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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