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Monthly model for genetic evaluation of laying hens. II. Random regression
A Anang1, N Mielenz, L Schüler
1Institute of Animal Breeding and Husbandry with Animal Clinic, Martin-Luther University, A. Kuckhoff Strasse 35, D-06108 Halle, Germany.
British Poultry Science
|August 28, 2002
Summary
Genetic evaluation of laying hens using monthly production records is more effective than cumulative records. The random regression with Ali and Schaeffer covariates (RRMAS) model showed the best performance for genetic improvement.
Area of Science:
- Animal genetics
- Poultry breeding
- Quantitative genetics
Background:
- Accurate genetic evaluation is crucial for improving laying hen productivity.
- Traditional methods often rely on cumulative production, which may not capture dynamic genetic trends.
- Test day models with random regression, adapted from dairy cattle, offer a potential advancement.
Purpose of the Study:
- To investigate the efficacy of monthly production records for genetic evaluation in laying hens.
- To compare the performance of various genetic models, including random regression and multiple trait models.
- To identify the optimal model for genetic selection of high-producing laying hens.
Main Methods:
- Analysis of monthly and cumulative egg production records from 6450 laying hens using restricted maximum likelihood (REML).
- Comparison of five models: random regression with Ali and Schaeffer covariates (RRMAS), quartic polynomial (RRMP4), fixed regression (FRM), multiple trait (MTM), and cumulative (CM).
- Evaluation based on Spearman rank correlations of estimated breeding values and phenotypic comparisons of selected hens.
Main Results:
- The RRMAS and MTM models demonstrated similar heritability patterns for monthly production.
- Spearman rank correlations between monthly models (RRMAS, FRM, MTM) were high (0.91–0.98), while correlations with the cumulative model (CM) were lower (0.85–0.87).
- The RRMAS model yielded the highest correlations for sire breeding values, indicating superior accuracy.
Conclusions:
- Monthly production records provide more accurate genetic evaluations for laying hens compared to cumulative records.
- The random regression with Ali and Schaeffer covariates (RRMAS) model is recommended as the most effective among those tested for genetic improvement programs.
- Utilizing intermittent monthly data, such as odd months, can support cost-efficient recording schemes without significant loss of information.
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