scholarly journals Health Status and Productive Performance of Somatic Cell Cloned Cattle and Their Offspring Produced in Japan

2008 ◽  
Vol 54 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Shinya WATANABE ◽  
Takashi NAGAI
Keyword(s):  
Health Status ◽  
Somatic Cell ◽  
Productive Performance

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.

Effects of an organic source of copper, manganese and zinc on dairy cattle productive performance, health status and fertility

2011 ◽  
Vol 164 (3-4) ◽  
pp. 191-198 ◽  
Author(s):  
Andrea Formigoni ◽  
Mattia Fustini ◽  
Laura Archetti ◽  
Stephen Emanuele ◽  
Charles Sniffen ◽  
...  
Keyword(s):  
Health Status ◽  
Dairy Cattle ◽  
Productive Performance ◽  
Copper Manganese ◽  
Organic Source

Effect of dietary level of protein and fiber on the productive performance and health status of piglets1

2009 ◽  
Vol 87 (11) ◽  
pp. 3569-3577 ◽  
Author(s):  
R. G. Hermes ◽  
F. Molist ◽  
M. Ywazaki ◽  
M. Nofrarías ◽  
A. Gomez de Segura ◽  
...  
Keyword(s):  
Health Status ◽  
Productive Performance ◽  
Dietary Level

Zinc oxide at low supplementation level improves productive performance and health status of piglets

2012 ◽  
Vol 90 (suppl_4) ◽  
pp. 436-438 ◽  
Author(s):  
J. Morales ◽  
G. Cordero ◽  
C. Piñeiro ◽  
S. Durosoy
Keyword(s):  
Zinc Oxide ◽  
Health Status ◽  
Productive Performance

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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