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Predictive ability of models for calving difficulty in US Holsteins
E L de Maturana1, D Gianola, G J M Rosa
1Department of Animal Sciences, University of Wisconsin, Madison, WI 53706, USA. lopezdematur@wisc.edu
Summary
Four models for analyzing calving difficulty (CD) in Holstein cows showed similar predictive abilities. Analyzing CD with four categories, rather than three, is recommended for more informative genetic evaluations.
Area of Science:
- Animal Genetics
- Dairy Science
- Quantitative Genetics
Background:
- Calving difficulty (CD) is a critical trait in dairy cattle, impacting cow and calf health and farm economics.
- Accurate genetic evaluation of CD is essential for breeding programs aiming to reduce incidence.
- Threshold models are commonly used for traits like CD, but their optimal configuration requires investigation.
Purpose of the Study:
- To evaluate the predictive performance of alternative threshold models for analyzing calving difficulty (CD) in Holstein cows.
- To compare models with CD classified into three or four categories.
- To assess the benefit of jointly analyzing CD with gestation length (GL) versus univariate analysis.
Main Methods:
- Four threshold models were compared for analyzing CD in 90,393 primiparous Holstein cows.
- Models varied in CD categorization (3 vs. 4 categories) and analysis type (univariate vs. joint with GL).
- Predictive ability was assessed using mean squared error, Kullback-Leibler divergence, Pearson's correlation, and classification accuracy of bulls.
Main Results:
- All four models demonstrated comparable predictive abilities for calving difficulty.
- Joint analysis of CD with gestation length offered minimal improvement over univariate models.
- Models with four CD categories showed slightly better potential for providing more information than three-category models.
Conclusions:
- The choice between three or four categories for CD in threshold models has a minor impact on predictive ability.
- Analyzing calving difficulty with four categories is suggested to maximize information content.
- Joint analysis with gestation length does not significantly enhance predictive performance for calving difficulty.
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