Short communication: Genetic parameters of production traits in Chinese Holsteins using a random regression test-day model

The objective of this study was to estimate genetic parameters of production traits in the first 3 parities in Chinese Holsteins. Data were a random sample of complete herds (109,005 test-day records of 9,706 cows from 54 herds) extracted from the original data set, which included 362,304 test-day records of 30,942 Holstein cows from 105 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. The multiple-trait analysis included milk, fat, and protein yield, and somatic cell score (SCS). Average daily heritabilities ranged between 0.222 and 0.346 for the yield traits and between 0.092 and 0.187 for SCS. Heritabilities were higher in the third lactation for all traits. Within-parity genetic correlations were very high among the yield traits (>0.806) and were close to zero between SCS and yield traits, especially for first-parity cows. Results were similar to previous literature estimates from studies that used the same model as applied to this study. The estimates found in this study will be used to perform breeding value estimation for national genetic evaluations in Chinese Holsteins.     

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

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

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.     

Effects of Sire of Fetus upon Subsequent Production and Days Open of the Dam

To quantify effects of the fetal-placental unit upon postpartum performance of the dairy cow, Dairy Herd Improvement Association records from Florida, Georgia, North Carolina, and South Carolina (27,200 lactations of 11,935 Holsteins and 3,731 of 1788 Jerseys) were studied by method of least squares. The final model included herd-year-season, sire of fetus, and sire of cow. Sire of fetus accounted for 8.2 and 11.8% of the total variability in mature equivalent milk yield (Holsteins and Jerseys), 9.8 and 14.3% in fat yield, and 1.9 to 3.2% in days open. Interactions of sire of fetus with sire of cow accounted for none of the variability in yields (negative estimates of variance) but 4.4 and 4.8% of the variability in days open. Correlations between effects of sire of fetus on milk and fat yields were .96; for yields and days open they were .02 and ?.02 for Jerseys and ?.12 and ?.13 for Holsteins. Correlation between sire of fetus constants and estimated breeding values for milk yield in a sample of 50 Jersey sires was ?.04. Future consideration of sire of fetus effects could lead to increased repeatability of sire proofs and rate of genetic change. Hypothesized mechanism is that effects of the fetal-placental unit, possibly hormonal and at least partly genetically determined, influence development of the dam's mammary gland and its subsequent performance.     

Estimating genetic values for milk production in the tropics

Variance components and predicted sire values were estimated using 305-d projected and unprojected milk records of varying lengths. Original data consisted of 15,512 lactation records of daughters of 138 Jamaica Hope sires that calved between 1969 and 1981 in 38 commercial dairy herds in Jamaica. Classification of records had little effect on components of variance. Herd-year-season variance decreased from 36% using all lactations to 28% with first lactations only. Sire variance was consistently about 10%. Cow component of variance accounted for 17% of the total variation using all lactations and 36% using all lactations of cows with recorded first lactations. Heritabilities for milk by Henderson's method 1 were five to six times larger than estimated from method 3 due to sire by herd confounding. Predicted sire values were between +400 kg and -400 kg. Rankings of sires with at least 5 progeny were considerably influenced by record classification, especially for sires with highest predicted values. There was less influence on rankings when at least 10 progeny per sire were used while the range in predicted sire values was larger using first lactation records only.     

Heritability of lactation cell count measures and their relationships with milk yield and composition in Ayrshire cows

Lactation measures of somatic cell count were calculated from monthly test-day observations (transformed to a log scale) taken between February 1977 and February 1981 in Ayrshire cows in 115 herds enrolled in the Quebec Dairy Herd Analysis Service. Analyses were separate within three groups: 1137 first lactations, representing 37 sires; 1728 second and later lactations, representing 57 sires; and 2510 all lactations, representing 74 sires. Heritabilities of lactation measures were estimated from sire and error variances obtained by iterative minimum norm quadratic unbiased estimation. Heritabilities ranged from .09 to .16 in first lactations and averaged .09 for the group of second and later lactations and .07 for all lactations. Genetic correlations of lactation measures of cell count with milk, fat, protein yield, fat percent, and protein percent averaged .36, .68, .74, .38, and .45, in first lactations; -.97, -.27, -.56, .52, and .03 in second and later lactations; and -.50, -.54, -.73, .43, and .19 in all lactations. Respective average phenotypic correlations were low and negative for milk, fat, protein yield, and fat percent and low and positive for protein percent.     

Estimated heterogeneity of phenotypic variance of test-day yield with a structural variance model

First-lactation test-day milk, fat, and protein yields from New York, Wisconsin, and California herds from 1990 through 2000 were adjusted additively for age and lactation stage. A random regression model with third-order Legendre polynomials for permanent environmental and genetic effects was used. The model included a random effect with the same polynomial regressions for 2 yr of calvings within herd (herd-time effect) to provide herd-specific lactation curves that can change every 2 yr. (Co)variance components were estimated using expectation-maximization REML simultaneously with phenotypic variances that were modeled using a structural variance model. Maximum heritability for test-day milk yield was estimated to be approximately 20% around 200 to 250 d in milk; heritabilities were slightly lower for test-day fat and protein yields. Herd-time effects explained 12 to 20% of phenotypic variance and had the greatest impact at start of lactation. Variances of test-day yields increased with time, subclass size, and milking frequency. Test month had limited influence on variance. Variance increased for cows in herds with low and high milk yields and for early and late lactation stages. Repeatabilities of variances observed for a given class of herd, test-day, and milking frequency were 14 to 17% across nested variance subclasses based on lactation stage.     

Derivation and calculation of approximate reliabilities and daughter yield-deviations of a random regression test-day model for genetic evaluation of dairy cattle

Test-day milk, fat, protein yield, and somatic cell score (SCS) were analyzed separately using data from the first 3 lactations and a random regression model. Data used in the model were from Austria, Germany, and Luxembourg and from Holstein, Red, and Jersey dairy cattle. For reliability approximation, a multiple-trait effective daughter contribution (MTEDC) method was developed under general multiple trait models, including random regression test-day models, by extending the single-trait daughter equivalents concept. The MTEDC was applied to the very large dairy population, with about 15.5 million animals. The calculation of reliabilities required less computer memory than the corresponding iteration program and a significantly lower computing time equivalent to 24 rounds of iteration. A formula for daughter-yield deviations was derived for bulls under multiple-trait models. Reliability associated with daughter-yield deviations was approximated using the MTEDC method. Both the daughter-yield deviation formula and associated reliability method were verified in a simulation study using the random regression test-day model. Correlations of lactation daughter-yield deviations with estimated breeding values calculated from a routine genetic evaluation were 0.996 for all bulls and 0.95 for young bulls having only daughters with short lactations.     

Genetic analysis of Holstein cattle populations in Brazil and the United States

Genetic relationships between Brazilian and US Holstein cattle populations were studied using first-lactation records of 305-d mature equivalent (ME) yields of milk and fat of daughters of 705 sires in Brazil and 701 sires in the United States, 358 of which had progeny in both countries. Components of(co)variance and genetic parameters were estimated from all data and from within herd-year standard deviation for milk (HYSD) data files using bivariate and multivariate sire models and DFREML procedures distinguishing the two countries. Sire (residual) variances from all data for milk yield were 51 to 59% (58 to 101%) as large in Brazil as those obtained from half-sisters in the average US herd. Corresponding proportions of the US variance in fat yield that were found in Brazil were 30 to 41% for the sire component of variance and 48 to 80% for the residual. Heritabilities for milk and fat yields from multivariate analysis of all the data were 0.25 and 0.22 in Brazil, and 0.34 and 0.35 in the United States. Genetic correlations between milk and fat were 0.79 in Brazil and 0.62 in the United States. Genetic correlations between countries were 0.85 for milk, 0.88 for fat, 0.55 for milk in Brazil and fat in the US, and 0.67 for fat in Brazil and milk in the United States. Correlated responses in Brazil from sire selection based on the US information increased with average HYSD in Brazil. Largest daughter yield response was predicted from information from half-sisters in low HYSD US herds (0.75 kg/kg for milk; 0.63 kg/kg for fat), which was 14% to 17% greater than estimates from all US herds because the scaling effects were less severe from heterogeneous variances. Unequal daughter response from unequal genetic (co)variances under restrictive Brazilian conditions is evidence for the interaction of genotype and environment. The smaller and variable yield expectations of daughters of US sires in Brazilian environments suggest the need for specific genetic improvement strategies in Brazilian Holstein herds. A US data file restricting daughter information to low HYSD US environments would be a wise choice for across-country evaluation. Procedures to incorporate such foreign evaluations should be explored to improve the accuracy of genetic evaluations for the Brazilian Holstein population.     

Estimating Effects of Permanent Environment, Lactation Stage, Age, and Pregnancy on Test-Day Yield

Test-day variances for permanent environmental effects within and across parities were estimated along with lactation stage, age, and pregnancy effects for use with a test-day model. Data were test-day records for calvings since 1990 for Jerseys and for Holsteins from California, Pennsylvania, Texas, and Wisconsin. Single-trait repeatability models were fitted for milk, fat, and protein test-day yields. Method R and a preconditioned conjugate gradient equation solver were used for variance component estimation because of large data sets. Test-day yields were adjusted for environmental effects of calving age, calving season, and milking frequency and for estimated breeding value (EBV) expressed on a daily basis. To assess the effect of adjustments, test-day yields also were analyzed without adjustment. For adjusted data, permanent environmental variances across parities relative to phenotypic variance ranged from 8.3 to 15.2% for milk, 4.4 to 8.3% for fat, and 6.9 to 11.0% for protein across regions and breeds; relative permanent environmental variances within parity ranged from 31.4 to 34.7% for milk, 18.2 to 22.3% for fat, and 28.3 to 29.1% for protein and were similar across regions and breeds. Adjustment for EBV reduced permanent environmental variance across parities and removed cow genetic variance. Relative permanent environmental variances within parity from unadjusted test-day yields were nearly identical to those from adjusted test-day yields. For unadjusted test-day yields, heritabilities ranged from 0.19 to 0.30 for milk, 0.13 to 0.15 for fat, and 0.17 to 0.23 for protein. Adjustments for lactation stage, age at milking, previous days open, and days pregnant were estimated from adjusted test-day yields using the same single-trait repeatability models and variance ratios estimated for permanent environment within and across parities. Those adjustments can be applied additively to test-day yields before evaluation analysis. Variance components and solutions for the various effects can be used to calculate test-day deviations in an analysis within herd that contributes to an analysis across herds.     

Bivariate analysis of conception rates and test-day milk yields in Holsteins using a threshold-linear model with random regressions

The objective of this study was to estimate genetic correlations between conception rates (CR) and test-day (TD) milk yields in Holsteins for different days in milk (DIM) in small and large herds. The data included 217,213 first-parity service records of 94,984 cows in New York State between 1999 and 2003. The CR was defined as the outcome of a single insemination. Conception rate and TD milk were analyzed using a series of threshold-linear models with fixed effects that included herd-test-date for TD milk and herd-year for CR, age, service month, cubic regressions on DIM using Legendre polynomials and with random effects that included herd × sire interaction, sire additive genetic and permanent environments with quadratic random regressions on DIM, service sire for CR, and residual. Variance components were estimated using a Bayesian method via Gibbs sampling. Herds were categorized into small (≤80 cows) and large operations. Large herds produced a higher TD milk (34.0 vs. 30.8 kg), had lower CR (29.5 vs. 34.4%), began breeding earlier (75 vs. 92 d to first service), and had fewer days open (138 vs. 145 d). The average CR was 20% at 50 DIM, increased to about 38% at DIM 100, and then leveled off. Estimated genetic correlations between CR and TD milk stayed around −0.15 for small herds but changed from positive (0.3) at 60 DIM to negative (−0.3) at 120 DIM for large herds. Genetic correlations for CR between small and large herds were highest at 80 DIM and lowest at 140 DIM. The chi-square test showed that the frequency of service records was significantly different during a given week for 71% of large herds and for 15% of small herds, suggesting more timed artificial insemination services in large herds. For the top 15% of cows for milk, fertility peaked around 100 DIM in large herds and at around 100 and 170 DIM in small herds. It seems that optimum breeding practices in large herds of breeding cows earlier are already followed.     

Genotype x environment interaction for yield and somatic cell score with automatic and conventional milking systems

The objective of this study was to quantify genotype by environment interaction (G x E) between automatic milking systems (AMS) and conventional milking systems (CMS) for test-day milk, fat, and protein yield and for test-day somatic cell score (SCS) in The Netherlands. The G x E was studied in 2 ways: 1) between AMS farms and CMS farms in the same period and 2) within farms comparing the period before introduction of AMS with the period after introduction of AMS. For both sub-objectives, a separate data set was generated. Test-day records were used to be more flexible with respect to the introduction date of AMS. Multivariate, fixed regression, test-day sire models were used to estimate variance components. Genetic correlations between AMS farms and CMS farms in the same period were 0.93, >0.99, 0.98, and 0.79 for test-day milk yield, fat yield, protein yield, and SCS, respectively. Genetic correlations within farms between the period before and after introduction of AMS were lower for production traits and higher for SCS: 0.89, 0.91, 0.87, and >0.99, respectively, for test-day milk yield, fat yield, protein yield, and SCS. Heterogeneity of variance was observed between AMS and CMS in both data sets. Especially the residual variance increased with automatic milking. As a consequence, the heritability tended to be lower for automatic milking. It was concluded that effects of G x E are small between AMS and CMS. Therefore, AMS farms can select sires accurately based on national rankings.     

Genetic Trends Among Artificially Bred Holsteins in Quebec

Mixed-model methods were used to evaluate 52 Holstein sires in artificial insemination for milk, fat, and protein yield and fat and protein percent. A total of 3288 305-day first lactations of Holstein on the Dairy Herd Analysis Service were studied. Sires were grouped by year of first service, and groups were used to measure trends in the average generic merit of sires sampled. Annual genetic trends among sires were 85, 1.4, and 1.0 kg for milk, fat, and protein yield and kg for milk, fat, and protein yield and ?.029 and ?.031% for fat and protein test. Genetic trends among their daughters were estimated for Quebec herds according to relative sire usage. Trends in yields were positive. Average yearly genetic gains for milk, fat, and protein yield were 46, 1.1 and .5 kg for all herds and 57, 1.9, and 1.1 kg for herds in Analysis Service. Trends for milk composition were negative. Annual genetic declines in fat and protein percent were ?.004 and ?.008% for the population and ?.003 and ?.014% for herds in Analysis Service.     

Impact of area and sire by herd interaction on heritability estimates for somatic cell count in Italian Holstein Friesian cows.

The aim of the paper was to estimate variance components for somatic cell scores for Italian Holsteins using data from three different areas of the country. A total of 2,202,804 first-parity test-day records, collected from 1990 to 1997 in three areas of Italy (Mantova, Milano, and Parmigiano cheese area), were available for study. The areas differ in herd size, feeding systems and especially in milk use. A minimum standard of quality is also required by some specific methods of cheese production, as for example from the Parmigiano Reggiano cheese chain. These reasons, taken together, affect the attention given to the quality of milk production in herds, and, therefore, to the sanitation levels. A pedigree file was extracted from the national database of Holstein Friesian breed. For computational reasons, eight samples of the data were extracted per area. Variance components were estimated by sample using two different test-day repeatability models. The first model included fixed effects of herd-test date, days in milk (30-d intervals) and calving month, and random effects of permanent environment, additive genetic and residual error. Estimated heritabilities in the first model ranged from 0.06 to 0.09 and repeatabilities from 0.36 to 0.45. Only small differences were detected among areas. In the second model, a random sire x herd interaction effect was added. Including the sire x herd effect resulted in heritability estimates ranging between 0.05 and 0.08 and repeatabilities from 0.35 to 0.45. The analysis revealed that only a small fraction of the total variance (0.35 to 1.5%) could be explained by sire x herd interaction effect. Based on this research, it appears that parameter estimates for somatic cell count do not differ by region, and inclusion of a sire x herd interaction effect is unnecessary.     

Genetic parameters for tunisian holsteins using a test-day random regression model

Genetic parameters of milk, fat, and protein yields were estimated in the first 3 lactations for registered Tunisian Holsteins. Data included 140,187; 97,404; and 62,221 test-day production records collected on 22,538; 15,257; and 9,722 first-, second-, and third-parity cows, respectively. Records were of cows calving from 1992 to 2004 in 96 herds. (Co)variance components were estimated by Bayesian methods and a 3-trait-3-lactation random regression model. Gibbs sampling was used to obtain posterior distributions. The model included herd × test date, age × season of calving × stage of lactation [classes of 25 days in milk (DIM)], production sector × stage of lactation (classes of 5 DIM) as fixed effects, and random regression coefficients for additive genetic, permanent environmental, and herd-year of calving effects, which were defined as modified constant, linear, and quadratic Legendre coefficients. Heritability estimates for 305-d milk, fat and protein yields were moderate (0.12 to 0.18) and in the same range of parameters estimated in management systems with low to medium production levels. Heritabilities of test-day milk and protein yields for selected DIM were higher in the middle than at the beginning or the end of lactation. Inversely, heritabilities of fat yield were high at the peripheries of lactation. Genetic correlations among 305-d yield traits ranged from 0.50 to 0.86. The largest genetic correlation was observed between the first and second lactation, potentially due to the limited expression of genetic potential of superior cows in later lactations. Results suggested a lack of adaptation under the local management and climatic conditions. Results should be useful to implement a BLUP evaluation for the Tunisian cow population; however, results also indicated that further research focused on data quality might be needed.     

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.     

Random regression test-day model for clinical mastitis: Genetic parameters,model comparison,and correlations with indicator traits

The objective was to study genetic (co)variance components for binary clinical mastitis (CM), test-day protein yield, and udder health indicator traits [test-day somatic cell score (SCS) and type traits of the udder composite] in the course of lactation with random regression models (RRM). The study used a data set from selected 15 large-scale contract herds including 26,651 Holstein cows. Test-day production and CM data were recorded from 2007 to 2012 and comprised parities 1 to 3. A longitudinal CM data structure was generated by assigning CM records to adjacent official test dates. Bivariate threshold-linear RRM were applied to estimate genetic (co)variance components between longitudinal binary CM (0 = healthy; 1 = diseased) and longitudinal Gaussian distributed protein yield and SCS test-day data. Heritabilities for liability to CM (heritability ~0.15 from 0 to 305 d after calving) were slightly higher than for SCS for corresponding days in milk (DIM) in the course of lactation. Daily genetic correlations between CM and SCS were moderate to high (genetic correlation ~0.70), but substantially decreased at the very end of lactation. Genetic correlations between CM at different test days were close to 1 for adjacent test days, but were close to zero for test days far apart. Daily genetic correlations between CM and protein yield were low to moderate. For identical DIM (e.g., DIM 20, 160, and 300), genetic correlations were −0.03, 0.11, and 0.18, respectively, and disproved pronounced genetic antagonisms between udder health and productivity. Correlations between estimated breeding values (EBV) for CM from the RRM and official EBV for linear type traits of the udder composite, including EBV from 74 influential sires (sires with >60 daughters), were −0.31 for front teat placement, −0.01 for rear teat placement, −0.31 for fore udder attachment, −0.32 for udder depth, and −0.08 for teat length. Estimated breeding values for CM from the RRM were compared with EBV from a multiple-trait model and with EBV from a repeatability model. For test days covering an identical time span and on a lactation level, correlations between EBV from RRM, multiple-trait model, and repeatability model were close to 1. Most relevant results suggest the routine application of threshold RRM to binary CM to (1) allow selection of genetically superior sires for distinct stages of lactation and (2) achieve higher selection response in CM compared with selection strategies based on indicator type traits or based on the indicator-trait SCS.     

Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows

A random regression model with both random and fixed regressions fitted by Legendre polynomials of order 4 was compared with 3 alternative models fitting linear splines with 4, 5, or 6 knots. The effects common for all models were a herd-test-date effect, fixed regressions on days in milk (DIM) nested within region-age-season of calving class, and random regressions for additive genetic and permanent environmental effects. Data were test-day milk, fat and protein yields, and SCS recorded from 5 to 365 DIM during the first 3 lactations of Canadian Holstein cows. A random sample of 50 herds consisting of 96,756 test-day records was generated to estimate variance components within a Bayesian framework via Gibbs sampling. Two sets of genetic evaluations were subsequently carried out to investigate performance of the 4 models. Models were compared by graphical inspection of variance functions, goodness of fit, error of prediction of breeding values, and stability of estimated breeding values. Models with splines gave lower estimates of variances at extremes of lactations than the model with Legendre polynomials. Differences among models in goodness of fit measured by percentages of squared bias, correlations between predicted and observed records, and residual variances were small. The deviance information criterion favored the spline model with 6 knots. Smaller error of prediction and higher stability of estimated breeding values were achieved by using spline models with 5 and 6 knots compared with the model with Legendre polynomials. In general, the spline model with 6 knots had the best overall performance based upon the considered model comparison criteria.     

Analysis of milk urea nitrogen and lactose and their effect on longevity in Canadian dairy cattle

The aim of this study was to assess the phenotypic level of lactose and milk urea nitrogen concentration (MUN) and the association of these traits with functional survival of Canadian dairy cattle using a Weibull proportional hazards model. A total of 1,568,952 test-day records from 283,958 multiparous Holstein cows from 4,758 herds, and 79,036 test-day records from 26,784 multiparous Ayrshire cows from 384 herds, calving from 2001 to 2004, were used for the phenotypic analysis. The overall average lactose percentage and MUN for Ayrshires were 4.49% and 12.20 mg/dL, respectively. The corresponding figures for Holsteins were 4.58% and 11.11 mg/dL. Concentration of MUN increased with parity number, whereas lactose percentage decreased in later parities. Data for survival analysis consisted of 39,536 first-lactation cows from 1,619 herds from 2,755 sires for Holsteins and 2,093 cows in 228 herds from 157 sires for Ayrshires. Test-day lactose percentage and MUN were averaged within first lactation. Average lactose percentage and MUN were grouped into 5 classes (low, medium-low, medium, medium-high, and high) based on mean and standard deviation values. The statistical model included the effects of stage of lactation, season of production, the annual change in herd size, type of milk-recording supervision, age at first calving, effects of milk, fat, and protein yields calculated as within herd-year-parity deviations, herd-year-season of calving, lactose percentage and MUN classes, and sire. The relative culling rate was calculated for animals in each class after accounting for the remaining effects included in the model. Results showed that there was a statistically significant association between lactose percentage and MUN in first lactation with functional survival in both breeds. Ayrshire cows with high and low concentration of MUN tended to be culled at a higher than average rate. Instead, Holstein cows had a linear association, with decreasing relative risk of culling with increasing levels of MUN concentration. The relationship between lactose percentage and survival was similar across breeds, with higher risk of culling at low level of lactose, and lower risk of culling at high level of lactose percentage.