No description available.

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No description available.

Data for this study were 79,997 evaluations for 12 individual body and udder traits of Holstein cows. Type scores were subjectively assigned by trained personnel of Select Sires, Inc., Plain City, OH, through their corrective mating and young sire evaluation programs. After editing data to remove incomplete, erroneous and duplicate records, 56,642 records were used to calculate overall and regional age adjustment factors. Genetic parameters were estimated. Heritabilities ranged from .40 for stature to .14 for fore udder. Standard errors ranged from .039 for stature to .018 for fore udder. Genetic correlations ranged from +.75 between teats and udder support to -.61 between feet and legs. Phenotypic correlations were generally smaller than genetic correlations, ranging from .56 between udder support and teats to -.23 for dairyness and strength. These findings were in general agreement with previous research. Genetic evaluations for the 12 individual linear traits were made using Herdmate Comparison (HC) and Best Linear Unbiased Prediction (BLUP) methods. Mean evaluations by HC were near zero, and ranged from .026 for stature to -.027 for teats. Mean repeatability also showed very little variation, ranging from .404 for stature to .241 for teats. BLUP evaluations were similar to HC evaluations, with mean evaluation ranging from .121 for feet to -.183 for dairyness. Direct correlations between BLUP and HC evaluations ranged from .90 for stature to .74 for dairyness. Forty-eight different combinations of minimum daughter numbers and model variables were tested to predict sires' overall evaluations for type from linear trait evaluations using regression analysis. Results indicate that with 5 or more daughters per sire, all predictions tested were similar in accuracy for Predicted Difference for Type. / M.S.

The influences of some environmental and genetic factors on the incidence of abnormal test-days in milk-recorded cows enrolled in the Quebec Dairy Herd Analysis Service (QDHAS) was determined in this study. Conditions Affecting Records (CAR) codes, collected monthly by QDHAS's supervisors, are possible explanation for a reduced production in the day of test. For the purpose of this study, CAR codes were used to analyze the incidence of health problems under generalized linear models methodology. Poisson and logistic regression model analyses were able to model the number of cases of abnormal test-days and health problems per lactation. Herd, testing program, parity number, and stage of lactation were important systematic effects included in the analysis. However, calving year, season of calving, and herd production level were not statistically significant in most analysis. Sires significantly differed in the incidence of some health problems of their daughters. Low heritability values, between 0.02 and 0.05, were found showing that most variability was explained by non-genetic factors. Regardless of the low heritability, the genetic variability has been shown to be considerable, suggesting that a significant genetic improvement of the disease resistance is achievable if proper procedures are adopted.

This study, being part of a larger project - "Canadian/New Zealand GxE Interaction Trial" - is comparing Canadian and New Zealand sired heifers for growth traits within Canada, since differences for growth traits were found in the Polish strain comparison (Jasiorowski et al., 1987) and due to selection programs in the two countries. Twenty Canadian Holstein and twenty New Zealand Friesian progeny tested, A.I. bulls were randomly mated to over 1,000 cows in 10 Canadian herds. 3,539 records of weight and wither height from 475 heifers (i.e. 241 Canadian and 234 New Zealand sired) were generated. Subsets of the data for different stages of heifer maturity were analyzed separately. Herd and strain effects least squares means were estimated using analysis of variance. Genetic and phenotypic and correlations and heritability for weight and wither height were estimated by a Derivative-Free Restricted Maximum Likelihood (DFREML) algorithm and an animal model (AM). No differences were found between sire strains for weight except at 15 and 18 months where sib groups of Canadian (CN) sires were heavier than their New Zealand (NZ) contemporaries (393 vs 386 kg and 447 vs 445 kg, respectively). CN sired heifers were taller at all ages except at birth, 3 and 9 months of age. At 24 months CN heifers were 136 cm while NZ heifers were 133 cm. Heritability estimates for weight at birth was 0.62 for the CN strain and 0.59 for the NZ strain. CN estimates (3 to 6 months) and NZ estimates (3 to 9 months) were close to zero. Between 9 to 24 months CN strain estimates ranged from 0.44 to 0.69 while NZ estimates were 0.17 to 0.51. The joint estimates ranged from 0.10 to 0.66. Heritability estimates for wither height for CN strain at birth and between 9 to 21 months were between 0.34 to 0.66 and close to zero between 3 to 6 and at 24 months. The NZ estimates at birth, 18, 21 and 24 months were between 0.36 to 0.93 but close to zero between 3 to 15 months. The joint estimates ranged from 0.32 to 0.75 between 12 to 24 months. Genetic correlations between weight and wither height ranged from 0.62 to 1.0 for CN strain and from -0.04 to 0.91 for NZ strain between 4.5 to 21 months. At six months of age the genetic correlation for CN strain was -0.01 and NZ strain was 0.54. At birth, both sire groups had a genetic correlation of 1.0. At 24 months NZ strain had a genetic correlation of 0.84 while that of the CN strain was 0. Genetic correlations for the joint analysis ranged from 0.61 to 1.0 for all ages except at 6 months (0.18). Phenotypic correlations between weight and wither height were between 0.33 to 0.60 for CN group and 0.33 to 0.62 for NZ group. The joint estimates were 0.36 to 0.61. There were no differences in the phenotypic variances except at 9, 12 and 15 months. Genetic variances were different at all ages except at birth for weight. / Land and Food Systems, Faculty of / Graduate

No description available.

Reproductive data from 30 research herds were on 31,132 breeding periods of 11,347 dairy cows. Cows were sired by 1,101 sires and had 66,184 services to 1,320 service sires. Several measures of reproductive pe.rformance were calculated. These included conception rate, number of services, service period length, days open, age at first breeding, calving interval, days between services, and return to estrus lag. First, second and third service period were each analyzed separately, while fourth and later service periods were pooled. Heritability was estimated using the sire component of variance and the estimate of the total variance derived from MIVQUEO and maximum likelihood analyses. The data set was restricted to daughters of sires used in multiple herds. Heritability estimates were less than .07 for all traits in the heifer service period except age at first breeding (.2 by maximum likelihood and .13 by MIVQUEO). Similarly, with the exception of conception rate, none of the measures of reproduction had heritabilities greater than .05 for all three remaining service period groups. Conception rate measured as a trait of the male (service sire) ranged from .08 to .135 for second and third service periods. Conception rate as female trait (sire) had heritabilities ranging from .09 to .249 for second and third service periods. Low heritability estimates obtained in this and other studies suggest that large progeny or service sire groups will be necessary to identify the small genetic differences between bulls. Many genetic and phenotypic correlations were forced positive due to a part-whole relationship or due to the fact they were simply different bounds for the same measure. A few correlations were in the range from .50 to .90, but many were not significantly different from zero due to large approximate standard errors. Repeatabilities based upon pairwise comparisons were in the range from 0 to .13. Repeatabilities for the reproductive performance of virgin heifers with first parity ranged from .01 to .06 and were generally smaller than later parities. Repeatabilities based upon repeated measures on the same cow ranged from 0 to .12. Predicted Differences for female (sire) and male (service sire) reproduction were calculated by Best Linear Unbiased Prediction. This analysis included 207 bulls which were in the data both as sire and service sire. Correlations between proofs for male and female reproduction ranged from -.13 to .13. These results suggest limited genetic relationships between male and female fertility. / Ph. D.

The objectives of this research were firstly, to investigate if dairy cattle genotypes in NZ exhibit genetic variation in environmental sensitivity and to determine if this genetic variation is statistically significant from a genetic evaluation perspective, and secondly, to use genetic information including environmental sensitivity data to simulate dairy cattle responses to changes in nutritional regime and variation in climate. A comprehensive review identified that simulation models either overlook, or do not represent environmental sensitivity information where genotypes and breeds respond differently when exposed to variations in environment. A large dataset of daily and total lactation records (yields of milk, fat and protein) from herds participating in the progeny testing of sires from 1989 to 2002 was obtained to test for differences in the environmental sensitivity of dairy cattle in New Zealand. Production data was matched with environmental data relating to climate, herd size, altitude and herd average production levels (a proxy for feeding level). The statistical analyses applying univariate and bivariate multibreed models to environmental character states identified minimal sire re-ranking between environmental character states as measured by genetic and rank correlations. However, differences in yields of milk, fat and protein between New Zealand Jersey and overseas Holstein Friesian systematically diverged with production level, in herds expected to use different levels of supplements. These results suggest New Zealand Jersey cattle are best suited to a grassland-type environment, and overseas Holstein Friesian cattle are more suited to an intensive-type environment. A phenotypic analysis identified thermal environment (cold and hot conditions) significantly affected the expression of production traits in Holstein Friesian, New Zealand Jersey and Holstein Friesian x New Zealand Jersey cattle. Holstein Friesian dairy cattle were more susceptible to the effects of heat conditions than New Zealand Jersey cattle with yields of milk, and concentrations of fat and protein of the former compromised at a lower value for temperature humidity index. Dairy cattle performance is likely to be compromised by heat more frequently than cold conditions in New Zealand. A simulation model that considers how dairy cow genotypes respond to different environments, incorporating the results presented above, was then developed. An initial estimate of feed intake is used to define cow genetic potential based on estimated breeding values for total yields milk, fat and protein, and environmental sensitivity information. A mammary gland module then predicts daily yields of milk, fat and protein based on the cow's genetic potential after considering her age, stage of lactation, body condition score, nutritional status and thermal environment. Live weight change is also predicted via a body energy stores module, which considers the effect of age, stage of lactation, current body condition score, nutritional status, and an estimated breeding value for body condition score. Feed intake is predicted from the requirements for maintenance, growth and pregnancy, and the genetic drive for yields of milk, fat and protein and body fat change. The predictive ability of the model was tested using information from a prior study with two Holstein Friesian genotypes managed in a pasture-based system. The model simulated to a high degree of accuracy, mean values for yields of milk, fat and protein, and concentrations of fat and protein of each genotype. Various tests identified the major source of error between simulated and observed values were due to a lack of simulated variation. In conclusion, the extent of genetic variation in environmental sensitivity for total lactation yields of milk, fat and protein within the range of New Zealand environments are not sufficient to warrant the formation of separate breeding schemes for distinct environments. However, New Zealand Jersey cattle are best suited to a grassland-type environment, and overseas Holstein Friesian cattle are more suited to an intensive-type environment. Genetic variation in the suitability of different breeds for specific environments existed within breeds. A simulation model was developed that was able to simulate the effect of genotype, environment and genotypic differences in environmental sensitivity on daily cow performance.