Estimation of genetic parameters using health, fertility and production data from a management recording system for dairy cattle

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Abstract
The Dairy Information System (DAISY) was developed to record fertility and health information for use in research and to help farmers manage their farms. Data from 33 herds recording health and fertility over a 6-year period were used to study genetic relationships of several health, fertility and production traits. There were 10 569 records from 4642 cows of all parities. These were used to estimate genetic parameters for health: mastitis, lameness and somatic cell score (SCS), for fertility: calving interval, days to first service, conception to first service and for production: 305-day milk, butterfat and protein yields. Heritabilities for these traits were also estimated for the first three lactations. (Co)variances were estimated using linear, multitrait restricted maximum likelihood (REML) with an animal model. Mastitis and lameness were treated as all-or-none traits. The incidence of these diseases increased with lactation number, which may lead to variance component estimation problems, as the mean is linked to the variance in binomial distributions. Therefore, a method was used to fix the within-lactation variance to one in all lactations while maintaining the same mean. The heritability for SCS across lactations was 0·15. Heritabilities for other health and fertility traits were low and ranged between 0·013 and 0·047. All genetic correlations with the production traits were antagonistic implying that selection for yield may have led to a deterioration in health and fertility. The genetic correlation between SCS and mastitis was 0·65 indicating that indirect selection for improvements in mastitis may be achieved using somatic cell counts as a selection criterion. The potential use of linear type scores as predictors of the health traits was investigated by regressing health traits on sire predicted transmitting abilities for type. The results indicate that some type traits may be useful as future selection criteria.