Fifteen countries, based on geographical representation, Interbull membership, and size of progeny testing programs, provided a brief description of national selection index and top bull listings from August 2003. Individual traits included in each selection index were grouped into 3 components as they related to production, durability, and health and reproduction. The relative emphasis for each component within the selection index, as well as the number of common bulls among top listings were compared across countries. Average relative emphasis for production, durability, and health and reproduction, across all countries, was 59.5, 28, and 12.5%, respectively. The main difference between selection indices in various countries was the relative emphasis on production. Overall, the Danish S-Index had the most balanced emphasis across the 3 components, with 34% on production, 29% on durability, and 37% on health and reproduction. Broadening of breeding goals through recent changes to selection indices decreased the similarities of top bull listings across the various countries, with a slightly greater commonality among sires of top bulls.

The Canadian Test-Day Model is a 12-trait random regression animal model in which traits are milk, fat, and protein test-day yields, and somatic cell scores on test days within each of first three lactations. Test-day records from later lactations are not used. Random regressions (genetic and permanent environmental) were based on Wilmink's three parameter function that includes an intercept, regression on days in milk, and regression on an exponential function to the power -0.05 times days in milk. The model was applied to over 22 million test-day records of over 1.4 million cows in seven dairy breeds for cows first calving since 1988. A theoretical comparison of test-day model to 305-d complete lactation animal model is given. Each animal in an analysis receives 36 additive genetic solutions (12 traits by three regression coefficients), and these are combined to give one estimated breeding value (EBV) for each of milk, fat, and protein yields, average daily somatic cell score and milk yield persistency (for bulls only). Correlation of yield EBV with previous 305-d lactation model EBV for bulls was 0.97 and for cows was 0.93 (Holsteins). A question is whether EBV for yield traits for each lactation should be combined into one overall EBV, and if so, what method to combine them. Implementation required development of new methods for approximation of reliabilities of EBV, inclusion of cows without test day records in analysis, but which were still alive and had progeny with test-day records, adjustments for heterogeneous herd-test date variances, and international comparisons. Efforts to inform the dairy industry about changes in EBV due to the model and recovering information needed to explain changes in specific animals' EBV are significant challenges. The Canadian dairy industry will require a year or more to become comfortable with the test-day model and to realize the impact it could have on selection decisions.

Genomic evaluations for 161,341 Holsteins were computed by using 311,725 of 777,962 markers on the Illumina BovineHD Genotyping BeadChip (HD). Initial edits with 1,741 HD genotypes from 5 breeds revealed that 636,967 markers were usable but that half were redundant. Holstein genotypes were from 1,510 animals with HD markers, 82,358 animals with 45,187 (50K) markers, 1,797 animals with 8,031 (8K) markers, 20,177 animals with 6,836 (6K) markers, 52,270 animals with 2,683 (3K) markers, and 3,229 nongenotyped dams (0K) with >90% of haplotypes imputable because they had 4 or more genotyped progeny. The Holstein HD genotypes were from 1,142 US, Canadian, British, and Italian sires, 196 other sires, 138 cows in a US Department of Agriculture research herd (Beltsville, MD), and 34 other females. Percentages of correctly imputed genotypes were tested by applying the programs findhap and FImpute to a simulated chromosome for an earlier population that had only 1,112 animals with HD genotypes and none with 8K genotypes. For each chip, 1% of the genotypes were missing and 0.02% were incorrect initially. After imputation of missing markers with findhap, percentages of genotypes correct were 99.9% from HD, 99.0% from 50K, 94.6% from 6K, 90.5% from 3K, and 93.5% from 0K. With FImpute, 99.96% were correct from HD, 99.3% from 50K, 94.7% from 6K, 91.1% from 3K, and 95.1% from 0K genotypes. Accuracy for the 3K and 6K genotypes further improved by approximately 2 percentage points if imputed first to 50K and then to HD instead of imputing all genotypes directly to HD. Evaluations were tested by using imputed actual genotypes and August 2008 phenotypes to predict deregressed evaluations of US bulls proven after August 2008. For 28 traits tested, the estimated genomic reliability averaged 61.1% when using 311,725 markers vs. 60.7% when using 45,187 markers vs. 29.6% from the traditional parent average. Squared correlations with future data were slightly greater for 16 traits and slightly less for 12 with HD than with 50K evaluations. The observed 0.4 percentage point average increase in reliability was less favorable than the 0.9 expected from simulation but was similar to actual gains from other HD studies. The largest HD and 50K marker effects were often located at very similar positions. The single-breed evaluation tested here and previous single-breed or multibreed evaluations have not produced large gains. Increasing the number of HD genotypes used for imputation above 1,074 did not improve the reliability of Holstein genomic evaluations.

The aim of this study was to explore the impact of type traits on the functional survival of Canadian Holstein cows using a Weibull proportional hazards model. The data set consisted of 1,130,616 registered cows from 13,606 herds calving from 1985 to 2003. Functional survival was defined as the number of days from first calving to culling, death, or censoring. Type information consisted of phenotypic type scores for 8 composite traits (with 18 classes of each) and 23 linear descriptive traits (with 9 classes of each). 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 within herd-year-parity deviations, herd-year-season of calving, each type trait, and the sire. Analysis was done one at a time for each of 31 type traits. The relative culling risk was calculated for animals in each class after accounting for the previously mentioned effects. Among the composite type traits with the greatest contribution to the likelihood function were final score, mammary system, and feet and legs, all having a strong relationship with functional survival. Cows with low scores for these traits had higher risk of culling compared with higher scores. For instance, cows classified as poor plus 1 vs. excellent plus 1 have a relative risk of culling 3.66 and 0.28, respectively. The corresponding figures for mammary system are 4.19 and 0.46 and for feet and legs are 2.34 and 0.50. Linear type traits with the greatest contribution to the likelihood function were fore udder attachment, udder texture, udder depth, rear udder attachment height, and rear udder attachment width. Stature and size had no strong relationship with functional survival.

The objectives of this study were to identify the most important factors that influence functional survival and to estimate the genetic parameters of functional survival for Canadian dairy cattle. Data were obtained from lactation records extracted for the May 2002 genetic evaluation of Holstein, Jersey, and Ayrshire breeds that calved between July 1, 1985 and April 5, 2002. Analysis was performed using a Weibull proportional hazard model, and the baseline hazard function was defined on a lactation basis instead of the traditional analysis of the whole length of life. 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 within herd-year-parity deviations; and the random effects of herd-year-season of calving and sire. All effects fitted in the model had a significant effect on functional survival of cows in all breeds. Milk yield was by far the most important factor influencing survival, and the hazard increased as the milk production of the cows decreased. The hazard also increased as the fat content increased compared with the average group. Heifers that were older at calving were at higher risk of being culled, and expanding herds were at lower risk of being culled compared with stable herds. More culling was found in unsupervised herds than in supervised herds. The heritability values obtained were 0.14, 0.10, and 0.09 for Holstein, Jersey, and Ayrshire, respectively. Rank correlation between estimated breeding values (EBV) obtained from the current national genetic evaluation of direct herd life and the survival kit used in this study ranged from 0.65 to 0.87, depending on the number of daughters per sire. Estimated genetic trend obtained using the survival kit was overestimated.

The aim of this study was to use survival analysis to assess the relationship between reproduction traits and functional longevity of Canadian dairy cattle. Data consisted of 1,702,857; 67,470; and 33,190 Holstein, Ayrshire, and Jersey cows, respectively. Functional longevity was defined as the number of days from first calving to culling, death, or censoring; adjusted for the effect of milk yield. The reproduction traits included calving traits (calving ease, calf size, and calf survival) and female fertility traits (number of services, days from calving to first service, days from first service to conception, and days open). The statistical model was a Weibull proportional hazards model and included the fixed effects of stage of lactation, season of production, the annual change in herd size, and type of milk recording supervision, age at first calving, effects of milk, fat, and protein yields calculated as within herd-year-parity deviations for each reproduction trait. Herd-year-season of calving and sire were included as random effects. Analysis was performed separately for each reproductive trait. Significant associations between reproduction traits and longevity were observed in all breeds. Increased risk of culling was observed for cows that required hard pull, calved small calves, or dead calves. Moreover, cows that require more services per conception, a longer interval between first service to conception, an interval between calving to first service greater than 90 d, and increased days open were at greater risk of being culled.

The aim of this study was to use a Weibull proportional hazards model to explore the impact of type traits on the functional survival of Canadian Jersey and Ayrshire cows. The data set consisted of 49,791 registered Jersey cows from 900 herds calving from 1985 to 2003. The corresponding figures for Ayrshire were 77,109 cows and 921 herds. Functional survival was defined as the number of days from first calving to culling, death, or censoring. Type information consisted of phenotypic type scores for 8 composite traits and 19 linear descriptive traits. 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; each type trait; and the animal's sire. Analysis was done one trait at a time for each of 27 type traits in each breed. The relative culling risk was calculated for animals in each class after accounting for the previously mentioned effects. Among the composite type traits with the greatest contribution to the likelihood function was final score followed by mammary system for Jersey breed, while in Ayrshire breed feet and legs was the second most important trait next to final score. Cows classified as Poor for final score in both breeds were >5 times more likely to be culled compared with the cows classified as Good Plus. In both breeds, cows classified as Poor for feet and legs were 5 times more likely to be culled than were cows classified as Excellent, and cows classified as Excellent for mammary system were >9 times more likely to survive than were cows classified as Poor.

The aim of this study was to assess the level of inbreeding and its relationship to the functional survival of Canadian dairy breeds by using a Weibull proportional hazard model. Data consisted of records from 72,385 cows in 1,505 herds from 2,499 sires for Jerseys, 112,723 cows in 1,482 herds from 2,926 sires for Ayrshires, and 1,977,311 cows in 17,182 herds from 8,261 sires for Holsteins. Longevity was defined as the number of days from first calving to culling, death, or censoring. Inbreeding coefficients (F) were grouped into 7 classes (F = 0, 0 < F < 3.125, 3.125 < or = F < 6.25, 6.25 < or = F <12.5, 12.5 < or = F < 18.25, 18.25 < or = F < 25.0, and F > or = 25.0%). 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, inbreeding, and sire. The relative culling rate was calculated for animals in each class after accounting for the above-mentioned effects. A trend toward increased risk of culling among more inbred animals was observed for all breeds. Little difference in survival was observed for cows with 0 < F <12.5%. The relative risk ratios (relative to F = 0) for cows with inbreeding coefficients up to 12.5% were 1.19, 1.16, and 1.14 for Jersey, Ayrshire, and Holstein cows, respectively. Greater effects of inbreeding were seen, however, when F increased beyond 12.5%.