Genetic trends for milk yield of Jerseys and correlated changes in productive and reproductive performance

Estimates of genetic trends in 24 measures of milk and constituent yields, somatic cell counts, and reproduction were obtained from 935 records of 374 Jerseys in a single herd. Data were obtained from a designed project for single-trait selection from 1969 through 1987. One line was subjected to selection solely for milk yield and included 259 cows; an unselected control line included 115 cows. Estimates of trends were based on differences in linear phenotypic trends between lines for first lactations, all lactations, and for 305-d and total records. The genetic changes in milk yield for these four data sets were 1.22 to 1.48%/yr (36.8 to 41.0 kg per cow yr) and 0.54 to 1.64%/yr for five constituent yields. Except for the percentages of minerals plus lactose, all constituent percentages decreased by 0.05 to 0.60%/yr. The ratios of protein to fat and solids-not-fat to fat increased 0.30 to 0.54%/yr, respectively. The number of services required per conception increased (0.17%) in first parity records and in all data (0.69%). The intervals from parturition to first estrus and from parturition to first service decreased in first lactation (1.19 and 0.82%) annually but increased (1.25 and 0.01%) in all data. Age of heifers at first estrus decreased by 0.44% annually. Most of the five measures of somatic cells decreased in first lactations but increased for all data. Estimates of realized genetic correlations of 14 measures of constituent yield and composition (four correlations each) agreed well with values expected from the literature. The results quantified change in milk yield, constituent yields and percentages, reproductive performance, and somatic cell counts in a single herd and should prove useful in the development of selection programs for dairy cattle.     

Genetic parameters and responses of linear type,yield traits,and somatic cell scores to divergent selection for predicted transmitting ability for type in Holsteins

The objective was to examine the direct and correlated responses of linear type, yield traits, and somatic cell scores (SCS) to divergent selection for predicted transmitting ability for type (PTAT) in Holsteins, while maintaining selection for yield traits across lines. For four generations, one-half of the University of Nebraska research Holstein herd was bred to Holstein sires with PTAT > 1.50 and the other half to sires with PTAT < 1.25, with nearly equal predicted transmitting abilities for yield traits for both groups. Estimates of genetic and residual correlations and heritabilities were obtained from REML estimates of (co)variance components. Model for type traits included fixed effect of date cows were classified, effects of age in days at freshening, and stage of lactation at classification. Year-season when cows freshened was fixed effect in model for yield and SCS. Animal genetic and residual effects were random. Final score, milk, fat, and protein yields, and SCS had heritability estimates of 0.38, 0.13,0.22, 0.09, and 0.38, respectively. Heritability estimates for type traits ranged from 0.04 to 0.52. Estimates of genetic correlations of final score with SCS and milk, fat, and protein yields were -0.64, 0.01, -0.18, and 0.06, respectively. Estimates of genetic correlations among linear type traits ranged from -0.77 to 1.00. Means of estimated breeding values for final score, stature, strength, body depth, fore udder attachment, rear udder height and width, udder cleft, udder depth, and front teat placement were significantly different between lines in the third generation. Milk, fat, and protein yields were not significantly different between lines in third generation, whereas SCS was significantly different. Estimate of genetic correlation between final score and SCS suggest that selection on PTAT would result in a change for SCS. In this study, divergent selection on PTAT of sires had a significant effect on udder and body traits, but little or no effect on feet and leg traits.     

Genetic correlations of mid-infrared-predicted milk fatty acid groups with milk production traits

The objective of this research was to estimate the genetic correlations between milk mid-infrared-predicted fatty acid groups and production traits in first-parity Canadian Holsteins. Contents of short-chain, medium-chain, long-chain, saturated, and unsaturated fatty acid groupings in milk samples can be predicted using mid-infrared spectral data for cows enrolled in milk recording programs. Predicted fatty acid group contents were obtained for 49,127 test-day milk samples from 10,029 first-parity Holstein cows in 810 herds. Milk yield, fat and protein yield, fat and protein percentage, fat-to-protein ratio, and somatic cell score were also available for these test days. Genetic parameters were estimated for the fatty acid groups and production traits using multiple-trait random regression test day models by Bayesian methods via Gibbs sampling. Three separate 8- or 9-trait analyses were performed, including the 5 fatty acid groups with different combinations of the production traits. Posterior standard deviations ranged from <0.001 to 0.01. Average daily genetic correlations were negative and similar to each other for the fatty acid groups with milk yield (?0.62 to ?0.59) and with protein yield (?0.32 to ?0.25). Weak and positive average daily genetic correlations were found between somatic cell score and the fatty acid groups (from 0.25 to 0.36). Stronger genetic correlations with fat yield, fat and protein percentage, and fat-to-protein ratio were found with medium-chain and saturated fatty acid groups compared with those with long-chain and unsaturated fatty acid groups. Genetic correlations were very strong between the fatty acid groups and fat percentage, ranging between 0.88 for unsaturated and 0.99 for saturated fatty acids. Daily genetic correlations from 5 to 305 d in milk with milk, protein yield and percentage, and somatic cell score traits showed similar patterns for all fatty acid groups. The daily genetic correlations with fat yield at the beginning of lactation were decreasing for long-chain and unsaturated fatty acid groups and increasing for short-chain fatty acids. Genetic correlations between fat percentage and fatty acids were increasing at the beginning of lactation for short- and medium-chain and saturated fatty acids, but slightly decreasing for long-chain and unsaturated fatty acid groups. These results can be used in defining fatty acid traits and breeding objectives.     

Genetic and Phenotypic Relationships Between Lactation Cell Counts and Milk Yield and Composition of Holstein Cows

Monthly somatic cell count data were collected between February 1977 and February 1982 for Holstein cows in 928 herds enrolled on the Quebec Dairy Herd Analysis Service. The geometric mean of the log monthly cell counts was calculated for each lactation. Official lactation records for 305-day milk, fat, and protein yields, and fat and protein percents were obtained for same cows. There were 18,189 cows in first lactation representing 257 sires, 13,225 in second lactation representing 206 sires, and 8,683 in third lactation representing 151 sires. Heritabilities of yield traits and protein percent increased across three lactations. Heritability of fat percent was similar in first and third lactations but decreased slightly in second lactations. Heritability of lactation cell count was small, being least in second lactations. Genetic correlations between lactation cell count and yield traits were positive in first lactations, small and negative in second lactations, and small and positive in third lactations. Genetic correlations between lactation cell count and fat and protein contents were small in the three lactations. Phenotypic correlations between lactation cell count and production traits were small in each of the three lactations. Genetic correlations between yield traits in early lactation and lactation cell count in a subsequent lactation were positive. The genetic correlation between protein percent in an early lactation and cell count in a later lactation was large between first and second lactations, decreased between second and third lactation, and small between first and third lactations. Genetic correlations were small and negative for fat percent.     

Genetic correlation patterns between somatic cell score and protein yield in the Italian Holstein-Friesian population

Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.     

Genetic parameters for first milk production and composition traits for Holsteins using multivariate restricted maximum likelihood

Heritabilities and genetic and phenotypic correlations among yields of milk, fat, protein, and percentages of fat and protein were estimated from 40,984 first lactation records of daughters of 488 young and 75 proven Holstein sires using multivariate REML and a sire model accounting for relationships and sire groups. Proven sires were treated as fixed effects. Heritabilities for yields of milk, fat, protein, and percentages of fat and protein were .29, .31, .25, .65, and .61, respectively. Genetic correlations of milk with yields of fat, protein, and percentages of fat and protein and correlations of fat yield with fat percentage were .45, .79, -.49, -.54 and .56, respectively. Genetic correlations among yields and among percentage of fat and protein were the same (.62). Genetic and phenotypic correlations of protein percentage with fat and protein yields and correlations of fat percentages with protein yield were small (-.13 to .11). Phenotypic correlations were .73 to .90 among yields of milk, fat, and protein; -.31 for milk and fat percentage; -.39 for milk and protein percentage; and .38 for fat yield and fat percentage. Estimates were consistent with an earlier study utilizing data from the same population and also with other reports.     

Potential of somatic cell concentration in milk as a sire selection criterion to reduce mastitis in dairy cattle

Lactation records for somatic cell counts in milk, bacteriological culture results, antibiotic treatment for mastitis, and production were formed for cows in 30 cooperating dairy herds in Virginia. A second data set, including somatic cell counts and production information for cows in approximately 400 herds in Virginia (not including the original 30), was used to evaluate sires genetically for somatic cell count. Approximate genetic correlations between measures of cell count and measures of infection ranged from .36 to .67. These were highest (lowest) for frequency of infection by major (minor) pathogens. Corresponding phenotypic correlations were similar but slightly smaller. Neither somatic cell counts nor measures of infection were well correlated with treatment. Production traits generally had small, negative genetic and phenotypic relationships with cell counts, rates of infection, and measures of treatment. Correlations for evaluations of sires for cell count were positive with daughter averages for infection rates (.20 to .38) and treatment measures (.02 to .13). Largest (absolute value) correlations between evaluations of sires for cell count and production traits were for fat percentage (-.38) and fat yield (-.28). Evaluation and selection of sires for decreased somatic cell count may augment management and treatment programs in the reduction of mastitis incidence.     

Genetic correlations among production, body size, udder, and productive life traits over time in Holsteins

Genetic correlations among milk, fat, and protein yields; body size composite (BSC); udder composite (UDC); and productive life (PL) in Holsteins were investigated over time. The data set contained 25,280 records of cows born in Wisconsin between 1979 and 1993. The multiple trait random regression (MT-RR) animal model included registration status, herd-year, age group, and stage of lactation as fixed effects; additive genetic effects with random regressions (RR) on year of birth using the first-order Legendre polynomial; and residual effects. Heterogeneous residual variances were considered in the model. Estimates of variance components and genetic correlations among traits from MT-RR were compared with those estimated with a multiple trait interval (MT-I) model, which assumed that every 3-yr interval was a separate trait and included the same effects as in the MT-RR model except for the RR. Genetic correlations estimated with MT-RR and MT-I models over time among all traits were compared with correlations among breeding values predicted with the single trait (ST) model without RR. Correlations among breeding values predicted with MT-RR, ST, and MT models were also calculated. Additive genetic and residual variances for all traits except PL increased over time; those for PL were constant. As a result, heritability estimates had no significant changes during the 15 yr. Genetic correlations of PL with milk, fat, protein, and BSC declined to zero or negative; those with UDC remained positive. Correlations among breeding values predicted with ST, MT, and MT-RR models were relatively high for all traits except PL. Genetic correlations between PL and other traits varied over time, with some correlations changing sign. For accurate indirect prediction of PL from other traits, the genetic correlations among the traits need to be re-estimated periodically.     

Best prediction of yields for long lactations

Lactation records of any reasonable length now can be processed with the selection index method known as best prediction (BP). Previous prediction programs were limited to the 305-d standard used since 1935. Best prediction was implemented in 1998 to calculate lactation records in USDA genetic evaluations, replacing the test interval method used since 1969 to calculate lactation records. Best prediction is more complex but also more accurate, particularly when testing is less frequent. Programs were reorganized to output better graphics, give users simpler access to options, and provide additional output, such as BP of daily yields. Test-day data for 6 breeds were extracted from the national dairy database, and lactation lengths were required to be ≥500 d (Ayrshire, Milking Shorthorn) or ≥800 d (all others). Average yield and SD at any day in milk (DIM) were estimated by fitting 3-parameter Wood's curves (milk, fat, protein) and 4-parameter exponential functions (somatic cell score) to means and SD of 15- (≤300 DIM) and 30-d (>300 DIM) intervals. Correlations among TD yields were estimated using an autoregressive matrix to account for biological changes and an identity matrix to model daily measurement error. Autoregressive parameters (r) were estimated separately for first (r = 0.998) and later parities (r = 0.995). These r values were slightly larger than previous estimates due to the inclusion of the identity matrix. Correlations between traits were modified so that correlations between somatic cell score and other traits may be nonzero. The new lactation curves and correlation functions were validated by extracting TD data from the national database, estimating 305-d yields using the original and new programs, and correlating those results. Daily BP of yield were validated using daily milk weights from on-farm meters in university research herds. Correlations ranged from 0.900 to 0.988 for 305-d milk yield. High correlations ranged from 0.844 to 0.988 for daily yields, although correlations were as low as 0.015 on d 1 of lactation, which may be due to calving-related disorders that are not accounted for by BP. Correlations between 305-d yield calculated using 50-d intervals from 50 to 250 DIM and 305-yield calculated using all TD to 500 DIM increased as TD data accumulated. Many cows can profitably produce for >305 DIM, and the revised program provides a flexible tool to model these records.     

Associations among milk yield, yield persistency, conception, and culling of Israeli Holstein dairy cattle

Genetic correlations among Predicted Differences for milk yield corrected for economic value of fat content, annualized yield, yield persistency, conception rate, and culling rate were estimated. Correlations were .43 between yield persistency and annualized yield, .42 between yield persistency and conception rate, and .1 between annualized yield and conception rate. For Predicted Differences for these traits computed separately for each of first three parities, correlations between pairs were highest for annualized yield and lowest for culling rate. Regression coefficients for conception rate from cow insemination records on daily yield preceding insemination and on absolute change of yield during month of insemination were significantly negative for the first three parities. A positive pleiotropic effect for yield, yield persistency, and conception rate was suggested; therefore, progeny testing for yield persistency may improve yield and conception rate. High yields and large changes of yield during month of insemination adversely affected conception rate of cows within herds.     

Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle

The objective of this research was to estimate heritabilities of milk urea nitrogen (MUN) and lactose in the first 3 parities and their genetic relationships with milk, fat, protein, and SCS in Canadian Holsteins. Data were a random sample of complete herds (60,645 test day records of 5,022 cows from 91 herds) extracted from the edited data set, which included 892,039 test-day records of 144,622 Holstein cows from 4,570 herds. A test-day animal model with multiple-trait random regression and the Gibbs sampling method were used for parameter estimation. Regression curves were modeled using Legendre polynomials of order 4. A total of 6 separate 4-trait analyses, which included MUN, lactose, or both (yield or percentage) with different combinations of production traits (milk, fat and protein yield, fat and protein percentages, and somatic cell score) were performed. Average daily heritabilities were moderately high for MUN (from 0.384 to 0.414), lactose kilograms (from 0.466 to 0.539), and lactose percentage (from 0.478 to 0.508). Lactose yield was highly correlated with milk yield (0.979). Lactose percentage and MUN were not genetically correlated with milk yield. However, lactose percentage was significantly correlated with somatic cell score (−0.202). The MUN was correlated with fat (0.425) and protein percentages (0.20). Genetic correlations among parities were high for MUN, lactose percentage, and yield. Estimated breeding values (EBV) of bulls for MUN were correlated with fat percentage EBV (0.287) and EBV of lactose percentage were correlated with lactation persistency EBV (0.329). Correlations between lactose percentage and MUN with fertility traits were close to zero, thus diminishing the potential of using those traits as possible indicators of fertility.     

Correlations between production traits in first lactation Holstein cows

Genetic and phenotypic correlations between milk yield, fat (yield and percent, protein (yield and percent), and somatic cell count in first lactation Holstein cows were estimated using a multivariate restricted maximum likelihood algorithm. There were 18,189 daughters of 257 sires in 928 herds. Genetic correlations between pairs of yield traits were all positive (.73 to .88), but phenotypic correlations with somatic cell count were small and negative. Genetic correlations between somatic cell count, and fat percent, and protein percent were negative, -.11. Milk yield, fat yield, and protein yield had heritabilities of .36, .38, and .25.     

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.     

Prediction of daily and lactation yields of milk, fat, and protein using an autoregressive repeatability test day model

We evaluated the accuracy of an autoregressive multiple-lactation test day (ATD) model to predict missing test day yields of milk, fat, and protein to obtain cumulative 305-d records for cows with incomplete or in-progress lactations. The data consisted of more than one million observations of daily yields on test days in the first 3 lactations of over 75,000 Portuguese Holstein cows. Differences between actual (estimates from complete lactations using the test interval method) and ATD-predicted 305-d yields were negligible and smaller than those predicted by the test interval method. The ATD procedure tended to slightly underestimate cumulative lactation yields, whereas the test interval method substantially overestimated them. Smaller differences obtained by the ATD procedure resulted in less biased estimates of lactation yield, which also implies greater accuracy. As expected, the correlations between actual and predicted lactation yields increased with the number of test days from 0.831 to 0.997. Average correlations (by parity) between actual and ATD-predicted yields ranged from 0.977 to 0.984. Correlations between actual test day yields and corresponding predicted yields exceeded 0.5 for up to 7 time-intervals from the last test day yield used to predict cumulative yield of projected lactations. These correlations indicate the good predictive ability of the ATD method. From a producer's viewpoint, these advantages underwrite management because most on-farm selection decisions are based on the producing abilities of cows. Implementation of ATD methodology does not require special computing capability and is easily transferable to the farm level.     

Heritability and genetic correlations of test day milk yield and composition,individual laboratory cheese yield,and somatic cell count for dairy ewes

Genetic parameters for milk yield, contents of fat, total protein, casein and serum protein, individual laboratory cheese yield, and somatic cell counts (SCC) were estimated from 7492 monthly test-day records of 1119 Churra ewes. Estimates were from multivariate REML using analytical gradients (AG-REML) procedures. Except for fat content, estimates for the other routinely recorded traits (milk yield, protein content, and SCC) agreed with those previously obtained in this and other dairy sheep populations. Protein content and composition had the highest heritabilities and repeatabilities. Heritabilities for protein and casein contents were very similar (0.23 and 0.21, respectively), and genetic correlation between the traits was close to unity (0.99). Accordingly, casein content is not advisable as an alternative to protein content as a selection criterion in dairy ewes; it does not have any compelling advantages and costs more to measure. Individual laboratory cheese yield (ILCY) obtained with Churra ewes had a low heritability (0.08), suggesting that potential for selection for this parameter would be possible but is not recommended. All correlations with ILCY were high and positive except for milk yield. A high SCC was accompanied by an increase in serum protein content and involved a loss in milk yield.     

Genetic parameters for yield traits of cows treated or not treated with bovine somatotropin

The objective of this study was to estimate genetic correlations between yield traits of cows treated with bovine somatotropin (bST) and the same yield traits of untreated cows. Lactation records from registered Holstein cows were divided by parity into 3 data sets: 1, 2, and 3 through 5. Approximately 10% of the records in each data set were from cows treated with bST. The numbers of records of treated and untreated cows in the data sets were 4,337 and 48,765; 3,730 and 37,796; and 3,645 and 33,957. Two-trait animal models (records for cows treated or not treated) were used to estimate genetic parameters for milk production traits and somatic cell score (SCS). Estimates of heritability for milk yield for records of treated and untreated cows for the 3 data sets were 0.13, 0.16, and 0.09, and 0.18, 0.18, and 0.14, respectively, with estimates of repeatability of 0.50 and 0.41 for data set 3. Estimates of heritability for fat yield for records of treated and untreated cows were 0.31, 0.16, and 0.12, and 0.27, 0.21, and 0.16. Estimates of repeatability were 0.50 and 0.43 for data set 3. Heritability estimates for protein yield for records of treated and untreated cows were 0.13, 0.17, and 0.12, and 0.20, 0.23, and 0.16, with estimates of repeatability of 0.52 and 0.47. Estimates of heritability for SCS for treated and untreated cows were 0.08, 0.15, and 0.13, and 0.11, 0.13, and 0.13 with repeatability estimates of 0.52 and 0.45. Estimates of genetic correlations between milk yields with and without bST treatment in lactations 1, 2, and 3 to 5 were all 0.99. Estimates of genetic correlations for fat and protein yields were 0.96 for all data sets. Estimates for SCS were 0.99. Estimates of genetic correlations between records of treated and untreated cows were large enough to conclude that records of treated and untreated cows could be considered to be one trait, with treatment as a fixed effect to account for differences in means.     

Inferring relationships between somatic cell score and milk yield using simultaneous and recursive models

A Bayesian analysis via Markov chain Monte Carlo methods extending the simultaneous and recursive model of Gianola and Sorensen (2004) was proposed to account for possible population heterogeneity. The method was used to infer relationships between milk yield and somatic cell scores of Norwegian Red cows. Data consisted of test-day records of milk yield and somatic cell score of first-lactation cows during the first 120 d of lactation. Results suggested large negative direct effects from somatic cell score to milk yield and small reciprocal effects from milk yield to somatic cell score. The direct effects were larger in the first 60 d of lactation than in the subsequent period. Bayesian model selection strongly favored the simultaneous and recursive models for milk yield and somatic cell score over the corresponding mixed model without considering simultaneity or recursiveness. Estimated effects between milk yield and somatic cell score seemed to be yield-dependent, larger in higher producing cows than in lower producing cows. Heritability estimates from the simultaneous and recursive models were similar to those from the mixed model, but some genetic correlations differed considerably among models.     

Variances of and correlations among progeny tests for reproductive traits of cows sired by AI bulls

Estimates of daughter fertility were computed using first artificial insemination (AI) breedings reported to the US Dairy Herd Improvement Association (DHIA) from 1995 through 1997. An animal model was used to compute estimated breeding values (EBV) of daughter groups with fixed effects of herd-year-month bred and classes of early lactation energy-corrected milk, days in milk (DIM) when bred, and parity. Standard deviations and ranges of bull EBV for daughter fertility for DIM were 9.1 and -31 to 18; standard deviations and ranges of bull EBV for daughter fertility for nonreturn were 3.8 and -11 to 10. Correlations were computed for EBV for daughter fertility with EBV for mating bull fertility and with predicted transmitting abilities (PTA) for milk, somatic cell score (SCS), and productive life for bulls (213) with minimums of 200 matings and 100 progeny with reproductive traits. None of the correlations among EBV for reproductive traits differed from 0.0. Correlations of EBV for daughter fertility with PTA for productive life were significantly positive. PTA for yield traits were not correlated with EBV for daughter differences in nonreturn or DIM. Very low correlations of EBV for daughter reproductive traits with PTA for yield indicate that, in order to improve daughter fertility, fertility must be incorporated in sire selection decisions.     

A structural equation model for describing relationships between somatic cell score and milk yield in first-lactation dairy cows

Relationships between production and diseases may involve recursive or simultaneous effects between traits. Four structural equation models (SEqM) for somatic cell score and milk yield, with varying specifications for the effects relating the 2 traits, were compared. Data consisted of repeated records of milk yield and somatic cell score of 33,453 first-lactation daughters of 245 Norwegian Red sires that had their first progeny test in 1991 and 1992. All models included random effects of the sire and of the cow and were fitted using the LISREL software. The Bayesian information criterion clearly favored a model with a recursive effect from somatic cell score on milk yield over the 3 other models fitted (absence of recursive effects; an effect from milk yield on somatic cell score; simultaneity of effects between the 2 traits). This provides evidence that the negative association between milk yield and somatic cell score is more likely due to an effect of infection (measured indirectly by the somatic cell score) on production than to a dilution effect. Estimates indicated that a mastitis event would reduce milk yield in the following 15 d by about 900 g/d. The estimated genetic (co)variances did not change sizably when the specification of recursive or simultaneous effects was varied. However, estimates of the phenotypic covariance were altered when a recursive effect from somatic cell score on milk yield was included in the model.     

Genetic parameters of lactation cell counts and milk and protein yields in dairy ewes

A total of 3231 lactation records of somatic cell counts (SCC), milk yield, and protein percentage for 2379 Spanish Churra ewes from 10 flocks were used to estimate genetic and environmental parameters. Genetic parameters were estimated by REML with a multitrait repeatability animal model. A lactation measure of SCC was obtained as the mean of test day log SCC adjusted for stage of lactation. Heritabilities for SCC, milk yield, and protein percentage were 0.12, 0.24, and 0.17, respectively. The corresponding repeatabilities were 0.35, 0.49, and 0.38. Heritability and repeatability estimates of SCC obtained from this study fell within the range frequently reported for dairy cows. Therefore, as practiced for dairy cattle, future possibilities for sire evaluation to improve udder health status using lactation measures of SCC for dairy sheep are not rejected, although hygienic practices are regarded as more important. Genetic correlations of SCC with milk yield and protein percentage were -0.15 and -0.03, respectively. The genetic correlation between milk yield and protein percentage was -0.47. The low genetic correlations of SCC with milk yield and protein percentage may indicate that breeding decisions to improve milk and protein yields of Churra ewes are not expected to have an effective correlated response in SCC.