Estimates of genetic parameters for single- and multiple-country test-day models

Single- and multiple-country random regression models were applied to estimate genetic parameters for first-lactation test-day milk yield of cows from four countries: Australia, Canada, Italy, and New Zealand. Selected countries represented a wide range of production systems and environments. Milk production in Canada and Italy is based mainly on intensive management systems, while Australia and New Zealand are largely based on rotational grazing. Legendre polynomials with five coefficients were used to model genetic and environmental lactation curves. Covariance components of lactation curve coefficients within and across countries, and selected functions of those, were estimated by Bayesian methods with Gibbs sampling, on selected subsets of data. Countries differed in both phenotypic and genetic parameters of lactation curves between d 5 and 305 of lactation. Principal component analysis of single-trait genetic and environmental covariance matrices showed, however, that the pattern of variability in test-day milk yield was very similar between countries. General level of milk production in lactation and persistency components accounted for more than 90% of the total variance. Estimated genetic correlations between countries for total yield in lactation ranged from 0.65 (Italy and New Zealand) to 0.83 (Australia and New Zealand), indicating a possibility of genotype by environment interaction for some pairs of countries.     

Validation of test-day models for genetic evaluation of dairy goats in Norway

Test-day data for daily milk yield and fat, protein, and lactose content were sampled from the years 1988 to 2003 in 17 flocks belonging to 2 genetically well-tied buck circles. In total, records from 2,111 to 2,215 goats for content traits and 2,371 goats for daily milk yield were included in the analysis, averaging 2.6 and 4.8 observations per goat for the 2 groups of traits, respectively. The data were analyzed by using 4 test-day models with different modeling of fixed effects. Model [0] (the reference model) contained a fixed effect of year-season of kidding with regression on Ali-Schaeffer polynomials nested within the year-season classes, and a random effect of flock test-day. In model [1], the lactation curve effect from model [0] was replaced by a fixed effect of days in milk (in 3-d periods), the same for all year-seasons of kidding. Models [2] and [3] were obtained from model [1] by removing the fixed year-season of kidding effect and considering the flock test-day effect as either fixed or random, respectively. The models were compared by using 2 criteria: mean-squared error of prediction and a test of bias affecting the genetic trend. The first criterion indicated a preference for model [3], whereas the second criterion preferred model [1]. Mean-squared error of prediction is based on model fit, whereas the second criterion tests the ability of the model to produce unbiased genetic evaluation (i.e., its capability of separating environmental and genetic time trends). Thus, a fixed structure with year (year, year-season, or possibly flock-year) was indicated to appropriately separate time trends. Heritability estimates for daily milk yield and milk content were 0.26 and 0.24 to 0.27, respectively.     

Genetic line comparisons and genetic parameters for endoparasite infections and test-day milk production traits

Keeping dairy cows in grassland systems relies on detailed analyses of genetic resistance against endoparasite infections, including between- and within-breed genetic evaluations. The objectives of this study were (1) to compare different Black and White dairy cattle selection lines for endoparasite infections and (2) the estimation of genetic (co)variance components for endoparasite and test-day milk production traits within the Black and White cattle population. A total of 2,006 fecal samples were taken during 2 farm visits in summer and autumn 2015 from 1,166 cows kept in 17 small- and medium-scale organic and conventional German grassland farms. Fecal egg counts were determined for gastrointestinal nematodes (FEC-GIN) and flukes (FEC-FLU), and fecal larvae counts for the bovine lungworm Dictyocaulus viviparus (FLC-DV). The lowest values for gastrointestinal nematode infections were identified for genetic lines adopted to pasture-based production systems, especially selection lines from New Zealand. Heritabilities were low for FEC-GIN (0.05–0.06 ± 0.04) and FLC-DV (0.05 ± 0.04), but moderate for FEC-FLU (0.33 ± 0.06). Almost identical heritabilities were estimated for different endoparasite trait transformations (log-transformation, square root). The genetic correlation between FEC-GIN and FLC-DV was 1.00 ± 0.60, slightly negative between FEC-GIN and FEC-FLU (?0.10 ± 0.27), and close to zero between FLC-DV and FEC-FLU (0.03 ± 0.30). Random regression test-day models on a continuous time scale [days in milk (DIM)] were applied to estimate genetic relationships between endoparasite and longitudinal test-day production traits. Genetic correlations were negative between FEC-GIN and milk yield (MY) until DIM 85, and between FEC-FLU and MY until DIM 215. Genetic correlations between FLC-DV and MY were negative throughout lactation, indicating improved disease resistance for high-productivity cows. Genetic relationships between FEC-GIN and FEC-FLU with milk protein content were negative for all DIM. Apart from the very early and very late lactation stage, genetic correlations between FEC-GIN and milk fat content were negative, whereas they were positive for FEC-FLU. Genetic correlations between FEC-GIN and somatic cell score were positive, indicating similar genetic mechanisms for susceptibility to udder and endoparasite infections. The moderate heritabilities for FEC-FLU suggest inclusion of FEC-FLU into overall organic dairy cattle breeding goals to achieve long-term selection response for disease resistance.     

Multiple-trait multiple-country genetic evaluations for udder health traits

International genetic bull evaluations of somatic cell counts (SCC) from 8 different Holstein populations and clinical mastitis from 3 of these populations were inferred simultaneously using a multiple-trait-multiple-country evaluation (MT-MACE) model. This model considered effective independent weighting factors and multivariately deregressed national genetic evaluations for countries with multiple-trait national models. Predictions of genetic merit from MT-MACE and their reliabilities were compared with the corresponding results from 2 separate single-trait-multiple-country evaluations (ST-MACE) for different groups of bulls. The assumed heritabilities for clinical mastitis (h² = 0.02 to 0.05) were substantially lower than the heritabilities for SCC (h² = 0.08 to 0.27). The predictive ability of MT-MACE was essentially equal to or better than the predictive ability of ST-MACE for all country-trait combinations, but both methods yielded effectively unbiased and consistent consecutive predictions (correlation > 0.93). Both sets of predictions also agreed well with future national genetic evaluations for bulls receiving additional daughter information (correlation > 0.96), except for evaluations for which within-country correlations were utilized internationally, but not nationally (correlation = 0.86 to 0.97). The reliabilities for MT-MACE were essentially equal to or higher than reliabilities for ST-MACE, depending on the trait and group of bulls in question. Reliabilities increased most for young bulls, and for clinical mastitis in countries that did not use the within-country correlations with SCC in the national evaluation (up to a 23% increase in average reliability).     

Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows

Several functions were used to model the fixed part of the lactation curve and genetic parameters of milk test-day records to estimate using French Holstein data. Parametric curves (Legendre polynomials, Ali-Schaeffer curve, Wilmink curve), fixed classes curves (5-d classes), and regression splines were tested. The latter were appealing because they adjusted the data well, were relatively insensitive to outliers, were flexible, and resulted in smooth curves without requiring the estimation of a large number of parameters. Genetic parameters were estimated with an Average Information REML algorithm where the average information matrix and the first derivatives of the likelihood functions were pooled over 10 samples. This approach made it possible to handle larger data sets. The residual variance was modeled as a quadratic function of days in milk. Quartic Legendre polynomials were used to estimate (co)variances of random effects. The estimates were within the range of most other studies. The greatest genetic variance was in the middle of the lactation while residual and permanent environmental variances mostly decreased during the lactation. The resulting heritability ranged from 0.15 to 0.40. The genetic correlation between the extreme parts of the lactation was 0.35 but genetic correlations were higher than 0.90 for a large part of the lactation. The use of the pooling approach resulted in smaller standard errors for the genetic parameters when compared to those obtained with a single sample.     

Genetic parameters for a multiple-trait multiple-lactation random regression test-day model in Italian Holsteins

The objectives of this study were to estimate variance components for test-day milk, fat, and protein yields and average daily SCS in 3 subsets of Italian Holsteins using a multiple-trait, multiple-lactation random regression test-day animal model and to determine whether a genetic heterogeneous variance adjustment was necessary. Data were test-day yields of milk, fat, and protein and SCS (on a log₂ scale) from the first 3 lactations of Italian Holsteins collected from 1992 to 2002. The 3 subsets of data included 1) a random sample of Holsteins from all herds in Italy, 2) a random sample of Holsteins from herds using a minimum of 75% foreign sires, and 3) a random sample of Holsteins from herds using a maximum of 25% foreign sires. Estimations of variances and covariances for this model were achieved by Bayesian methods using the Gibbs sampler. Estimated 305-d genetic, permanent environmental, and residual variance was higher in herds using a minimum of 75% foreign sires compared with herds using a maximum of 25% foreign sires. Estimated average daily heritability of milk, fat, and protein yields did not differ among subsets. Heritability of SCS in the first lactation differed slightly among subsets and was estimated to be the highest in herds with a maximum of 25% foreign sire use (0.19 ± 0.01). Genetic correlations across lactations for milk, fat, and protein yields were similar among subsets. Genetic correlations across lactations for SCS were 0.03 to 0.08 higher in herds using a minimum of 75% or a maximum of 25% foreign sires, compared with herds randomly sampled from the entire population. Results indicate that adjustment for heterogeneous variance at the genetic level based on the percentage of foreign sire use should not be necessary with a multiple-trait random regression test-day animal model in Italy.     

Within-herd effects of age at test day and lactation stage on test-day yields

Variance ratios were estimated for random within-herd effects of age at test day and lactation stage, on test-day yield and somatic cell score to determine whether including these effects would improve the accuracy of estimation. Test-day data starting with 1990 calvings for the entire US Jersey population and Holsteins from California, Pennsylvania, Wisconsin, and Texas were analyzed. Test-day yields were adjusted for across-herd effects using solutions from a regional analysis. Estimates of the relative variance (fraction of total variance) due to within-herd age effects were small, indicating that regional adjustments for age were adequate. The relative variances for within-herd lactation stage were large enough to indicate that accuracy of genetic evaluations could be improved by including herd stage effects in the model for milk, fat, and protein, but not for somatic cell score. Because the within-herd lactation stage effect is assumed to be random, the effect is regressed toward the regional effects for small herds, but in large herds, lactation curves become herd specific. Model comparisons demonstrated the greater explanatory power of the model with a within-herd-stage effect as prediction error standard deviations were greater for the model without this effect. The benefit of the within-herd-stage effects was confirmed in a random regression model by comparing variance components from models with and without random within-herd regressions and through log-likelihood ratio tests.     

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.     

An autoregressive repeatability animal model for test-day records in multiple lactations

Test-day (TD) models are becoming a standard for genetic evaluation of production traits in dairy cattle. Various approaches to model covariances between TD records include random regression, autoregressive repeatability, orthogonal polynomials, and models based on character processing. The applicability of these models is mainly associated with the number of parameters to estimate, incorporation of multiple lactations, and the accuracy of correlations generated by the cow's repeated expression of milking performance (TD yields) within and across lactations. We define and evaluate a multiple-lactation, autoregressive-repeatability model that disentangles environmental effects due to cow within and between lactations. Simulated records either included or ignored a long-term environmental effect between lactations. Our autoregressive TD animal model correctly detected presence and the absence of this effect and accurately recovered the assumed variance components and correlations underlying the data (10 parameters for three lactations). Estimates of variance components and autocorrelation coefficients were obtained using DFREML-simplex methodology. Given the value of this approach to reduce the size of residual variance components, autoregressive animal models are a preferable alternative to classical methods based on cumulative lactation yield to improve milk production in dairy cattle.     

Joint international evaluation of Milking Shorthorn dairy cattle for production traits

Pedigree information and test-day records for the first 3 parities of Milking Shorthorn dairy cattle from 5 countries were analyzed. After editing, the data included 1,018,528 test-day records from 68,653 cows. A multiple-lactation random regression test-day model with Legendre polynomials of order 4 and a Bayesian method were used to estimate variance components for both single and multiple-countries. Fixed effects included herd-test-day class and regressions on DIM within age at calving-parity-season of calving. Random effects included animal genetic, permanent environmental, and residual effects. Average daily heritabilities from single country analyses ranged from 0.33 to 0.47 for milk yield and from 0.37 to 0.45 for protein yield across lactations and countries. Common sires (66) and their daughters were identified for creating a connected data set for simultaneous (co)variance component estimation of milk yield across all 5 countries. Between-country genetic correlations were low, with values from 0.08 to 0.46 and standard deviations from 0.08 to 0.12. Estimated breeding values for milk were generated for each animal using the same test-day animal model. Correlations among country estimated breeding values were higher than genetic correlations. Top 100 bull lists were generated on the scale of each country, and genetic progress was assessed. Future evaluation with increased genetic ties among countries may facilitate international comparison of Milking Shorthorns.     

Genetic evaluation of dairy cattle with test-day models with autoregressive covariance structures and with a 305-d model

This study compared genetic evaluations from 3 test-day (TD) models with different assumptions about the environmental covariance structure for TD records and genetic evaluations from 305-d lactation records for dairy cows. Estimates of genetic values of 12,071 first-lactation Holstein cows were obtained with the 3 TD models using 106,472 TD records. The compound symmetry (CS) model was a simple test-day repeatability animal model with compound symmetry covariance structure for TD environmental effects. The AR_s and AR_e models also used TD records but with a first-order autoregressive covariance structure among short-term environmental effects or residuals, respectively. Estimates of genetic values with the TD models were also compared with those from a model using 305-d lactation records. Animals were genetically evaluated for milk, fat, and protein yields, and somatic cell score (SCS). The largest average estimates of accuracy of predicted breeding values were obtained with the AR_s model and the smallest were with the 305-d model. The 305-d model resulted in smaller estimates of correlations between average predicted breeding values of the parents and lactation records of their daughters for milk and protein yields and SCS than did the CS and AR_e models. Predicted breeding values with the 3 TD models were highly correlated (0.98 to 1.00). Predicted breeding values with 305-d lactation records were moderately correlated with those with TD models (0.71 to 0.87 for sires and 0.80 to 0.87 for cows). More genetic improvement can be achieved by using TD models to select for animals for higher milk, fat, and protein yields, and lower SCS than by using models with 305-d lactation records.     

Comparison of random regression test-day models for Polish Black and White cattle

Test-day milk yields of first-lactation Black and White cows were used to select the model for routine genetic evaluation of dairy cattle in Poland. The population of Polish Black and White cows is characterized by small herd size, low level of production, and relatively early peak of lactation. Several random regression models for first-lactation milk yield were initially compared using the “percentage of squared bias” criterion and the correlations between true and predicted breeding values. Models with random herd-test-date effects, fixed age-season and herd-year curves, and random additive genetic and permanent environmental curves (Legendre polynomials of different orders were used for all regressions) were chosen for further studies. Additional comparisons included analyses of the residuals and shapes of variance curves in days in milk. The low production level and early peak of lactation of the breed required the use of Legendre polynomials of order 5 to describe age-season lactation curves. For the other curves, Legendre polynomials of order 3 satisfactorily described daily milk yield variation. Fitting third-order polynomials for the permanent environmental effect made it possible to adequately account for heterogeneous residual variance at different stages of lactation.     

Genetic analysis of milk production traits of polish black and white cattle using large-scale random regression test-day models

Genetic parameters for milk, fat, and protein yield and persistency in the first 3 lactations of Polish Black and White cattle were estimated. A multiple-lactation model was applied with random herd-test-day effect, fixed regressions for herd-year and age-season of calving, and random regressions for the additive genetic and permanent environmental effects. Three data sets with slightly different edits on minimal number of days in milk and the size of herd-year class were used. Each subset included more than 0.5 million test-day records and more than 58,000 cows. Estimates of covariance components and genetic parameters for each trait were obtained by Bayesian methods using the Gibbs sampler. Due to the large size and a good structure of the data, no differences in estimates were found when additional criteria for record selection were applied. More than 95% of the genetic variance for all traits and lactations was explained by the first 2 principal components, which were associated with the mean yield and lactation persistency. Heritabilities of 305-d milk yield in the first 3 lactations (0.18, 0.16, 0.17) were lower than those for fat (0.12, 0.11, 0.12) and protein (0.13, 0.14, 0.15). Estimates of daily heritabilities increased in general with days in milk for all traits and lactations, with no apparent abnormalities at the beginning or end of lactation. Genetic correlations between yields in different lactations ranged from 0.74 (fat yield in lactations 1 and 3) to 0.89 (milk yield in lactations 2 and 3). Persistency of lactation was defined as the linear regression coefficient of the lactation curve. Heritability of persistency increased with lactation number for all traits and genetic correlations between persistency in different lactations were smaller than those for 305d yield. Persistency was not genetically correlated with the total yield in lactation.     

Assessment of the value of international genetic evaluations for yield in predicting domestic breeding values for foreign Holstein bulls

International genetic evaluations are a valuable source of information for decisions about the importation of (the semen of) foreign bulls. This study analyzed data from 6 countries (Australia, Canada, Italy, France, the Netherlands, and the United States) and compared international evaluations for production traits of foreign bulls (i.e., when no national daughter information was available) to their national breeding values in August 2009, which were based only on domestic daughters’ data. A total of 821 bulls with highly reliable estimated breeding values (EBV) for milk, fat, and protein yield were analyzed. No evidence of systematic over- or underestimation was found in most of the countries analyzed. Observed correlations between national and international evaluations were close to 0.9 and, for most countries, generally close to their expected values (calculated from national and international EBV reliabilities). In Italy, however, higher differences between observed and expected correlations and significant mean differences between EBV for more than one trait were observed in bulls progeny-tested in the United States and in other European countries (with differences up to 33.1% of the genetic standard deviation). These results were probably induced by a relatively recent change in the model for national evaluation. The findings in this study reflect a conservative estimate of the real value of international evaluations, as changes in methodologies in either the national or the international evaluations decreased the ability of past international evaluations to predict current national evaluations. Nevertheless, our results indicate that international evaluations based on foreign information for Holstein bulls were reasonably accurate predictors of the future national breeding values based only upon domestic daughters.     

Production loss due to new subclinical mastitis in Dutch dairy cows estimated with a test-day model

Milk, fat, and protein loss due to a new subclinical mastitis case may be economically important, and the objective of this study was to estimate this loss. The loss was estimated based on test-day (TD) cow records collected over a 1-yr period from 400 randomly selected Dutch dairy herds. After exclusion of records from cows with clinical mastitis, the data set comprised 251,647 TD records from 43,462 lactations of 39,512 cows. The analysis was carried out using a random regression test-day modeling approach that predicts the cow production at each TD based on the actual production at all previous TD. The definition of new subclinical mastitis was based on the literature and assumed a new subclinical case if somatic cell count (SCC) was >100,000 cells/mL after a TD with SCC <50,000 cells/mL. A second data set was created by applying an adjustment to correct low SCC for the dilution effect when determining if the previous test-day SCC was <50,000 cells/mL. Thereafter, the loss was estimated for records with SCC >100,000 cells/mL. The production (milk, fat, or protein) losses were modeled as the difference between the actual and predicted production (milk, fat, or protein) at the TD of new subclinical mastitis, for 4,382 cow records, and 2,545 cow records after dilution correction. Primiparous cows were predicted to lose 0.31 (0.25-0.37) and 0.28 (0.20-0.35) kg of milk/d at an SCC of 200,000 cells/mL, for unadjusted and adjusted low SCC, respectively. For the same SCC increase, multiparous cows were predicted to lose 0.58 (0.54-0.62) and 0.50 (0.44-0.56) kg of milk/d, respectively. Moreover, it was found that the greater the SCC increase above 100,000 cells/mL, the greater the production losses. The estimated production losses were more precise than previously reported estimates.     

Application of a multiple-trait, multiple-country genetic evaluation model for female fertility traits

The need to implement a method that can handle multiple traits per country in international genetic evaluations is evident. Today, many countries have implemented multiple-trait national genetic evaluations and they may expect to have their traits simultaneously analyzed in international genetic evaluations. Traits from the same country are residually correlated and the method currently in use, single-trait multiple across-country evaluation (ST-MACE), cannot handle nonzero residual correlations. Therefore, multiple-trait, multiple across-country evaluation (MT-MACE) was proposed to handle several traits from the same country simultaneously. To test the robustness of MT-MACE on real data, female fertility was chosen as a complex trait with low heritability. Data from 7 Holstein populations, 3 with 2 traits and 4 with 1 trait, were used. The differences in the estimated genetic correlations by MT-MACE and the single ST-MACE analysis (average absolute deviation of 0.064) were due to the bias of considering several traits from the same country in the ST-MACE analysis. However, the differences between the estimated genetic correlations by MT-MACE and multiple ST-MACE analyses avoiding more than one trait per country in each analysis (average absolute deviation of 0.066) were due to the lack of analysis of the correlated traits from the same country together and using the reported within-country genetic correlations. Applying MT-MACE resulted in reliability gain in international genetic evaluations, which was different from trait to trait and from bull to bull. The average reliability gain by MT-MACE over ST-MACE was 3.0 points for domestic bulls and 6.3 points for foreign bulls. Even countries with 1 trait benefited from the joint analysis of traits from the 2-trait countries. Another superiority of MT-MACE over ST-MACE is that the bulls that do not have national genetic evaluation for some traits from multiple trait countries will receive international genetic evaluations for those traits. Rank correlations were high between ST-MACE and MT-MACE when considering all bulls. However, the situation was different for the top 100 bulls. Simultaneous analysis of traits from the same country affected bull ranks, especially for top 100 bulls. Multi-trait MACE is a recommendable and robust method for international genetic evaluations and is appropriate for handling multiple traits per country, which can increase the reliability of international genetic evaluations.     

Random regression test-day models with residuals following a Student's-t distribution

First-lactation milk yield test-day records of Canadian Holsteins were analyzed by single-trait random regression test-day models that assumed normal or Student's-t distribution for residuals. Objectives were to test the performance of the robust statistical models that use heavy-tailed distributions for the residual effect. Models fitted were: Gaussian, Student's-t, and Student's-t with fixed number of degrees of freedom (equal to 5, 15, 30, 100 or 1000) for the t distribution. Bayesian methods with Gibbs sampling were used to make inferences about overall model plausibility through Bayes factors, posterior means for covariance components, estimated breeding values for regression coefficients, solutions for permanent environmental regressions, and residuals of the models. Bayes factors favored Student's-t model with the posterior mean of degrees of freedom equal to 2.4 over all other models, indicating very strong departure from normality. Number of outliers in Student's-t model was reduced by 35% in comparison with the Gaussian model. Differences in covariance components for regression coefficients between models were small, and rankings of animals based on additive genetic merit for the first two regression coefficients (total yield and persistency) were similar. Results from the Gaussian and Student's-t models with fixed degrees of freedom become more alike (smaller departures from normality for Student's-t models) with increasing number of degrees of freedom for the t-distributions. For any pair of Student's-t models, the one with the smaller number of degrees of freedom for the t-distribution was shown to be superior. Similarly, number of outliers increased with increasing degrees of freedom for the t distribution.     

Relationship of test-day somatic cell score with test-day and lactation milk yields

To determine the relationship of test-day (TD) somatic cell score (SCS) to TD and lactation milk yields, 1,320,590 records from Holstein first and second calvings from 1995 through 2002 were examined. All lactations had recorded yield and SCS for at least the first 4 TD. Least square analyses were conducted for yields on TD 2 through 10 within herd and cow. The model included regressions on current TD SCS and mean SCS of all previous TD with separate estimates by parity; effects for parity and calving year were included as well as regression on days in milk on TD 1. Corresponding analyses were conducted without regression on current SCS. An analysis of lactation yield was performed with a similar model and regression on all TD SCS. The SCS was highest most often on TD 1 for parity 1 (22.5%) and on TD 10 for parity 2 (18.5%). Regression of TD milk yield on mean of previous TD SCS was highest during the latter half of lactation (maximum of -0.346 kg/SCS unit on TD 9) for parity 1 and during TD through 7 (maximum of -0.366 kg/SCS unit on TD 4) for parity 2. Regression of TD yield on current TD SCS tended to be larger for later lactation. Regression of lactation yield on TD SCS was negative and important for TD 1 through 6 for parity 1 and for all TD for parity 2. To minimize milk loss, mastitis control is most important immediately pre- and postcalving for parity 1 and throughout lactation for parity 2.     

International genetic evaluation for direct longevity in dairy bulls

The aims of this study were to document, present, and discuss the procedure used to calculate the international estimated breeding value (EBV) for longevity for Brown Swiss, Guernsey, Holstein, Jersey, Red Dairy Cattle, and Simmental breeds. Data from 19 countries and 123,833 national sires’ breeding value were used for this purpose. Trait definitions and national genetic evaluation procedures were first summarized; and this showed that differences among countries existed. International breeding values for direct longevity were calculated using a multi-trait across-country evaluation model. The data editing method was identical to the one used for the February 2007 routine international genetic evaluation. Estimated genetic correlations presented in this study were similar to those presented in the literature and, in general, differed from unity because of differences in trait definitions, culling reasons, data included, evaluation procedures, genotype-environment interactions, and weak genetic ties among countries. The average genetic correlations for Holstein ranged from 0.49 to 0.76. The genetic correlations for Brown Swiss and Guernsey ranged from 0.29 to 0.95 and from 0.30 to 0.89, respectively. For Jersey and Red Dairy Cattle the genetic correlations ranged from 0.39 to 0.61 and from 0.30 to 0.96, respectively. For Simmental the genetic correlation was 0.59. Different predictors were used at national levels to define combined longevity. These predictors were combined using economic and empirical weights. Three out of 15 countries published international EBV of direct longevity only and 12 out of 15 countries combined direct longevity with predictors (combined longevity). International breeding values for longevity were combined into the total merit index by most of the member organizations and made available to breeders across the world through magazines and Web sites. Even if some breeders are not familiar with longevity EBV, they will select for this trait automatically if they use the published total merit indexes.     

Genetic evaluation of dairy cattle using test-day models

Recently there has been considerable interest in modeling individual test-day records (TDR) for genetic evaluation of dairy cattle as a replacement for the traditional use of estimated accumulated 305-d yields. Some advantages of test-day models (TDM) include the ability to account for environmental effects of each test day, the ability to model the trajectory of the lactation for individual genotypes or groups of animals, and the possibility of genetic evaluations for persistency of production. Also, the use of test-day models avoids the necessity of extending short lactations on culled animals and animals with records in progress. The disadvantages of TDM include computational difficulties associated with analyzing much larger datasets and the need to estimate many more parameters than in a traditional 305-d lactation model. Several different models have been proposed to model the trajectory of the lactation, including so-called "biological functions," various polynomials and character process models. At present, there is not universal agreement on which models to use in routine prediction of breeding values and better methods to compare models are desirable. Obtaining accurate estimates of the dispersion parameters to use in TDM remains a challenge. Methods used include a two-step procedure in which the dispersion parameters are estimated in a series of multivariate models followed by a reduction in order of fit using covariance functions, and a one-step procedure in which the parameters of TDM are estimated using restricted maximum likelihood or Bayesian methods in a random regression model. Further research should focus on including multiple lactation data and accounting for heterogeneity variance.