scholarly journals Mastitis diagnosis in ten Galician dairy herds (NW Spain) with automatic milking systems

2015 ◽  
Vol 13 (4) ◽  
pp. e0504 ◽  
Author(s):  
Angel Castro ◽  
Jose M. Pereira ◽  
Carlos Amiama ◽  
Javier Bueno
Keyword(s):  
Detection System ◽  
Operational Reliability ◽  
Clinical Mastitis ◽  
Standard Test ◽  
Primary Objective ◽  
Dairy Herds ◽  
Nw Spain ◽  
Streptococcus Uberis ◽  
Automatic Milking ◽  
Automatic Milking Systems

<p>Over the last few years, the adoption of automatic milking systems (AMS) has experienced significant increase. However, hardly any studies have been conducted to investigate the distribution of mastitis pathogens in dairy herds with AMS. Because quick mastitis detection in AMS is very important, the primary objective of this study was to determine operational reliability and sensibility of mastitis detection systems from AMS. Additionally, the frequency of pathogen-specific was determined. For this purpose, 228 cows from ten farms in Galicia (NW Spain) using this system were investigated. The California Mastitis Test (CMT) was considered the gold-standard test for mastitis diagnosis and milk samples were analysed from CMT-positive cows for the bacterial examination. Mean farm prevalence of clinical mastitis was 9% and of 912 milk quarters examined, 23% were positive to the AMS mastitis detection system and 35% were positive to the CMT. The majority of CMT-positive samples had a score of 1 or 2 on a 1 (lowest mastitis severity) to 4 (highest mastitis severity) scale. The average sensitivity and specificity of the AMS mastitis detection system were 58.2% and 94.0% respectively being similar to other previous studies, what could suggest limitations for getting higher values of reliability and sensibility in the current AMSs. The most frequently isolated pathogens were <em>Streptococcus dysgalactiae</em> (8.8%), followed by<em> Streptococcus uberis </em>(8.3%) and<em> Staphylococcus aureus </em>(3.3%).<em> </em>The relatively high prevalence of these pathogens indicates suboptimal cleaning and disinfection of teat dipping cups, brushes and milk liners in dairy farms with AMS in the present study.</p>

scholarly journals Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems

2010 ◽  
Vol 93 (6) ◽  
pp. 2559-2568 ◽  
Author(s):  
W. Steeneveld ◽  
L.C. van der Gaag ◽  
W. Ouweltjes ◽  
H. Mollenhorst ◽  
H. Hogeveen
Keyword(s):  
False Positive ◽  
Clinical Mastitis ◽  
True Positive ◽  
Automatic Milking ◽  
Automatic Milking Systems

Effect of lameness on dairy cows’ visits to automatic milking systems

2008 ◽  
Vol 88 (1) ◽  
pp. 1-8 ◽  
Author(s):  
T. F. Borderas ◽  
A. Fournier ◽  
J. Rushen ◽  
A. M. B. de Passillé
Keyword(s):  
Dairy Cows ◽  
Low Frequency ◽  
Standard Test ◽  
Economic Consequences ◽  
Welfare Problem ◽  
Numerical Rating ◽  
Automatic Milking ◽  
Automatic Milking Systems ◽  
On Farm ◽  
Automated Methods

Lameness is a major welfare problem for dairy cows and has important economic consequences. On-farm detection of lameness is difficult, and automated methods may be useful for early diagnoses. Lameness may reduce the efficiency of automated milking systems (AMS) if lame cows are less willing to visit the automatic milking unit voluntarily and poor attendance at milking units may help detect lameness. To determine whether a low frequency of visits in an AMS could serve as an indicator of lameness, data on the frequency of visits of 578 cows in 12 AMS on eight farms were collected. From each AMS, 22 cows (from a mean of 54 cows per AMS), were classified as either the 11 highest visitors or the 11 lowest visitors based on the total number of visits to the milking unit. These selected cows (n= 256) were videotaped while walking in a standard test area and their gait scored on a 5-point scale (1 = sound 5 = severely lame). Intra- and inter-observer reliability values between and within observers were high for gait scoring. Significant differences in gait scores between the two groups of cows (P< 0.05) were found in 9 out of 12 AMS: high-visiting cows had better gait scores than low-visiting cows. Four percent of high visitors were classified as slightly lame and 32% of low visitors were classified as either slightly or severely lame. The overall numerical rating score was the most effective in discriminating between high and low visitors, and scoring each individual component of gait did not greatly improve discrimination between the two groups of cows. The frequency that dairy cows visit an AMS is related to their locomotory ability, and data from the AMS may help in the early detection of lameness. Key words: Cattle, lameness, automatic milking systems, behaviour, gait scoring

scholarly journals Molecular Detection and Sensitivity to Antibiotics and Bacteriocins of Pathogens Isolated from Bovine Mastitis in Family Dairy Herds of Central Mexico

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ma. Fabiola León-Galván ◽  
José E. Barboza-Corona ◽  
A. Arianna Lechuga-Arana ◽  
Mauricio Valencia-Posadas ◽  
Daniel D. Aguayo ◽  
...  
Keyword(s):  
Bacillus Thuringiensis ◽  
Bovine Mastitis ◽  
Dairy Herd ◽  
Subclinical Mastitis ◽  
Herd Size ◽  
Clinical Mastitis ◽  
Dairy Herds ◽  
Coagulase Negative Staphylococci ◽  
California Mastitis Test ◽  
Streptococcus Uberis

Thirty-two farms (n=535cows) located in the state of Guanajuato, Mexico, were sampled. Pathogens from bovine subclinical mastitis (SCM) and clinical mastitis (CLM) were identified by 16S rDNA and the sensitivity to both antibiotics and bacteriocins ofBacillus thuringiensiswas tested. Forty-six milk samples were selected for their positive California Mastitis Test (CMT) (≥3) and any abnormality in the udder or milk. The frequency of SCM and CLM was 39.1% and 9.3%, respectively. Averages for test day milk yield (MY), lactation number (LN), herd size (HS), and number of days in milk (DM) were 20.6 kg, 2.8 lactations, 16.7 animals, and 164.1 days, respectively. MY was dependent on dairy herd (DH), LN, HS, and DMP<0.01, and correlations between udder quarters from the CMT were around 0.49P<0.01. Coagulase-negative staphylococci were mainly identified, as well asStaphylococcus aureus,Streptococcus uberis,Brevibacterium stationis,B. conglomeratum, andStaphylococcus agnetis. Bacterial isolates were resistant to penicillin, clindamycin, ampicillin, and cefotaxime. Bacteriocins synthesized byBacillus thuringiensisinhibited the growth of multiantibiotic resistance bacteria such asS. agnetis,S. equorum,Streptococcus uberis,Brevibacterium stationis, andBrachybacterium conglomeratum, but they were not active againstS. sciuri, a microorganism that showed an 84% resistance to antibiotics tested in this study.

Simplify the interpretation of alert lists for clinical mastitis in automatic milking systems

2010 ◽  
Vol 71 (1) ◽  
pp. 50-56 ◽  
Author(s):  
W. Steeneveld ◽  
L.C. van der Gaag ◽  
H.W. Barkema ◽  
H. Hogeveen
Keyword(s):  
Clinical Mastitis ◽  
Automatic Milking ◽  
Automatic Milking Systems

scholarly journals Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

2020 ◽  
Vol 33 (3) ◽  
pp. 408-415
Author(s):  
B. Sitkowska ◽  
M. Kolenda ◽  
D. Piwczyński
Keyword(s):  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Dairy Herds ◽  
Lactation Curve ◽  
Milk Performance ◽  
Automatic Milking System ◽  
Automatic Milking ◽  
Automatic Milking Systems ◽  
Wood Model

Objective: The aim of the paper was to compare the fit of data derived from daily automatic milking systems (AMS) and monthly test-day records with the use of lactation curves; data was analysed separately for primiparas and multiparas.Methods: The study was carried out on three Polish Holstein-Friesians (PHF) dairy herds. The farms were equipped with an automatic milking system which provided information on milking performance throughout lactation. Once a month cows were also subjected to test-day milkings (method A4). Most studies described in the literature are based on test-day data; therefore, we aimed to compare models based on both test-day and AMS data to determine which mathematical model (Wood or Wilmink) would be the better fit.Results: Results show that lactation curves constructed from data derived from the AMS were better adjusted to the actual milk yield (MY) data regardless of the lactation number and model. Also, we found that the Wilmink model may be a better fit for modelling the lactation curve of PHF cows milked by an AMS as it had the lowest values of Akaike information criterion, Bayesian information criterion, mean square error, the highest coefficient of determination values, and was more accurate in estimating MY than the Wood model. Although both models underestimated peak MY, mean, and total MY, the Wilmink model was closer to the real values.Conclusion: Models of lactation curves may have an economic impact and may be helpful in terms of herd management and decision-making as they assist in forecasting MY at any moment of lactation. Also, data obtained from modelling can help with monitoring milk performance of each cow, diet planning, as well as monitoring the health of the cow.

scholarly journals Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks

2009 ◽  
Vol 77 (2) ◽  
pp. 168-175 ◽  
Author(s):  
Zhibin Sun ◽  
Sandhya Samarasinghe ◽  
Jenny Jago
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Accuracy ◽  
Clinical Mastitis ◽  
Classification Rate ◽  
Significant Difference ◽  
Artificial Neural ◽  
Automatic Milking ◽  
Automatic Milking Systems ◽  
Som Model

Two types of artificial neural networks, multilayer perceptron (MLP) and self-organizing feature map (SOM) were used to detect mastitis by automatic milking systems (AMS) using a new mastitis indicator that combined two previously reported indicators based on higher electrical conductivity (EC) and lower quarter yield (QY). Four MLPs with four combinations of inputs were developed to detect infected quarters. One input combination involved principal components (PC) adopted for addressing multi-collinearity in the data. The PC-based MLP model was superior to other non-PC-based models in terms of less complexity and higher predictive accuracy. The overall correct classification rate (CCR), sensitivity and specificity of this model were 90·74%, 86·90% and 91·36%, respectively. The SOM detected the stage of progression of mastitis in a quarter within the mastitis spectrum and revealed that quarters form three clusters: healthy, moderately ill and severely ill. The clusters were validated using k-means clustering, ANOVA and least significant difference. Clusters reflected the characteristics of healthy and subclinical and clinical mastitis, respectively. We conclude that the PC based model based on EC and QY can be used in AMS to detect mastitis with high accuracy and that the SOM model can be used to monitor the health status of the herd for early intervention and possible treatment.

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