Kenneth V. Nordlund

Effect of milk sample collection strategy on the sensitivity and specificity of bacteriologic culture and somatic cell count for detection of Staphylococcus aureus intramammary infection in dairy cattle

Four milk sample collection strategies for bacteriologic culture and identification of bovine intramammary infection due to Staphylococcus aureus were evaluated. Milk samples were collected at 24 h intervals from 245 lactating mammary quarters of 62 cows from one commercial dairy herd on 6 successive days. A total of 1470 quarter milk samples were available for study. Based on the bacteriologic culture results of all six quarter milk samples, each quarter was classified as infected with or free of S. aureus. The case definition used to establish the ‘gold standard’ was the isolation of two or more colonies of S. aureus on two or more occasions from the six quarter milk samples obtained from a given mammary quarter. The probability of a false-negative classification of a mammary quarter using all six culture results was estimated to be less than 0.0046, while the probability of a false-positive classification was less than 0.0004. Twenty-two quarters from 16 of 62 cows had S. aureus intramammary infection. Inocula (0.1 ml) for bacteriologic culture were prepared in the laboratory from quarter milk samples to represent alternative strategies for milk sample collection on farms. Sensitivity and specificity of detection of S. aureus-infected mammary quarters and/or cows was then determined. The accuracy of somatic cell counts for the same purpose was also determined for several cut-off values. The range of sensitivity values for bacteriologic culture and SCC were 91–100% and 54–95%, respectively. The range of specificity values for each test method ranged from 97.6 to 100% and from 81 to 83%, respectively. Bacteriologic culture, using any of the sampling strategies examined, had high specificity ( > 98%) and relatively high sensitivity ( > 91%) for identifying S. aureus intra mammary infection (IMI). However, there was a great difference in the number of culture attempts necessary to achieve this accuracy which would influence a dairy farm managers choice of which type of milk sample collection strategy to use.

Risk factors for postpartum problems in dairy cows: Explanatory and predictive modeling

The postpartum period is associated with a high incidence of most dairy cattle diseases and a high risk of removal from the herd. Postpartum diseases often share risk factors, and these factors may trigger a cascade of other diseases. The objective of this cohort study was to derive explanatory and predictive models for treatment or removal from the herd within the first 30 d in milk (TXR30). The TXR30 outcome was specifically defined as ≥1 treatment for ≥1 occurrence of milk fever, retained placenta, metritis, ketosis, displaced abomasum, lameness, or pneumonia; removal from the herd (sold or died); or both treatment and later herd removal. The study population consisted of 765 multiparous and 544 primiparous cows (predominantly Holstein) from 4 large commercial freestall-housed dairy herds. Treatment or removal from the herd was recorded as a binary outcome for each cow. Potential explanatory and predictive variables were limited to routine cow data that could be collected either before or within 24 h of calving. Models for multiparous and primiparous cows were developed separately because previous lactation variables are available only for multiparous cows. Adjusted odds ratios for TXR30 in the explanatory model for the multiparous cohort were 2.1 for lactation 3 compared with lactation 2, and 2.3 for lactation 4 or greater compared with lactation 2; 2.3 for locomotion score 3 or 4 compared with score 1; 3.3 for an abnormality at calving compared with no calving abnormality; 1.8 for each 1-standard deviation increase in previous lactation length; and 0.4 for each 5,000-kg increment in previous lactation milk yield in cows with longer previous lactation length. The final predictive model for TXR30 in multiparous cows included predictors similar but not identical to those included in the explanatory model. The area under the curve for the receiver operating characteristic curve from the final predictive model for the multiparous cohort was 0.70, with 60% sensitivity. For the primiparous cohort, calving abnormality increased the odds of TXR30 and was the only variable included in both the explanatory and predictive models. The area under the curve for the receiver operating characteristic curve from the final predictive model for the primiparous cohort was 0.66, with 35% sensitivity. This study identified key risk factors for TXR30 and developed equations for the prediction of TXR30. This information can help dairy producers better understand causes of postpartum problems.

A linear programming assessment of the profit from strategies to reduce the prevalence of Staphylococcus aureus mastitis

We used a linear programming model to estimate the financial returns to a Staphylococcus aureus testing and control program over a 1-year period for a 100-cow herd, with a 8636-kg rolling-herd average. Six tests, which vary in sensitivity from 0.80 to 0.98 and specificity of 0.99, were examined in simulated herds with 10, 20 and 30% prevalence of S. aureus infection. Sensitivity of these results to a range of assumptions regarding rolling-herd average, milk price, somatic cell-count premium, and cost and cure rate of dry treatment were examined to determine the profits from the program. The profits of a control program are most dependent upon prevalence, cell-count premium, and cost of dry treatment. In our simulation for a 100-cow herd, a testing and control program appears to cost less than US

Modern techniques for monitoring high‐producing dairy cows 1. Principles of herd‐level diagnoses

10 per cow per year, and pays for itself within 1 yr, except under the lowest prevalence and most-adverse conditions (low yield, high cost of dry treatment, or low SCC premium.

Practical Considerations for Ventilating Calf Barns in Winter

THIS article, the first of two describing common practices used to monitor high‐producing dairy herds in North America, focuses on how to obtain herd‐level diagnoses using one‐time assessment of herd performance instead of day‐to‐day cow and herd‐level monitoring. This can provide important information when troubleshooting a particular problem or undertaking ongoing periodic surveillance of management practices on dairy farms. The majority of the techniques discussed have been developed and refined by clinicians working in the Food Animal Production Medicine group at the School of Veterinary Medicine, University of Wisconsin‐Madison, using information collected from dairy herd problem investigations, on‐farm recording systems, blood, milk and urine samples, and information stored in the Dairy Herd Improvement Association (DHIA) database (the US equivalent of the National Milk Records). The article also outlines how herd health records and information obtained from the DHIA may be used to highlight subclinical production disease problems. Part 2, to be published in the next issue, will consider how these tools can be used to achieve a specific herd‐level diagnosis for ketosis, subacute ruminal acidosis and hypocalcaemia.

A model to determine sampling strategies and milk inoculum volume for detection of intramammary Staphylococcus aureus infections in dairy cattle by bacteriological culture

The use of air sampling devices to measure the concentrations of airborne bacteria in clinical investigations and research trials in calf barns has indicated that traditional systems of ventilation are problematic in cold weather. Individual pen designs should have two solid sides, but the front and rear should be as open as possible. Thermal stress should be managed by providing deep bedding and not by enclosing the pen. Air hygiene can be improved by reducing stocking density and using supplemental positive-pressure ventilation systems to deliver small amounts of air to each pen. Implementation of these recommendations can produce calf barns that seem to equal calf hutches in minimizing disease and provide better working conditions for the caregivers.

Modern techniques for monitoring high‐producing dairy cows 2. Practical applications

Abstract A model was developed to evaluate the effects that methods of obtaining milk samples and culture inoculum volumes had on the sensitivity of microbiological culture to detect Staphylococcus aureus intramammary infections (IMI). An assumption was made that milk from mammary quarters infected with S. aureus only contains bacteria intermittently. A modified sine wave function was used to model this intermittent shedding pattern. Specifications for the components of the shedding cycle used in this function were based on quantitative culture results from 54 experimentally infected S. aureus quarters, sampled daily for a period of 30–49 days. The components of the shedding cycle were length in days, peak number of CFU shed per milliliter of milk, and length of time in the cycle when no shedding occurred. These components were used to estimate the models predicted distribution of S. aureus CFU ml −1 milk when individual quarter milk samples were cultured for S. aureus . The sensitivity of culture for several sampling methods was then calculated. The model predicted that culture of a single quarter milk sample had a sensitivity ranging from 60 to 87% for detection of S. aureus IMI depending on inoculum volume. Quarter milk samples taken on day 1 and repeated either on day 3 or day 4, and cultured separately using 0.1 ml of milk for culture inoculum, were predicted to have sensitivities of 90–95% and 94–99%, respectively. Other milk-sampling strategies examined included culture of a composite milk sample (equal-volume mixture of milk from four separate mammary quarters ) and pooled milk samples in which samples from different milkings (either quarter or composite samples) were mixed together and then cultured. The range of predicted sensitivities of these other sampling strategies was 30–97%. Factors having the greatest impact on the sensitivity of culture, in order of importance were: the type of milk sample, the volume of milk cultured, and the time interval between repeated milk sample collection strategies.

Cluster analysis of Dairy Herd Improvement data to discover trends in performance characteristics in large Upper Midwest dairy herds

THIS article, the second of two discussing common practices used to monitor high‐producing dairy herds in North America, describes how the general principles of herd‐level diagnoses reviewed in Part 1 (In Practice, October 2006, volume 28, pp 510–515) may be applied to three major metabolic and nutritional disease syndromes frequently affecting these animals ‐ namely ketosis, subacute ruminal acidosis and milk fever. In each case, it describes how to make a herd diagnosis and outlines the prevention measures that can be implemented.

Survey of facility and management characteristics of large, Upper Midwest dairy herds clustered by Dairy Herd Improvement records

Principal component analysis (PCA) is a variable reduction method used on over-parameterized data sets with a vast number of variables and a limited number of observations, such as Dairy Herd Improvement (DHI) data, to select subsets of variables that describe the largest amount of variance. Cluster analysis (CA) segregates objects, in this case dairy herds, into groups based upon similarity in multiple characteristics simultaneously. This project aimed to apply PCA to discover the subset of most meaningful DHI variables and to discover groupings of dairy herds with similar performance characteristics. Year 2011 DHI data was obtained for 557 Upper Midwest herds with test-day mean ≥200 cows (assumed mostly freestall housed), that remained on test for the entire year. The PCA reduced an initial list of 22 variables to 16. The average distance method of CA grouped farms based on best goodness of fit determined by the minimum cophenetic distance. Six groupings provided the optimal fitting number of clusters. Descriptive statistics for the 16 variables were computed per group. On observations of means, groups 1, 2, and 6 demonstrated the best performances in most variables, including energy-corrected milk, linear somatic cell score (log of somatic cell count), dry period intramammary infection cure rate, new intramammary infection risk, risk of subclinical intramammary infection at first test, age at first calving, days in milk, and Transition Cow Index. Groups 3, 4, and 5 demonstrated the worst mean performances in most the PCA-selected variables, including DIM, age at first calving, risk of subclinical intramammary infection at first test, and dry period intramammary infection cure rate. Groups 4 and 5 also had the worst mean herd performances in energy-corrected milk, Transition Cow Index, linear somatic cell score, and new intramammary infection risk. Further investigation will be conducted to reveal patterns of management associated with herd categorization. The PCA and CA should be used when describing the multivariate performance of dairy herds and whenever working with over-parameterized data sets, such as DHI databases.

Methods for conducting an economic opportunity survey in smallholder dairy farms

A survey of management practices was conducted to investigate potential associations with groupings of herds formed by cluster analysis (CA) of Dairy Herd Improvement (DHI) data of 557 Upper Midwest herds of 200 cows or greater. Differences in herd management practices were identified between the groups, despite underlying similarities; for example, freestall housing and milking in a parlor. Group 6 comprised larger herds with a high proportion of primiparous cows and most frequently utilized practices promoting increased production [e.g., 84.4% used recombinant bovine somatotropin (rbST)], decreased lameness (e.g., 96.9% used routine hoof trimming for cows), and improved efficiency in reproduction [e.g., 93.8% synchronized the first breeding in cows (SYNCH)] and labor (e.g., mean ± SD, 67 ± 19 cows per 50-h per week full-time equivalent worker). Group 1 had the best mean DHI performances and followed most closely group 6 for the rate of adoption of intensive management practices while tending to outperform group 6 despite a generally smaller mean herd size (e.g., 42.3 ± 3.6 kg vs. 39.9 ± 3.6 kg of energy-corrected milk production; 608 ± 352 cows vs. 1,716 ± 1,405 cows). Group 2 were smaller herds with relatively high levels of performance that used less intensive management (e.g., 100% milked twice daily) and less technology (33.3 vs. 73.0% of group 1 used rbST). Group 4 were smaller but poorer-performing herds with low turnover and least frequently used intensive management practices (e.g., 39.1% SYNCH; 30.4% allowed mature, high-producing cows access to pasture). Group 5 used modern technologies and practices associated with improved production, yet had the least desirable mean DHI performance of all 6 groups. This group had the lowest proportion of deep loose-bedded stalls (only 52.2% used sand bedding) and the highest proportion (34.8%) of herds not using routine hoof trimming. The survey of group 3 herds did not reveal strong trends in management. The differences identified between herd groupings confirm significant variation in management practices linked to variation in overall herd performance measured by DHI variables. This approach provides an opportunity for consultants and outreach educators to better tailor efforts toward a certain type of dairy management philosophy, rather than taking a blanket approach to applying recommendations to farms simply because of their larger herd size.