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.     

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 correlations between pathogen-specific mastitis and somatic cell count in Danish Holsteins

The aim of this study was to estimate genetic correlations (r_a) between 2 lactation average somatic cell count (LASCC) traits and 6 different mastitis traits in 226,482 first-parity Danish Holstein cows that calved between 1998 and 2008. The LASCC traits were defined from 5 to either 170 d (LASCC_170) or 300 d (LASCC_300) after calving, and the mastitis traits were unspecific mastitis (all mastitis treatments, both clinical and subclinical, regardless of the causative pathogen) and mastitis caused by either Streptococcus dysgalactiae, Escherichia coli, coagulase-negative staphylococci (CNS), Staphylococcus aureus, or Streptococcus uberis. Variance components were estimated using bivariate threshold-Gaussian models via Gibbs sampling. The posterior means of r_a between LASCC_170 and the mastitis traits were greatest for unspecific mastitis (r_a = 0.71), followed by CNS, Strep. dysgalactiae, Strep. uberis, and E. coli (r_a = 0.54 to 0.69) and were lowest for Staph. aureus mastitis (r_a = 0.44). The genetic correlation between LASCC_300 and the mastitis traits were generally smaller (r_a = 0.47 to 0.69). Caution should be taken when interpreting the results, however, because some posterior density intervals for r_a were large (between 0.14 and 0.47 units). Phenotypically, Staph. aureus is known to be associated with high SCC and especially with subclinical mastitis through chronic infections, so the low r_a between Staph. aureus mastitis and LASCC, compared with r_a for the other pathogens, was not expected. Subclinical cases are usually submitted to dry cow therapy (not included in the present study), not treated at all, or wrongly recorded as clinical cases. Thus, the incidence of Staph. aureus mastitis is likely too low, and the genetic correlation between Staph. aureus mastitis and LASCC may therefore be underestimated in the present study. The results for the remaining pathogens were as expected, smallest for E. coli and larger but similar for Strep. dysgalactiae, Strep. uberis, and CNS. Selection for lower LASCC is expected to decrease the incidence of pathogen-specific mastitis, especially for Strep. uberis, Strep. dysgalactiae, and CNS and, to a lesser extent, for Staph. aureus and E. coli. Data recording should preferably be improved, and economic weights for the pathogen-specific mastitis traits should be estimated before implementing an udder health index that includes pathogen-specific mastitis traits.     

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.     

Alternative somatic cell count traits exploitable in genetic selection for mastitis resistance in Italian Holsteins

The aim of the present study was to characterize alternative somatic cell count (SCC) traits that could be exploited in genetic selection for mastitis resistance. Data were from 66,407 first-parity Holsteins in 404 herds. Novel SCC traits included average somatic cell score (SCS, log-transformation of SCC) in early lactation (SCS_150), standard deviation of SCS of the entire lactation (SCS_SD), the presence of at least one test-day (TD) SCC >400,000 cells/mL in the lactation, and the ratio of number of TD SCC >400,000 cells/mL to total number of TD in the lactation. Novel traits and lactation-mean SCS (SCS_LM) were analyzed using linear mixed or logistic regression models, including month of calving, year of calving, number of TD, and milk yield as fixed effects, and herd and residual as random terms. A multitrait linear animal model was applied to a random subset of 152 herds (n = 22,695 cows) to assess heritability of and genetic correlations between SCC traits. Alternative SCC traits were affected by the environmental factors included in the model; in particular, results suggested a seasonal effect and a tendency toward an improvement of the udder health status in the last years. Association was also found between novel SCC traits and milk production. Alternative SCC traits exhibited coefficients of additive genetic variation that were similar to or larger than that of traditional SCS_LM. Heritability of novel SCC traits was smaller than heritability of SCS_LM (0.126 ± 0.014), ranging from 0.044 ± 0.008 (SCS_SD) to 0.087 ± 0.010 (SCS_150). Genetic correlations between SCC traits ranged from 0.217 ± 0.096 (SCS_150 and SCS_SD) to 0.969 ± 0.010 (SCS_LM and SCS_150). Alternative SCC traits exhibited additive genetic variation that is potentially exploitable in breeding programs of Italian Holstein population to improve resistance to mastitis.     

Characterization of distributions of somatic cell counts

There is more useful information in distributions of somatic cell count (SCC) than is currently used in practice. Analysis of SCC of individual quarters (n = 450,834 quarter records of 133,102 cows) showed that the presence of pathogens did not change the peak of the SCC distribution. Instead, the percentages of observations in the tail changed. Probability density functions of specified sets of up to 5 standard distributions were then fitted on the number of records per class, using a maximum likelihood procedure. Analysis of cow SCC (2 data sets: n = 335,135 test-day records of 41,567 cows on 407 farms and n = 1,665,431 test-day records) showed that a mixture of a normal, a log-normal and an exponential density function (N+LN+E) best described the distribution of SCC. A mixture of 4 normal and an exponential distribution (4N+E) was also a good approximation. For this last mixture, each distribution could be associated with presence or absence of pathogens. The first 2 normal distributions appear to consist of uninfected cows and cows recovering from an infection, the third normal distribution may be associated with minor pathogens, and the fourth normal and the exponential distribution with major pathogens and persistent infections. Estimated percentages of records in each underlying distribution differed between parities, between stages of lactation, and between records with previous records being above or below 100,000 cells/mL. The categorical nature of cow-SCC can be utilized by deriving new traits such as the fraction of cow-SCC records in a lactation that are associated with an infection with a major pathogen.     

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.     

牛乳体细胞数的检测方法

讨论了体细胞数与乳腺炎的关系以及体细胞数对牛乳成分及产奶量损失的影响。主要介绍了4种常用的体细胞数的检测方法，即加利福尼亚细胞数测定法(CMT)，威斯康辛乳腺炎试验(WMT)，电子体细胞计数法(DHI)和直接镜检法(CMSCC)。     

Short communication: Genetic aspects of milk differential somatic cell count in Holstein cows: A preliminary analysis

The aim of the present study was to assess genetic variation and heritability of a novel indicator of udder health, milk differential somatic cell count (DSCC), which represents the percentage of neutrophils plus lymphocytes in the total somatic cell count (SCC). Furthermore, we estimated genetic and phenotypic correlations of DSCC with other milk traits routinely measured in Italian Holstein cows. Besides DSCC, test-day data included milk yield, composition traits (i.e., fat, protein, casein, and lactose percentages), pH, milk urea nitrogen, and SCC. After editing, the final data set included 10,709 test-day records of 5,142 cows in 299 herds. Mean of DSCC was 62.07%, which means that macrophages were approximately 38% of total SCC. Comparing our results with the literature offered compelling evidence of the importance of acquiring information about the proportion of the different cell types in milk to better define the udder health status. In addition, our analysis revealed, for the first time, that DSCC is a heritable trait, and heritability (0.08 ± 0.02) was higher than that of traditional somatic cell score (0.04 ± 0.02). Nevertheless, heritability of DSCC is still low compared with milk yield and quality traits. Single-trait analysis within parity showed that DSCC was less heritable in primiparous than in multiparous cows, whereas bivariate analysis confirmed that DSCC and somatic cell score were 2 different traits, as their genetic and phenotypic correlations differed from unity. From a genetic point of view, the DSCC was positively weakly associated with milk yield, lactose percentage, and milk urea nitrogen, and negatively associated with pH. Our findings contributed to the understanding of the genetic background of DSCC and are a precursor to the potential use of DSCC in breeding programs to enhance cow resistance to mastitis. However, further research is needed to determine the weight this novel trait should receive in a selection program aimed to reduce udder health problems.     

Distribution of non-aureus staphylococci species in udder quarters with low and high somatic cell count,and clinical mastitis

The effect of non-aureus staphylococci (NAS) in bovine mammary health is controversial. Overall, NAS intramammary infections (IMI) increase somatic cell count (SCC), with an effect categorized as mild, mostly causing subclinical or mild to moderate clinical mastitis. However, based on recent studies, specific NAS may affect the udder more severely. Some of these apparent discrepancies could be attributed to the large number of species that compose the NAS group. The objectives of this study were to determine (1) the SCC of quarters infected by individual NAS species compared with NAS as a group, culture-negative, and major pathogen-infected quarters; (2) the distribution of NAS species isolated from quarters with low SCC (<200,000 cells/mL) and high SCC (≥200,000 cells/mL), and clinical mastitis; and (3) the prevalence of NAS species across quarters with low and high SCC. A total of 5,507 NAS isolates, 3,561 from low SCC quarters, 1,873 from high SCC quarters, and 73 from clinical mastitis cases, were obtained from the National Cohort of Dairy Farms of the Canadian Bovine Mastitis Research Network. Of quarters with low SCC, high SCC, or clinical mastitis, 7.6, 18.5, and 4.3% were NAS positive, respectively. The effect of NAS IMI on SCC was estimated using mixed-effect linear regression; prevalence of NAS IMI was estimated using Bayesian analyses. Mean SCC of NAS-positive quarters was 70,000 cells/mL, which was higher than culture-negative quarters (32,000 cells/mL) and lower than major pathogen-positive quarters (129,000 to 183,000 cells/mL). Compared with other NAS species, SCC was highest in quarters positive for Staphylococcus capitis, Staphylococcus gallinarum, Staphylococcus hyicus, Staphylococcus agnetis, or Staphylococcus simulans. In NAS-positive quarters, Staphylococcus xylosus (12.6%), Staphylococcus cohnii (3.1%), and Staphylococcus equorum (0.6%) were more frequently isolated from quarters with low SCC than other NAS species, whereas Staphylococcus sciuri (14%) was most frequently isolated from clinical mastitis cases. Finally, in NAS-positive quarters, Staphylococcus chromogenes, S. simulans, Staphylococcus epidermidis, and Staphylococcus haemolyticus were isolated with similar frequency from among low SCC and high SCC quarters and clinical mastitis cases. Staphylococcus chromogenes, S. simulans, S. xylosus, S. haemolyticus, S. epidermidis, S. agnetis, Staphylococcus arlettae, S. capitis, S. gallinarum, S. sciuri, and Staphylococcus warneri were more prevalent in high than in low SCC quarters. Because the NAS are a large, heterogeneous group, considering them as a single group rather than at the species, or even subspecies level, has undoubtedly contributed to apparent discrepancies among studies as to their distribution and importance in IMI and mastitis.     

Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis

This paper studies whether cows with originally lower somatic cell count (SCC) are more susceptible to clinical mastitis (CM) than cows with higher somatic cell count, and evaluates the correlations between CM, SCC, and milk yield. Data were extracted from the Finnish national milk-recording database and from the health recording system. First and second lactation records of 87,861 Ayrshire cows calving between January 1998 and December 2000 were included. Traits studied were incidence of CM, test-day SCC, and test-day milk yield before and following CM. Genetic parameters were estimated using multitrait REML with a sire model. Results did not indicate that cows with genetically low SCC would be more susceptible to CM. The genetic correlation between CM in the first and second lactation was reasonably high (0.73), suggesting that susceptibility to mastitis remains similar across lactations. The genetic correlation between CM and milk yield traits was positive (from 0.38 to 0.56), confirming the genetic antagonism between production and udder health traits. The genetic correlation between SCC and milk was positive in the first lactation, but negative, or near zero in the second lactation. This indicates that breeding for lower SCC might not affect milk production in later lactations. The results of this study support the use of SCC as an indicator of mastitis and a tool for selection for mastitis resistance.     

Genetic parameters for milk somatic cell scores and relationships with production traits in French Lacaune dairy sheep

Records from 94,445 and 45,499 French Lacaune dairy ewes in first and second lactations, respectively, were used to estimate genetic parameters for somatic cell scores. Somatic cell count data came from an extensive recording scheme and sample testing that began in 1999 using the flocks enrolled in the official milk recording system. Somatic cell count data were from 2 to 4 test days per lactation. Lactation average and single test-day somatic cell scores were considered in multitrait sire models. The heritability estimate of lactation somatic cell score was close to 0.13 and similar for first and second parity. Heritabilities of somatic cell scores increased from first to fourth test day (from 0.07 to 0.11 in first lactation and from 0.05 to 0.13 in second lactation). Genetic correlations between somatic cell scores were high, usually more than 0.91, but lower between first test day and later test days in first lactation (0.64 to 0.88). The genetic correlations between lactation somatic cell score and milk yield, between lactation somatic cell score and fat content, and between lactation somatic cell score and protein content were 0.18, 0.04, and 0.03 in first lactation, respectively. The genetic antagonism between test day somatic cell score and milk yield measured in first lactation increased from beginning to the end of the lactation (0.05 to 0.23). This antagonism was slightly lower for somatic cell score in second lactation (from 0.09 to 0.14, and 0.08 for lactation mean). Environmental correlations in first lactation between lactation somatic cell score and milk yield, between lactation somatic cell score and fat content, and between lactation somatic cell score and protein content were -0.18, 0.13, and 0.30, respectively.     

Microbiological quality and somatic cell count of ewe milk with special reference to staphylococci

A total 1502 useful half udders of 762 Churra ewes from eight herds were aseptically sampled in midlactation to study both the bacteriological isolates and the SCC of milk. Corynebacteria, enterococci, micrococci, staphylococci, and streptococci represented 11.2, 2.9, 1.4, 78.9, and 3.1% of all isolates, respectively. Within staphylococci, novobiocin-sensitive species (71.1%) were much more frequently isolated than novobiocin-resistant ones (7.8%). Staphylococcus epidermidis was the most prevalent species (53.2% of the isolates). Log SCC of uninfected half udder milk was 4.86. Isolates of novobiocin-resistant coagulase-negative staphylococci, micrococci, and corynebacteria were associated to low values of log SCC (4.85 to 5.20). In contrast, infection by novobiocin-sensitive coagulase-negative staphylococci, streptococci, and enterococci organisms was related to a sharp inflammatory response with log SCC means between 5.92 and 6.32. The species that showed the highest log SCC were Pasteurella haemolytica (7.62), Streptococcus agalactiae (7.28), and Staphylococcus aureus (6.68). High prevalence of infections by novobiocin-sensitive staphylococci together with high SCC related to such infections show a relevant role of these organisms in ewe mastitis. Consequently, implementation of staphylococcal mastitis control programs would be of great interest in dairy ewe herds to improve microbiological and hygienic quality of milk.     

Assessment of the impact of somatic cell count on functional longevity in Holstein and Jersey cattle using survival analysis methodology

Survival analysis in a Weibull proportional hazards model was used to evaluate the impact of somatic cell count (SCC) on the involuntary culling rate of US Holstein and Jersey cows with first calvings from 1990 to 2000. The full data set, consisting of records from 978,043 Holstein and 250,835 Jersey cows, was divided into subsets (5 for Holsteins and 3 for Jerseys) based on herd average lactation SCC values. Functional longevity (also known as herd life or length of productive life) was defined as days from first calving until culling or censoring, after correcting for milk production. Our model included the time-dependent effects of herd-year-season, parity by stage of lactation interaction, within-herd-year quintile ranking for mature equivalent production, and lactation average SCC (rounded to the nearest 50,000 cells/mL), as well as the time-independent effect of age at first calving. Parameters of the Weibull distribution, as well as variance components for herd-year-season effects, were estimated within each group of herds. Mean failure and censoring times decreased as herd average SCC increased, and a nonlinear relationship was observed between SCC and longevity in all groups. The risk of culling for Holstein cows with lactation average SCC > 700,000 cells/mL was 3.4, 2.7, or 2.3 times greater, respectively, than that of Holstein cows with SCC of 200,000 to 250,000 cells/mL in herds with low, medium, or high average SCC. Likewise, the risk of culling for Jersey cows with lactation average SCC > 700,000 cells/mL was 4.0, 2.9, or 2.2 times greater, respectively, than that of Jersey cows with SCC of 200,000 to 250,000 cells/mL in low, medium, or high SCC herds. These trends may reflect more stringent culling of high SCC cows in herds with few mastitis problems. In addition, cows with lactation average SCC <100,000 cells/mL had a slightly higher risk of culling than cows with SCC of 100,000 to 200,000 cells/mL in both breeds, particularly in herds with high average SCC, where exposure to mastitis pathogens was likely.     

Genetic parameters of different measures of somatic cell counts in the Rendena breed

The objective of this study was to consider different and alternative methods of using somatic cell count (SCC) data recorded according to the Italian official milk recording system, estimating its genetic parameters and the correlations with the yield traits (milk, fat, and protein) in the Rendena breed. The SCC traits defined for genetic evaluation were somatic cell score, log of the total daily SCC (LTSCC, i.e., SCC multiplied by daily milk yield) individually recorded in a day of official control, and 3 different thresholds (≥80,000, ≥150,000, and ≥400,000 cells/mL) for somatic cells. A total of 187,052 test-day monthly records of milk, fat, and protein yields and SCC belonging to 11,718 cows were used to estimate heritability and genetic correlations between SCC and yield traits via a bi-trait repeatability test-day model using a Bayesian approach. The heritability values estimated for the threshold traits ranged from 0.036 to 0.065, less than those observed for monthly somatic cell score and LTSCC traits that were equivalent to 0.088 and 0.103, respectively. Higher genetic correlations were estimated between LTSCC trait and all productive traits (0.379 for milk, 0.240 for fat, and 0.370 for protein). The other SCC traits considered have shown low or almost null genetic correlations with the productive traits (from 0.008 between fat yield and SCC ≥150,000 cells/mL to 0.234 between protein yield and SCC ≥400,000 cells/mL) and almost all estimates included zero in the 95% highest posterior density region interval. These results indicated that genetic selection for milk, fat, and protein production negatively affects the LTSCC content and SCC ≥400,000 cells/mL but does not negatively influence the other somatic cell and threshold SCC traits in the Rendena breed. However, the complete framework of genetic relationships of SCC with all traits under selection should be considered when deciding on the possible inclusion of SCC in the breeding program of this small cattle population.     

Cow, farm, and herd management factors in the dry period associated with raised somatic cell counts in early lactation

This study investigated cow characteristics, farm facilities, and herd management strategies during the dry period to examine their joint influence on somatic cell counts (SCC) in early lactation. Data from 52 commercial dairy farms throughout England and Wales were collected over a 2-yr period. For the purpose of analysis, cows were separated into those housed for the dry period (6,419 cow-dry periods) and those at pasture (7,425 cow-dry periods). Bayesian multilevel models were specified with 2 response variables: ln SCC (continuous) and SCC >199,000 cells/mL (binary), both within 30 d of calving. Cow factors associated with an increased SCC after calving were parity, an SCC >199,000 cells/mL in the 60 d before drying off, increasing milk yield 0 to 30 d before drying off, and reduced DIM after calving at the time of SCC estimation. Herd management factors associated with an increased SCC after calving included procedures at drying off, aspects of bedding management, stocking density, and method of pasture grazing. Posterior predictions were used for model assessment, and these indicated that model fit was generally good. The research demonstrated that specific dry-period management strategies have an important influence on SCC in early lactation.     

Alternative somatic cell count traits as mastitis indicators for genetic selection

The aim of this study was to define alternative traits of somatic cell count (SCC) that can be used to decrease genetic susceptibility to clinical and subclinical mastitis (CM and SCM, respectively). Three kinds of SCC traits were evaluated: 1) lactation-averages of SCC, 2) traits derived from the proportion of test-day SCC above 150,000 cells/mL, and 3) patterns of peaks in SCC. Genetic parameters for these SCC traits and their genetic correlation with CM and SCM were estimated; CM and SCM were scored as binary traits. Two data sets (A and B) depending on CM recording were available. After editing, subset A contained 28,688 lactations from 21,673 cows in 394 herds. Subset B contained 56,726 lactations of 30,145 cows in 272 herds. Variance components for sire and permanent animal effects were estimated. Estimated heritabilities for all mastitis traits were around 0.03. Heritabilities for SCC traits ranged from 0.01 for patterns of peaks in SCC to 0.13 for lactation-average SCC. Genetic correlations between SCC traits and CM or SCM ranged from 0.55 to 0.93 for CM and from 0.55 to 0.98 for SCM. High genetic correlations were estimated between CM and SCC averaged over 250 d in milk (0.87), and between SCM and presence of test-day SCC >150,000 cells/mL (0.98) in subset A. In subset B, a high genetic correlation was estimated between CM and an SCC peak with a quick recovery (0.93) and between SCM and SCC averaged between 151 and 400 d (0.95). Partial genetic correlations were calculated to investigate the additional information of the alternative SCC traits, compared with lactation-average SCC. They showed that some traits remain informative for CM and others for SCM. Therefore, use of information from a combination of different SCC traits may be more successful in improving overall udder health than the traditional single SCC measure.     

Regularly fluctuating somatic cell count pattern in dairy herds

Online somatic cell count (SCC) measurement is widely used in dairy herds milked with automatic milking systems (AMS) and gives the opportunity to closely monitor individual cow udder health. Using automated SCC data, we observed cows displaying a remarkably regularly fluctuating SCC (rfSCC) pattern, which is described in this study. We aimed to (1) estimate the prevalence of rfSCC in cows milked by AMS, (2) characterize the rfSCC pattern, and (3) identify factors potentially associated with the rfSCC pattern. We analyzed 30-d episodes of composite SCC recordings of 1,000 cows from 55 dairy herds from 6 countries using an AMS with automated SCC measurement, and we identified the rfSCC pattern in 4.7% (95% CI: 3.5–6.2%) of these episodes. The rfSCC episodes had a median SCC of 701 × 1,000 cells/mL (2.5–97.5% quantile: 539–1,162), a median amplitude of 552 × 1,000 cells/mL (2.5–97.5% quantile: 409–886), and a median cycle length of 4.1 d (2.5–97.5% quantile: 3.7–4.9). Bacteriological culture data from quarter-milk samples collected every 2 wk in 1 Dutch AMS herd were analyzed, yielding no clear association between pathogen species and the rfSCC pattern found in that herd. Altogether, we described an intriguing phenomenon, present in almost 5% of the cows during a 1-mo study period. Further work is needed to quantify its importance in terms of udder health, but also to elucidate the mechanism behind this remarkable SCC pattern.     

Dynamics of somatic cell count (SCC) and differential SCC during and following intramammary infections

Somatic cell count is frequently used as an indicator of intramammary infections (IMI) in dairy cattle worldwide. The newly introduced differential SCC (DSCC) can potentially contribute to detection of IMI. The purpose of this study was to investigate the dynamics of SCC and DSCC after IMI. We used a data set with monthly samples from 2 Danish dairy herds through 1 yr, using bacterial culture to identify IMI. The dynamics of SCC and DSCC with regard to IMI were assessed at quarter level following new IMI with each of 3 defined pathogen groups, major, minor, or “other” pathogens, using general additive models. Both SCC and DSCC increased after IMI, with a more pronounced increase if major or other pathogens were detected compared with minor pathogens. We found that DSCC increased after IMI with other pathogens in both herds and, in herd 2, after IMI caused by major and minor pathogens. We also estimated the duration of increased SCC and DSCC when they exceeded a threshold, done separately for each pathogen group. Major pathogens had the longest-lasting effect in both herds for both SCC and DSCC. We conclude that the magnitude and duration of response of SCC and DSCC to IMI differs between herds and causative pathogens.