Test-day records for protein yield, protein percent, fat percent and somatic cell score combined with diagnoses for health traits from 19,870 Holstein cows kept in 9 large-scale contract herds in the region of Thuringia, Germany, were used to infer genetic parameters. From an electronic database system for recording diagnoses, 15 health disorders with highest incidences were extracted and grouped into the following 5 disease categories: claw disorders, mastitis, female fertility, metabolism, and ectoparasites. In a bayesian approach, threshold methodology was applied for binary distributed health disorders and linear models were used for gaussian test-day observations. Variances and variance ratios for health disorders were from univariate and covariance components among health disorders and between health disorders, and test-day production traits were from bivariate repeatability models. Incidences of health disorders increased with increasing parity and were substantially higher at the beginning of lactation. Only incidences for ectoparasites slightly increased with increasing stage of lactation. Heritabilities ranged from 0.00 for ectoparasites to 0.22 for interdigital hyperplasia. Heritabilities of remaining health disorders were in a narrow range between 0.04 (corpus luteum persistent) and 0.09 (dermatitis digitalis). Clustering diseases into categories did not result in higher heritabilities. The variance ratio of the permanent environmental component was higher than the heritability for the same trait, pointing to the conclusion that non-genetic factors influence repeated occurrence of health problems during lactation. Repeatabilities were relatively high with values up to 0.49 for interdigital hyperplasia. Genetic correlations among selected health disorders were low and close to zero, disproving the assumption that a cow being susceptible for a specific disease is also susceptible for other types of health disorders. Antagonistic genetic relationships between test-day protein yield and health disorders were found for ovarian cysts (0.57) and clinical mastitis (0.29). Remaining genetic correlations between diseases and production traits were close to zero. The genetic correlation between clinical mastitis and somatic cell score was 0.69. This study revealed reliable genetic parameters for health disorders and underlined the possibility of precise health data recording by farmers from contract herds that can be used for genetic evaluation of health traits.

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates.

No abstract

The aims of the study were to evaluate the relationships among milk urea nitrogen and nonreturn rates at the phenotypic scale, and to estimate genetic parameters among milk urea nitrogen, milk yield, and fertility traits in the early period of lactation. Milk yield, protein percentage, the interval from calving to first service, and 56- and 90-d nonreturn rates were available from 73,344 Holstein cows from 2,178 different herds located in a region in northwestern Germany. Generalized linear models with a logit link function were applied to assess the phenotypic relationships. Bivariate threshold-threshold, linear-threshold, and linear-linear models, fitted in a Bayesian framework, were used to estimate genetic correlations among traits. Milk yield, protein percentage, and milk urea nitrogen were means from test-day 1 (on average 20.8 d in milk) and test-day 2 (on average 53.1 d in milk) after calving. An increase in milk urea nitrogen was associated with decreasing 56-d nonreturn rates on the phenotypic scale. At fixed levels of milk urea nitrogen, greater values of protein percentage, indicating a surplus of energy in the feed, were positively associated with nonreturn rates. Heritabilities were 0.03 for 56- and 90-d nonreturn rates, 0.07 for interval from calving to first service, 0.13 for milk urea nitrogen, and 0.19 for milk yield. Service sire explained a negligible part (below 0.15%) of the total variance for nonreturn rates. Genetic correlations between the interval from calving to first service and nonreturn rates were close to zero. The genetic correlation between nonreturn rates was 0.94, suggesting that a change from nonreturn after 90 d to nonreturn after 56 d in the national genetic evaluation would not result in any loss of information. The genetic correlation between milk yield and nonreturn after 56 d was -0.31, and between milk yield and calving to first service was 0.14, both indicating an antagonistic relationship between production and reproduction. The genetic correlation between milk yield and milk urea nitrogen was 0.44, reflecting an energy deficiency in early lactation. The genetic correlations between milk urea nitrogen and nonreturn rates were too weak (-0.19 for 56-d nonreturn rate, and -0.23 for 90-d nonreturn rate) to justify the use of milk urea nitrogen as an additional trait in genetic selection for fertility, as demonstrated by selection index calculations.