Lactation Mean Somatic Cell Score Research Articles

Genetic parameters for somatic cell score, milk yield and type traits in Nigerian Dwarf goats.

ObjectiveThis study was conducted to estimate multi-trait genetic parameters for somatic cell score (SCS), milk yield and type traits in Nigerian Dwarf (ND) goats from the United States.MethodsData from 1,041 ND goats in the United States with kiddings in 95 herds were used to estimate multi-trait genetic parameters for SCS, milk (MILK), fat (FAT), and protein (PROT) yields, and 14 type traits. An 18-trait mixed linear animal model for lactation mean SCS (Log2), MILK, FAT, PROT, and 14 type traits was applied. A factor analytic approach (FA1) in ASReml software was used to obtain convergence.ResultsAverages for SCS were low (2.85±1.29 Log2), and were 314±110.6, 20.9±7.4, and 14±4.9 kg, respectively, for MILK, FAT, and PROT. Heritabilities for SCS, MILK, FAT, and PROT were 0.32, 0.16, 0.16, and 0.10, respectively. The highest heritabilities for type traits were for stature (0.72), teat diameter (0.49), and rump width (0.48), and the lowest estimates were for dairyness (0.003) and medial suspensory ligament (0.03). Genetic correlations of SCS with MILK, FAT, and PROT were positive but low (0.25, 0.18, and 0.23, respectively). Genetic and phenotypic correlations between MILK, FAT, and PROT were high and positive (≥0.66). Absolute values of genetic correlations involving SCS with type traits were generally low or no different from zero. Most of the phenotypic correlations involving SCS with type traits were low. No serious unfavorable genetic correlations between milk yield traits and SCS or between milk yield traits or SCS and type traits were found.ConclusionGenetic variation exists in the ND breed for most studied traits. The development of selection programs based on these estimates may help accelerate favorable multi-trait genetic changes in this breed.

A genetic study of new udder health indicator traits with data from automatic milking systems

The current study aimed to investigate new udder health traits based on data from automatic milking systems (AMS) for use in routine genetic evaluations. Data were from 77 commercial herds; out of these, 24 had equipment for measuring online cell count (OCC), whereas all had data on electrical conductivity (EC). A total of 4,714 Norwegian Red dairy cows and 2,363,928 milkings were included in the genetic analyses. Electrical conductivity was available on quarter level for each milking, whereas OCC was measured per milking. The AMS traits analyzed were log-transformed online cell count (lnOCC), maximum conductivity (ECmax), mean conductivity (ECmean), elevated mastitis risk (EMR), and log-transformed EMR (lnEMR). In addition, lactation mean somatic cell score (LSCS) was collected from the Norwegian dairy herd recording system. Elevated mastitis risk expresses the probability of a cow having mastitis and was calculated from smoothed lnOCC values according to individual trend and level of the OCC curve. The udder health traits from AMS were analyzed as repeated milkings from 30 to 320 DIM, and LSCS as repeated parities. In addition, both ECmax and lnOCC were analyzed as multiple traits by splitting the lactation into 5 periods. (Co)variance components were estimated from bivariate mixed linear animal models, and investigated traits showed genetic variation. Estimated heritabilities of ECmean, ECmax, and lnEMR were 0.35, 0.23, and 0.12, respectively, whereas EMR and lnOCC both showed heritabilities of 0.09. Heritability varied between periods of lactation, from 0.04 to 0.13 for lnOCC and from 0.12 to 0.27 for ECmax, although standard errors of certain periods were large. Genetic correlations among the AMS traits ranged from 0 to 0.99. The genetic correlations between EC-based traits and OCC-based traits in AMS were 0. Genetic correlations with LSCS were favorable, ranging from 0.37 to 0.80 (±0.11-0.22). The strongest correlation (0.80 ± 0.13) was found between LSCS and lnEMR. Results question the value of ECmax and ECmean as indicators of udder health in genetic evaluations and suggest OCC to be more valuable in this manner. This study demonstrates a potential of using AMS data as additional information on udder health for genetic evaluations, although further investigation is recommended before these traits can be implemented.

Genetic associations between somatic cell score and pathogen‐specific subclinical mastitis in Norwegian Red cows

The aim of this study was to estimate heritabilities of and genetic correlations between pathogen-specific subclinical mastitis (SCM) traits and lactation mean somatic cell score (LSCS) in Norwegian Red cattle. Based on data from 130733 first-lactation cows four binary pathogen-specific SCM traits, Staphylococcus aureus, Streptococcus dysgalactiae, Streptococcus uberis and coagulase-negative staphylococci SCM, were analysed together with unspecific SCM and LSCS using a multivariate sire model with threshold models for binary traits and a linear model for LSCS. Posterior means (SD) of heritabilities were 0.17 (0.01) for LSCS, 0.11 (0.01) for liability to unspecific SCM and ranged from 0.04 (Staph. aureus) to 0.14 (Strep. dysgalactiae) for liability to pathogen-specific SCM. Genetic correlations were positive and moderate to high, ranging from 0.37 to 0.98. All genetic correlations except the one between LSCS and unspecific SCM were lower than 1, indicating that SCM caused by different pathogens can be considered as partly different traits.

Genetic analysis of clinical mastitis and somatic cell count traits in Austrian Fleckvieh cows

The objectives of this study were to investigate genetic associations between clinical mastitis (CM) and different somatic cell count traits, and to examine their relationships, in terms of estimated breeding values, with other traits that are routinely evaluated in Austrian Fleckvieh dual-purpose cows. Records on veterinary treatments of CM were available from the Austrian health-monitoring project. For CM, 3 intervals in early lactation were considered: −10 to 50 d, 51 to 150 d, and −10 to 150 d after calving. Within each interval, absence or presence of CM was scored as 1 or 0 based on whether or not the cow had recorded at least one veterinary treatment of CM. The average somatic cell score of the first 2 test-days after calving was defined as early lactation average somatic cell score, and lactation mean somatic cell score was the average of all test-day somatic cell scores from 8 to 305 d after calving. Subclinical mastitis was expressed as a binary trait based on prolonged elevated somatic cell counts. If somatic cell counts on 3 consecutive test-days in the interval from 8 to 305 d after calving were above 200,000 cells/mL, the binary variable subclinical mastitis was defined as 1 and otherwise 0. Records of Austrian Fleckvieh cows, with calving from January 1, 2007, to February 28, 2009, were analyzed using univariate and bivariate sire models. Threshold liability models were applied for binary traits, and Gaussian models were used for early lactation average somatic cell score and lactation mean somatic cell score. A Bayesian approach using Gibbs sampling was applied for genetic analyses. Posterior means of heritability of liability to CM were 0.06 and 0.02 in the first and second interval, respectively, and 0.05 in the full period (−10 to 150 d). Heritability estimates of somatic cell count traits were higher (0.09 to 0.13). The posterior mean of the genetic correlation between CM in lactation period 1 (−10 to 50 d after calving) and 2 (51 to 150 d after calving) was close to unity. Posterior means of genetic correlations between CM and somatic cell count traits ranged from 0.64 to 0.77. Because CM and somatic cell count describe different aspects of udder health, information on both traits should be considered for selection of bulls. Correlations of sire breeding values revealed that especially the udder conformation trait udder depth may be useful as additional information to reduce both CM and somatic cell count.

A model for the genetic evaluation of number of clinical mastitis cases per lactation in Czech Holstein cows

Cases of mastitis from 9,550 lactations of 6,242 cows were recorded on 5 farms in the Czech Republic from 1996 to 2008. The number of clinical mastitis (CM) cases per cow adjusted to a lactation length of 305 d was analyzed with 4 linear single-trait animal models and one 3-trait model, which also included lactation mean somatic cell score (SCS) and 305-d milk yield. Factors included in the model of choice were parity, combined effect of herd and a 2-yr calving period, calving season, permanent environmental effect of the cow, and additive genetic effect of the cow. From both the single-trait and multiple-trait models, estimated heritability of number of CM cases was 0.11 (±0.015 for the multiple-trait model). Permanent environmental effects accounted for approximately one-third of the phenotypic variance. Heritability estimates for lactation mean SCS and 305-d milk yield were 0.17±0.019 and 0.25±0.011, respectively, and genetic correlations of these traits with number of CM cases were 0.80±0.059 and 0.34±0.079, respectively. Genetic evaluation of the number of CM cases in Czech Holsteins could be carried out including data from all parities using a 3-trait animal model with SCS and milk yield as additional traits.

Short Communication: Correlated Selection Responses in Somatic Cell Count from Selection Against Clinical Mastitis

Correlated selection responses in lactation mean somatic cell score (LSCS) were estimated for groups of cows selected for high protein yield and low mastitis frequency, respectively. Selection for increased milk production resulted in an unfavorable correlated response for LSCS, whereas direct selection against clinical mastitis resulted in a favorable correlated selection response. After 6 cow generations, the genetic difference between the high protein yield group and the low mastitis group was 0.3 units LSCS, equivalent to a difference in somatic cell count of approximately 15,000, assuming deviations from a population mean LSCS of 4.1.

Genetic Relationship Between Culling, Milk Production, Fertility, and Health Traits in Norwegian Red Cows

First-lactation records on 836,452 daughters of 3,064 Norwegian Red sires were used to examine associations between culling in first lactation and 305-d protein yield, susceptibility to clinical mastitis, lactation mean somatic cell score (SCS), nonreturn rate within 56 d in heifers and primiparous cows, and interval from calving to first insemination. A Bayesian multivariate threshold-linear model was used for analysis. Posterior mean of heritability of liability to culling of primiparous cows was 0.04. The posterior means of the genetic correlations between culling and the other traits were −0.41 to 305-d protein yield, 0.20 to lactation mean SCS, 0.36 to clinical mastitis, 0.15 to interval from calving to first insemination, −0.11 to 56-d nonreturn as heifer, and −0.04 to 56-d nonreturn as primiparous cow. As much as 66% of the genetic variation in culling was explained by genetic variation in protein yield, clinical mastitis, interval of calving to first insemination, and 56-d nonreturn in heifers, whereas contribution from the SCS and 56-d nonreturn as primiparous cow was negligible, after taking the other traits into account. This implies that for breeds selected for a broad breeding goal, including functional traits such as health and fertility, most of the genetic variation in culling will probably be covered by other traits in the breeding goal. However, in populations where data on health and fertility is scarce or not available at all, selection against early culling may be useful in indirect selection for improved health and fertility. Regression of average sire posterior mean on birth-year of the sire indicate a genetic change equivalent to an annual decrease of the probability of culling in first-lactation Norwegian Red cattle by 0.2 percentage units. This genetic improvement is most likely a result of simultaneous selection for improved milk yield, health, and fertility over the last decades.

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 1.4million 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.

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.

Genetic analysis of somatic cell count and milk traits in Manchega ewes: Mean lactation and test-day approaches

A total of 36 873 lactation records of Somatic Cell Counts (SCC) and 32 844 of milk yield, protein and dry matter contents of 28 694 Spanish Manchega ewes from 118 herds were used to estimate genetic parameters under different approaches. In a first step estimations were carried out under multivariate animal models including lactation mean somatic cell score (SCSL) and other milk traits (milk yield and protein and dry matter contents). These analysis were made within 1st, 2nd and 3rd lactations separately and also with multiple lactations under repeatability and multivariate animal models, considering SCSL as repeated measures of the same trait or as different traits among lactations. In a second step, a Test Day approach was analysed also within and among lactations. SCS test day were considered as different traits within lactations and as repeated measures of the same trait within and across lactations by fitting two permanent environmental effects. Heritability estimates of SCSL and SCS test day under models without repeatability were higher (0.12–0.24) than estimates obtained when permanent environmental effect was fitted (0.04). This fact also happened for the other milk traits. Genetic correlations between SCS test day in all lactations were high (0.78–0.99), showing similar genetic basis of this trait within lactations. Genetic correlations between SCSL in different lactations were also high (0.97–0.99), suggesting similar behavior of this trait across lactations. Genetic correlation between SCSL and milk yield was low and negative (−0.12 to −0.16), showing a slightly favourable genetic relationship between these traits, however was positive between SCSL and protein content (0.22). Positive genetic correlations between SCSL and milk yield found in cattle and other sheep breeds could be due to models, population’s structure and selection process of breeds. Thus, in breeds with a long history of selection to improve milk yield, a fixation of alleles that diminish mastitis resistance could occur.

Genetic and phenotypic relationships among milk production and composition traits in primiparous Holstein cows in two different herd environments

Genotype by environment interactions for mature equivalent milk yield (MEM), mature equivalent protein yield (MEP), mature equivalent fat yield (MEF), protein percentage (P%), fat percentage (F%), and lactation mean somatic cell score (LMSCS) were studied using 248 230 first parity records of Holstein cows calving from 1987 to 1994, daughters of 588 sires in 3042 herds. Herds were classified into low and high yield environment classes. Genetic parameters were estimated with bivariate linear mixed models using the multiple trait derivative free software ( mtdfreml). For low yield environment herds, heritabilities for MEM, MEP, MEF, P%, F% and LMSCS were 0.22, 0.20, 0.23, 0.51, 0.58 and 0.11, while for high yield environment herds they were 0.30, 0.27, 0.27, 0.56, 0.56 and 0.09. All genetic correlations, except between MEM and P% and F%, were different in low and high yield environment herd classes. These results indicate that differences in management between the two herd environment classes modify the genetic expression of the traits studied and their genetic association. This suggests that, when these traits are considered in a selection index, appropriate weights for these traits depend on the herd environment class. These results also indicate that different correlated responses for MEP, MEF and LMSCS are expected in high relative to low yield environment herds when selection is based on MEM only.

Effect of Herd Environment on the Genetic and Phenotypic Relationships Among Milk Yield, Conception Rate, and Somatic Cell Score in Holstein Cattle

A total of 248,230 primiparous records of Holstein cows calving from 1987 to 1994 (daughters of 588 sires in 3042 herds) was used to evaluate potential genotype by environment interactions among mature equivalent milk yield, lactation mean somatic cell score, and conception rate at first service. Herds were classified into low and high environmental groups using three different criteria: standard deviation of herd mature equivalent milk yield, a combination of herd mature equivalent milk yield mean and standard deviation, and the herd mean of body weight at first calving divided by age at first calving. Genetic parameters were modeled by using multiple-trait linear mixed models and were fitted using the multiple-trait derivative-free software. Heritabilities for mature equivalent milk yield, lactation mean somatic cell score, and conception rate at first service were 0.221, 0.106, and 0.015 in low environment herds and 0.300, 0.093, and 0.009 in high environment herds, respectively. Genetic (and phenotypic) correlations between mature equivalent milk yield and lactation mean somatic cell score, mature equivalent milk yield and conception rate at first service, and lactation mean somatic cell score and conception rate at first service were 0.277, –0.417 and –0.209, (–0.049, –0.180, and –0.040) and 0.173, –0.318, and –0.144, (–0.087, –0.166, and –0.035) in low and high environment herds, respectively. The genetic correlations between pairs of traits were consistently smaller in high environment herds, suggesting that differences in management between the two environment levels lessened the antagonistic genetic association between the traits studied. A long-range plan for low environment herds should focus on improving the level of management, which would greatly reduce the unfavorable correlated changes in lactation mean somatic cell score and conception rate at first service associated with the genetic improvement of mature equivalent milk yield.

Development of an Udder Health Index for Sire Selection Based on Somatic Cell Score, Udder Conformation, and Milking Speed

Genetic parameters of subjectively scored milking speed and somatic cell score were estimated using REML and a sire model. Approximately 250,000 records were used. Heritabilities were 0.15 for milking speed and 0.14 and 0.16 for lactation mean somatic cell score (SCS) for first and second lactations, respectively. Genetic correlations between milking speed and SCS were 0.41 and 0.25 for first and second lactations, respectively, indicating that faster milking was associated with increased SCS. Genetic parameters for milking speed, SCS, and udder conformation were estimated using REML and an animal model. Records from approximately 120,000 cows were used. Genetic correlations were greatest for udder depth (–0.26) with SCS and for width of rear udder attachment (–0.24) with milking speed. An udder health index for use in sire selection was developed for an aggregate genotype that included subclinical mastitis in lactations 1 and ≥2, clinical mastitis in lactations 1 and ≥2, and milking time. Respective economic weights were –$12, –$31, –$15, –$59, and –$11 per genetic standard deviation. Traits in the selection index were milking speed, udder conformation, and SCS in first and later lactations. Standardized weights for a simple index for sires based on estimated breeding value from 50 daughter records were 5.5, –1.2, 3.5, –3.9, and –8.7 for udder depth, front teat length, milking speed, and SCS for first and later lactations, respectively. The accuracy of the index was 0.776, an increase of 15% over an index with only SCS.