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Title: Genetics of health and fertility in dairy cattle
Author: Pryce, J. E.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1997
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In this study genetic parameters were estimated and the importance of genotype by feeding system interactions investigated for a range of health and fertility traits in dairy cattle. Data were from the Langhill Dairy Cattle Research Centre and two UK recording schemes, one a recording scheme operated by the Scottish Livestock Services Ltd. (SLS) and the other a health and fertility recording and management system, the Dairy Information System (DAISY). Genetic parameters for 305 day yield of milk and its components, health traits (mastitis, somatic cell counts (SCC), lameness and milk fever) and fertility traits (calving interval, days to first service and conception to first service) were estimated using data from DAISY and SLS. Heritabilities and correlations agreed well between the two datasets implying that the nature of the recording scheme had little effect on the parameter estimates obtained. Heritabilities for all health and fertility traits were less than 0.10 with the exception of SCC which had a heritability of 0.15. It was concluded that selection for production has led to a deterioration in some health and fertility traits in UK dairy herds. This deterioration could be halted, or the situation improved, by direct recording and selection. However, in the absence of a national recording scheme for health and fertility, an alternative would be to use predictor traits, as they are generally easier to measure and record and have higher heritabilities. Results from this study support the use of SCC as a selection criterion for mastitis (on a small dataset), but the regressions on sire predicted transmitting abilities for type were less conclusive. Therefore future research should investigate associations between health, fertility, production, type, SCC and other potential predictor traits more fully and compare the role of direct measurements of these traits or indirect predictions of them in national indices.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available