Genetic analysis of somatic cell score in danish holsteins using a liability-normal mixture model

Mixture models are appealing for identifying hidden structures affecting somatic cell score (SCS) data, such as unrecorded cases of subclinical mastitis. Thus, liability-normal mixture (LNM) models were used for genetic analysis of SCS data, with the aim of predicting breeding values for such cases of mastitis. Here, putative mastitis statuses and breeding values for liability to putative mastitis were inferred solely from SCS observations. In total, there were 395,906 test-day records for SCS from 50,607 Danish Holstein cows. Four different statistical models were fitted: A) a classical (nonmixture) random regression model for test-day SCS; B1) an LNM test-day model assuming homogeneous (co)variance components for SCS from healthy (IMI-) and infected (IMI+) udders; B2) an LNM model identical to B1, but assuming heterogeneous residual variances for SCS from IMI- and IMI+ udders; and C) an LNM model assuming fully heterogeneous (co)variance components of SCS from IMI- and IMI+ udders. For the LNM models, parameters were estimated with Gibbs sampling. For model C, variance components for SCS were lower, and the corresponding heritabilities and repeatabilities were substantially greater for SCS from IMI- udders relative to SCS from IMI+ udders. Further, the genetic correlation between SCS of IMI- and SCS of IMI+ was 0.61, and heritability for liability to putative mastitis was 0.07. Models B2 and C allocated approximately 30% of SCS records to IMI+, but for model B1 this fraction was only 10%. The correlation between estimated breeding values for liability to putative mastitis based on the model (SCS for model A) and estimated breeding values for liability to clinical mastitis from the national evaluation was greatest for model B1, followed by models A, C, and B2. This may be explained by model B1 categorizing only the most extreme SCS observations as mastitic, and such cases of subclinical infections may be the most closely related to clinical (treated) mastitis.     

Genetic analysis of somatic cell scores in US Holsteins with a Bayesian mixture model

The objective of this study was to apply finite mixture models to field data for somatic cell scores (SCS) for estimation of genetic parameters. Data were approximately 170,000 test-day records for SCS from first-parity Holstein cows in Wisconsin. Five different models of increasing level of complexity were fitted. Model 1 was the standard single-component model, and the others were 2-component Gaussian mixtures consisting of similar but distinct linear models. All mixture models (i.e., 2 to 5) included separate means for the 2 components. Model 2 assumed entirely homogeneous variances for both components. Models 3 and 4 assumed heterogeneous variances for either residual (model 3) or genetic and permanent environmental variances (model 4). Model 5 was the most complex, in which variances of all random effects were allowed to vary across components. A Bayesian approach was applied and Gibbs sampling was used to obtain posterior estimates. Five chains of 205,000 cycles were generated for each model. Estimates of variance components were based on posterior means. Models were compared by use of the deviance information criterion. Based on the deviance information criterion, all mixture models were superior to the linear model for analysis of SCS. The best model was one in which genetic and PE variances were heterogeneous, but residual variances were homogeneous. The genetic analysis suggested that SCS in healthy and infected cattle are different traits, because the genetic correlation between SCS in the 2 components of 0.13 was significantly different from unity.     

Relationship between intramammary infection prevalence and somatic cell score in commercial dairy herds

We examined consistency of the relationship between intramammary infection (IMI) and somatic cell score (SCS) across several classes of cow, herd, and sampling time variables. Microbial cultures of composite milk samples were performed by New York Quality Milk Production Services from 1992 to 2004. SCS was from the most recent Dairy Herd Improvement test before IMI sampling. Records were analyzed from 79,308 cows in 1,124 commercial dairy herds representing a broad range of production systems. Three binary dependent variables were presence or absence of contagious IMI, environmental IMI, and all IMI. Independent variables in the initial models were SCS, SCS², lactation number, days in milk, sample day milk yield, use of coliform mastitis vaccine, participant type (required by regulation or voluntary), production system (type of housing, milking system, and herd size), season of sampling, year of sampling, and herd; also the initial models included interactions of SCS and SCS² with other independent variables, except herd and milk yield. Interaction terms characterize differences in the IMI-SCS relationship across classes of the independent variables. Models were derived using the Glimmix macro in SAS (SAS Institute Inc., Cary, NC) with a logistic link function and employing backward elimination. The final model for each dependent variable included all significant independent variables and interactions. Simplified models omitted SCS² and all interactions with SCS. Interactions of SCS with days in milk, use of coliform mastitis vaccine, participant type, season, and year were not significant in any of the models. Interaction of SCS with production system was significant for the all IMI model, whereas interaction of SCS with lactation number was significant for the environmental and all IMI models. Each 1-point increase in SCS (or doubling of somatic cell count) was associated with a 2.3, 5.5, and 9.1% increase in prevalence of contagious, environmental, and all IMI, respectively. Empirical receiver operator characteristic curves and areas under the curve were derived for final and simplified models. The areas under the curve for simplified and final models within each type of IMI differed by 0.009 or less. We concluded that the relationship of IMI with SCS was generally stable over time and consistent across seasons, production systems, and cow factors.     

Short communication: Comparison of bulk milk, yield-corrected, and average somatic cell counts as parameters to summarize the subclinical mastitis situation in a dairy herd

In this study, the correlation was determined between the prevalence of high cow-level somatic cell count (SCC >250,000 cells/mL), a summary of the subclinical mastitis situation in a dairy herd, and 3 average herd SCC parameters: bulk milk SCC (BMSCC), yield-corrected test-day SCC (CHSCC), and the arithmetic average test-day SCC (HSCC) of the lactating herd. The herd prevalence of cows with an SCC of >250,000 cells/mL was calculated by using Dairy Herd Improvement data. Herds were included if BMSCC was sampled within 2 d of the Dairy Herd Improvement test day and if the BMSCC did not exceed 400,000 cells/mL. The interval between sampling, 0, 1, or 2 d, did not significantly influence the correlation between BMSCC and the prevalence of high SCC. The correlations between the prevalence of high SCC and BMSCC, yield-corrected test-day SCC, and HSCC, examined by using a linear regression model, were 0.64, 0.78, and 0.89, respectively. Therefore, it can be concluded that, based on the highest correlation, HSCC is a more appropriate parameter than BMSCC to summarize the average herd subclinical mastitis situation in a dairy herd.     

Combining somatic cell count traits for optimal selection against mastitis

Test-day records of somatic cell counts (SCC) can be used to define alternative traits to decrease genetic susceptibility to clinical mastitis (CM) and subclinical mastitis (SCM). This paper examines which combination of alternative SCC traits can be used best to reduce both CM and SCM and whether direct information on CM is useful in this respect. Genetic correlations between 10 SCC traits and CM and SCM were estimated from 3 independent data sets. The SCC traits with the strongest correlations with CM differed from those with the strongest correlations with SCM. Selection index calculations were made for a breeding goal of 50% CM and 50% SCM resistance using these correlations. They indicated that a combination of 5 SCC traits (SCC early and late in lactation, suspicion of infection based on increased SCC, extent of increased SCC, and presence of a peak pattern in SCC) gave a high accuracy, almost without loss, compared with the full set of 10 SCC traits. The estimated accuracy of this index was 0.91, assuming that the correlations had been estimated without error. To take errors in estimation into account, correlations were resampled from a normal distribution with mean and standard errors as originally estimated. The accuracy of the index calculated with the original correlations was then recalculated using the resampled correlations. The average accuracy based on 50,000 resamplings decreased to 0.81. Use of direct information on CM improved the accuracy (uncorrected for errors in correlations) only slightly, to 0.92.     

Application of a finite mixture model to somatic cell scores of Italian goats

The objectives of this study were to apply a finite mixture model (FMM) to data for somatic cell count in goats and to compare the fit of the FMM with that of a standard linear mixed effects model. Bacteriological information was used to assess the ability of the model to classify records from healthy or infected goats. Data were 4518 observations of somatic cell score (SCS) and bacterial infection from both udder halves of 310 goats from 5 herds in Northern Italy. The records were from a complete production season, and were taken monthly from February to November 2000. Explanatory factors in both models included a 3-parameter regression on days in milk (DIM); fixed class effects of herd-test-day, parity group, and udder side (left or right); and random effects of goat and udder half within goat. In addition, the 2-component FMM included a fixed mean for the second component of the model (theoretically corresponding to infected udder halves), as well as an unknown probability of membership to a given putative infection status. A Bayesian statistical approach was used for the analysis with Gibbs sampling used to obtain draws from posterior distributions of parameters of interest. Two sampling chains of 200,000 cycles each were generated for each model. The FMM yielded a much lower estimate of residual variance than the standard model (1.28 vs. 3.02 SCS²), and a slightly higher estimate for the between-goat variance (1.79 vs. 1.48). The deviance information criterion (DIC) was used to compare the fit of the 2 models. The DIC was much lower for the FMM, indicating a better fit to the data. The FMM was able to classify correctly 60 and 48% of the healthy and infected observations, respectively. This was slightly higher than what would be expected from random classification, but not high enough for useful mastitis diagnosis. Nevertheless, increased precision of genetic evaluation is the goal of applying the FMM, rather than timely and accurate mastitis diagnosis. The results suggest that more research on FMM for SCS is merited and necessary for proper application.     

Differential somatic cell count in milk before,during, and after lipopolysaccharide- and lipoteichoic-acid-induced mastitis in dairy cows

Intramammary infections induce the initiation of the inflammatory response, resulting in an increase in somatic cell count (SCC) in milk. The SCC includes several different types of cells but does not differentiate between them. On the contrary, the new differential somatic cell count (DSCC) parameter allows for the differentiation between 2 groups of cells: polymorphonuclear neutrophils (PMN) and lymphocytes versus macrophages. Therefore, the aim of this paper was to describe the changes of both DSCC and SCC during mastitis induced by cell wall components from typical mastitis-causing pathogens [lipopolysaccharide (LPS), Escherichia coli; lipoteichoic acid (LTA), Staphylococcus aureus] known to trigger different severities of mastitis. In addition, the effect the glucocorticoid prednisolone (PRED), which is known to attenuate the immune response in the mammary gland, was investigated. Twenty dairy cows were equally divided into 5 groups and treated with LPS, LTA, LPS+PRED, LTA+PRED, or a saline control. Milk samples were taken at the following time points: baseline (d ?3, ?2, and ?1), right before treatment (d 0), 5 h after treatment (d 0.2), early cure phase (d 1 and 2), and late cure phase (d 3, 4, 5, 6, 7, and 14) and analyzed for DSCC and SCC. Mean DSCC values increased significantly from <60% at baseline and right before treatment to >81% 5 h after treatment and the early cure phase in all groups, except for the groups control and LTA+PRED. This increase clearly reflects a shift in cell populations to predominantly PMN. The SCC increased significantly following the stimulation, too, as expected. Interestingly, we observed cases where SCC increased moderately only whereas DSCC showed an evident increase, meaning that the shift in cell populations occurred even at low SCC levels. The PRED clearly lowered the cell migration in group LTA+PRED. This is the first ever study investigating DSCC during induced mastitis under controlled conditions. The combination of DSCC and SCC could be employed for the earlier detection of mastitis by revealing the shift in cell population independent from the SCC level. Furthermore, combining DSCC and SCC information could help to determine the stage of mastitis because we observed high DSCC and SCC results in the early stage of mastitis but evidently lower DSCC and high SCC in the cure phase. Hence, our results offer the first fundamental insights on how mastitis monitoring could be improved in the frame of dairy herd improvement programs.     

Recursive relationships between milk yield and somatic cell score of Canadian Holsteins from finite mixture random regression models

Finite mixture, multiple-trait, random regression animal models with recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test-day were applied to first lactation Canadian Holstein data. All models included fixed herd-test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Causal links between phenotypes for milk yield and SCS were fitted separately for records from healthy cows and cows with a putative, subclinical form of mastitis. Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Bayes factors indicated superiority of the model with recursive link from milk to SCS over the reciprocal recursive model and the standard multiple-trait model. Differences between models measured by other, single-trait model comparison criteria (i.e., weighted mean squared error, squared bias, and correlation between observed and expected data) were negligible. Approximately 20% of test-day records were classified as originating from cows with mastitis in recursive mixture models. The proportion of records from cows infected with mastitis was largest at the beginning of lactation. Recursive mixture models exhibited different distributions of data from healthy and infected cows in different parts of lactation. A negative effect of milk to SCS (up to −0.15 score points for every kilogram of milk for healthy cows from 5 to 45 d in milk) was estimated for both mixture components (healthy and infected) in all stages of lactation for the most plausible model. The magnitude of this effect was stronger for healthy cows than for cows infected with mastitis. Different patterns of genetic and environmental correlations between milk and SCS for healthy and infected records were revealed, due to heterogeneity of structural coefficients between mixture components. Estimated breeding values for SCS from the best fitting model for sires of infected daughters were more related to estimated breeding values for the same trait from the regular multiple-trait model than evaluations for sires of mastitis-free cows.     

Joint analysis of quantitative trait loci for clinical mastitis and somatic cell score on five chromosomes in three Nordic dairy cattle breeds

Five chromosomes were selected for joint quantitative trait loci (QTL) analyses for clinical mastitis (CM) and somatic cell score (SCS) in 3 breeds: Finnish Ayrshire (FA), Swedish Red and White (SRB), and Danish Red (DR). In total, 19 grandsires and 672 sons in FA, 19 grandsires and 499 sons in SRB, and 8 grandsires and 258 sons in DR were used in the study. These individuals were genotyped with the 61 microsatellite markers used in any of the previous QTL scans on the selected chromosomes. Within-family QTL analyses based on linear regression models were carried out for CM and SCS to identify the segregating sires for each region. On the segregating families, joint single-trait and 2-trait analyses were performed using variance components models. The analyses confirmed that QTL affecting CM or SCS, or both, segregate on Bos taurus autosomes (BTA) 9, 11, 14, and 18, whereas a QTL on BTA29 could not be confirmed. Our results indicate that there may be at least 2 linked QTL on BTA9, one that primarily affects CM and a second that primarily affects SCS. On chromosomes BTA11, 14, and 18, the joint analyses were only significant for SCS.     

Genetic analysis of somatic cell score in Norwegian cattle using random regression test-day models

The dataset used in this analysis contained a total of 341,736 test-day observations of somatic cell scores from 77,110 primiparous daughters of 1965 Norwegian Cattle sires. Initial analyses, using simple random regression models without genetic effects, indicated that use of homogeneous residual variance was appropriate. Further analyses were carried out by use of a repeatability model and 12 random regression sire models. Legendre polynomials of varying order were used to model both permanent environmental and sire effects, as did the Wilmink function, the Lidauer-M?ntysaari function, and the Ali-Schaeffer function. For all these models, heritability estimates were lowest at the beginning (0.05 to 0.07) and higher at the end (0.09 to 0.12) of lactation. Genetic correlations between somatic cell scores early and late in lactation were moderate to high (0.38 to 0.71), whereas genetic correlations for adjacent DIM were near unity. Models were compared based on likelihood ratio tests, Bayesian information criterion, Akaike information criterion, residual variance, and predictive ability. Based on prediction of randomly excluded observations, models with 4 coefficients for permanent environmental effect were preferred over simpler models. More highly parameterized models did not substantially increase predictive ability. Evaluation of the different model selection criteria indicated that a reduced order of fit for sire effects was desireable. Models with zeroth- or first-order of fit for sire effects and higher order of fit for permanent environmental effects probably underestimated sire variance. The chosen model had Legendre polynomials with 3 coefficients for sire, and 4 coefficients for permanent environmental effects. For this model, trajectories of sire variance and heritability were similar assuming either homogeneous or heterogeneous residual variance structure.     

Associations among body condition score, body weight, somatic cell count, and clinical mastitis in seasonally calving dairy cattle

The objective of this study was to determine if an association existed among body condition score (BCS), body weight (BW), and udder health, as indicated by somatic cell score (SCS) and cases of clinical mastitis (CM). The data consisted of 2,635 lactations from Holstein-Friesian (n = 523) and Jersey (n = 374) cows in a seasonal calving pasture-based research herd between the years 1986 and 2000, inclusive. Increased BCS at calving was associated with reduced SCS in first- and second-parity cows, and greater SCS in cows of third parity or greater. This relationship persisted for most BCS traits throughout lactation. Body weight was positively associated with SCS, although the effect was greater in Jersey cows than in Holstein-Friesians. Increased BCS and BW loss in early lactation were associated with lower SCS and a reduced probability of a high test-day SCC. Body condition score was not significantly related to CM with the exception of a curvilinear relationship between the daily rate of BCS change to nadir and CM in early lactation. Several BW variables were positively associated with a greater likelihood of CM. Nevertheless, most associations with udder health lacked biological significance within the ranges of BCS and BW generally observed on-farm. Results are important in assuring the public that modern dairy systems, where cows are subjected to substantial amounts of BCS mobilization in early lactation, do not unduly compromise cow udder health.     

Genetic improvement in mastitis resistance: comparison of selection criteria from cross-sectional and random regression sire models for somatic cell score

Several selection criteria for reducing incidence of mastitis were developed from a random regression sire model for test-day somatic cell score (SCS). For comparison, sire transmitting abilities were also predicted based on a cross-sectional model for lactation mean SCS. Only first-crop daughters were used in genetic evaluation of SCS, and the different selection criteria were compared based on their correlation with incidence of clinical mastitis in second-crop daughters (measured as mean daughter deviations). Selection criteria were predicted based on both complete and reduced first-crop daughter groups (261 or 65 daughters per sire, respectively). For complete daughter groups, predicted transmitting abilities at around 30 d in milk showed the best predictive ability for incidence of clinical mastitis, closely followed by average predicted transmitting abilities over the entire lactation. Both of these criteria were derived from the random regression model. These selection criteria improved accuracy of selection by approximately 2% relative to a cross-sectional model. However, for reduced daughter groups, the cross-sectional model yielded increased predictive ability compared with the selection criteria based on the random regression model. This result may be explained by the cross-sectional model being more robust, i.e., less sensitive to precision of (co)variance components estimates and effects of data structure.     

Short communication: Effectiveness of tools provided by a dairy company on udder health in Dutch dairy farms

A Dutch dairy company initiated a quality system to support dairy farmers to improve sustainability on their farm. Improvement of udder health is defined by the dairy company as one of the sustainability items. A part of that quality system is to offer farmers 3 tools to improve the udder health status of the herd. The first tool is an Udder Health Workshop at which farmers make a farm-specific action plan to improve the udder health situation in their herd. The second tool is the Udder Health Navigator, which is an internet-based program to gain insight in the actual udder health situation at the farm. The third tool is the Udder Health Checklist, which is available on the internet and it identifies farm-specific risks for udder health problems. The aim of this study was to evaluate the effectiveness of these tools in improving udder health. The bulk milk somatic cell count (BMSCC) was used as the measure of herd udder health performance. In total, 605 farms attended the Udder Health Workshop, 988 farms completed the Udder Health Navigator, and 1,855 farms completed the Udder Health Checklist in 2012. Information on BMSCC records (2 records per month) was available for 12,782 Dutch dairy farms during the years 2011 and 2012. For every farm, the average BMSCC of all months during the years 2012 and 2011 were calculated. This resulted in 306,768 average monthly observations of the BMSCC. Subsequently, all months after the completion of one of the tools were assigned a 1, and all other months were assigned a 0. A statistical analysis was carried out to compare the average monthly BMSCC of the farms that completed one or more tools with farms that did not complete one of the tools. Both completing the Udder Health Navigator and the Udder Health Checklist had a significant association with a lower average monthly BMSCC. The effect of the Udder Health Navigator and Udder Health Checklist on the BMSCC were greater in herds with a BMSCC in 2011 of 200,000 to 250,000 cells/mL and even greater for herds with a BMSCC above 250,000 cells/mL compared with herds with a BMSCC in 2011 of 150,000 to 200,000 cells/mL or less than 150,000 cells/mL. It is difficult to draw conclusions on the effect of the Udder Health Workshop due to overlap in participation between the tools. The results suggest that completing the web tools is associated with a reduction in the BMSCC of the herd.     

Analysis of the relationship between somatic cell score and functional longevity in Canadian dairy cattle

The aim of this study was to assess the level of somatic cell count (SCC) and to explore the impact of somatic cell score (SCS) on the functional longevity of Canadian dairy cattle by using a Weibull proportional hazards model. Data consisted of 1,911,428 cows from 15,970 herds sired by 7,826 sires for Holsteins, 80,977 cows in 2,036 herds from 1,153 sires for Ayrshires, and 53,114 cows in 1,372 herds from 1,758 sires for Jerseys. Functional longevity was defined as the number of days from the first calving to culling, death, or censoring. The test-day SCC was transformed to a linear score, and the resulting SCS were averaged within each lactation. The average SCS were grouped into 10 classes. The statistical model included the effects of stage of lactation; season of production; annual change in herd size; type of milk recording supervision; age at first calving; effects of milk, fat, and protein yields, calculated as within-herd-year-parity deviations; herd-year-season of calving; SCS class; and sire. The relative culling rate was calculated for animals in each SCS class after accounting for the aforementioned effects. The overall average SCC for Holsteins was 167,000 cells/mL, for Ayrshires was 155,000 cells/mL, and for the Jerseys was 212,000 cells/mL. In all breeds there were no appreciable differences in the relative risk of culling among classes of SCS breed averages (i.e., up to a SCS of 5). However, as the SCS increased beyond the breed average, the relative risk of cows being culled increased considerably. For instance, Holstein, Ayrshire, and Jersey cows with the highest classes of SCS had, respectively, a 4.95, 6.73, and 6.62 times greater risk of being culled than cows with average SCS.     

Short communication: bivariate genetic analysis of clinical mastitis and somatic cell count in Norwegian dairy cattle

Clinical mastitis (CM) and lactation mean somatic cell score (LSCS) were analyzed with a bivariate linear sire model. Nearly 1.4 million primiparous cows of Norwegian Dairy Cattle from 2043 sires were used. The heritability estimates were 0.03 for CM and 0.11 for LSCS. The estimates of genetic and residual correlations between the 2 traits were 0.53 and 0.10, respectively. It is postulated that the genetic correlation probably is highly population-specific.     

Impact of early lactation somatic cell count in heifers on somatic cell counts over the first lactation

The objective of this study was to estimate the impact of somatic cell count in early lactation (SCCel) from Belgian dairy heifers on test-day somatic cell count (SCC) in first lactation. Geometric mean SCCel [5 to 14 d in milk (DIM)] of the 14,766 available samples was 104,000 cells/mL, and decreased from 178,000 at 5 DIM to 74,000 cells/mL at 14 DIM. Proportion of SCCel >200,000 cells/mL was 27.5. Heifers calving in the period April-June had highest SCCel.In total, 117,496 monthly SCC were measured. A multilevel regression analysis revealed that an increase of the natural log-transformed SCCel (LnSCCel) by one unit on average resulted in an increase of test-day natural log-transformed SCC (LnSCC) by 0.22 unit. The impact of LnSCCel on LnSCC depended on when LnSCCel was measured; an elevated LnSCCel at 14 DIM was more consequential than an equally elevated LnSCCel at 5 DIM. The probability of having a test-day SCC >200,000 cells/mL during the first lactation, also increased with an increasing LnSCCel. The negative effect of an elevated LnSCCel was still present, although to a lesser extent, in heifers with a second test-day SCC 相似文献   

Pathogen-specific effects of quantitative trait loci affecting clinical mastitis and somatic cell count in Danish Holstein cattle

The aim of this study was to investigate whether quantitative trait loci (QTL) affecting the risk of clinical mastitis (CM) and QTL affecting somatic cell score (SCS) exhibit pathogen-specific effects on the incidence of mastitis. Bacteriological data on mastitis pathogens were used to investigate pathogen specificity of QTL affecting treatments of mastitis in first parity (CM1), second parity (CM2), and third parity (CM3), and QTL affecting SCS. The 5 most common mastitis pathogens in the Danish dairy population were analyzed: Streptococcus dysgalactiae, Escherichia coli, coagulase-negative staphylococci, Staphylococcus aureus, and Streptococcus uberis. Data were analyzed using 2 approaches: an independence test and a generalized linear mixed model. Three different data sets were used to investigate the effect of data sampling: all samples, only samples that were followed by antibiotic treatment, and samples from first-crop daughters only. The results showed with high certainty that 2 QTL affecting SCS exhibited pathogen specificity against Staph. aureus and E. coli, respectively. The latter result might be explained by a pleiotropic QTL that also affects CM2 and CM3. Less certain results were found for QTL affecting CM. A QTL affecting CM1 was found to be specific against Strep. dysgalactiae and Staph. aureus, a QTL affecting CM2 was found to be specific against E. coli, and finally a QTL affecting CM3 was found to be specific against Staph. aureus. None of the QTL analyzed was found to be specific against coagulase-negative staphylococci and Strep. uberis. Our results show that particular mastitis QTL are highly likely to exhibit pathogen-specificity. However, the results should be interpreted carefully because the results are sensitive to the sampling method and method of analysis. Field data were used in this study. These kind of data may be heavily biased because there is no standard procedure for collecting milk samples for bacteriological analysis in Denmark. Furthermore, using only the mean SCS from d 10 to 180 after parturition may lead to truncated effects of SCS-QTL when samples collected after d 180 are used. Additionally, repeated samples were used, which could boost the difference in incidence of pathogens between daughters of sires inheriting the positive and negative QTL allele, respectively. However, the magnitude of these effects in this study is unclear.     

Genetic improvement of mastitis resistance: validation of somatic cell score and clinical mastitis as selection criteria

Mean daughter deviations for clinical mastitis among second-crop daughters were regressed on predicted transmitting abilities for clinical mastitis and lactation mean somatic cell score in first-crop daughters to validate the predictive ability of these traits as selection criteria for reduced incidence of clinical mastitis. A total of 321 sires had 684,897 second-crop daughters, while predicted transmitting abilities were calculated for 2159 sires, based on 495,681 records of first-crop daughters. Predictive ability, as a measure of efficiency of selection, was 23 to 43% higher for clinical mastitis than for lactation mean somatic cell score. Compared to single-trait selection, predictive ability improved 8 to 13% from utilizing information on both traits. The relative weight that should be assigned to standardized predicted transmitting abilities from univariate genetic analyses were 60 to 67% for clinical mastitis and 33 to 40% for lactation mean somatic cell score. No significant nonlinear genetic relationship between the two traits was found.     

Management practices from questionnaire surveys in herds with very low somatic cell score through a national mastitis program in France

French dairy herds (n = 534) were enrolled in the National 'Zero Mastitis Objective' Program to highlight management practices characterizing very low somatic cell score (SCS) herds. The herds studied were stratified into 2 groups. The first group (LOW) included herds within the first 5 percentiles and the second group (MED) herds within the 50 to 55 percentiles of herds on the basis of mean SCS for the 36 mo preceding the program. Potential explanatory variables, collected through questionnaire surveys, were analyzed using multistep logistic regression models. Twenty-six variables were significant factors in the final models, in which 18 were considered as primary factors for very low SCS. The probability for a herd belonging to the LOW group was associated with: (1) regular use of teat spraying; (2) herdsman precise in his techniques; (3) less than 1 person-year used at activities other than dairy herd; (4) teat dipping after mammary infusion at dry off; (5) heifers kept in a calving pen around parturition; (6) cows locked in feed-line lockups after milking; (7) dry cows with prepartum Ca restriction; (8) heifers on a nondamp pasture; (9) cows culled when at least one damaged teat; (10) heifers at pasture not drinking water from a river; and (11) disinfecting teat ends with alcohol before intramammary infusion at dry off. The probability for a herd belonging to the MED group was associated with: (1) milking cows housed in a straw yard; (2) checking heifers for mastitis only beginning at 2-wk prepartum; (3) no mastitis treatment when at least one clot was observed in milk at successive milkings; (4) distance of herdsman's house to cowshed >300 m; (5) only dirty teats washed before milking; (6) free access of cows from pasture to cowshed during bad weather; and (7) more than 18% of spring calvings. The variables associated with very low SCS should be applied as part of a thorough mastitis-control program adapted to each herd.