Abstract
The objectives of this study were to derive phenotypic
and genetic prediction equations of liveweight
from linear conformation traits, and estimate genetic
and phenotypic parameters for these traits. Data pertained
to 2,728 conformation and liveweight records of
613 cows in 1,529 lactations. Cows were raised at the
Scottish Agricultural College research station and had
calved between 2002 and 2010. Fifteen linear conformation
traits were considered as predictors. To validate
phenotypic predictions, the data set was randomly split
into independent reference and validation subsets. Reference
subsets were used to derive prediction equations
with the use of a mixed model. Comparisons between
predicted and actual liveweight in the validation subsets
indicated that stature, chest width, body depth,
and angularity could be used to derive phenotypic
predictions of liveweight. Accuracy of these predictions
was better for first-lactation than for all-lactation liveweight
data. Significant genetic correlations between
liveweight and the 4 predictor traits ranged from 0.49
to 0.76, and phenotypic correlations were 0.33 to 0.56.
Estimated genetic (co)variances were used to develop
prediction equations of animal genetic merit for liveweight
from routinely calculated genetic evaluations for
conformation traits.
Original language | English |
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Pages (from-to) | 2170 - 2175 |
Number of pages | 6 |
Journal | Journal of Dairy Science |
Volume | 95 |
Issue number | 4 |
Early online date | 26 Mar 2012 |
DOIs | |
Publication status | Print publication - Apr 2012 |
Keywords
- Conformation trait
- Liveweight prediction
Fingerprint
Dive into the research topics of 'Prediction of liveweight from linear conformation traits in dairy cattle'. Together they form a unique fingerprint.Impacts
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Incorporating novel data-driven approaches into cattle genetic improvement programmes leads to better animal performance and overall economic gains
Wall, E. (Participant), Coffey, M. (Participant), Pritchard, T. (Participant), Banos, G. (Participant) & Mrode, R. (Participant)
Impact: Technological
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