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Country bias in international dairy bull evaluations

Country bias in international dairy bull evaluations. MACE Evaluation Process. Interbull combines yield evaluations from 27 national Holstein bull populations. Estimated genetic correlations between evaluations from different populations are applied.

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Country bias in international dairy bull evaluations

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  1. Country bias in international dairy bull evaluations

  2. MACE Evaluation Process • Interbull combines yield evaluations from 27 national Holstein bull populations. • Estimated genetic correlations between evaluations from different populations are applied. • Results are reported on the scale of each participating country organization.

  3. Investigation of Country Bias • Full brothers are expected to have the same genetics on average. • Full brothers with daughters in different countries should have the same MACE evaluations (on average). • Do full brother evaluations reveal country bias?

  4. Data • May 2005 Interbull evaluations of Holsteins for milk, fat, and protein yields, and SCS • Eligible bulls • ≥1 eligible full-brother • Daughters in only one eligible country • Eligible countries • Represented in ≥25 multi-country families • 18 countries for yield • 15 countries for SCS

  5. Data

  6. Multi-Country Families

  7. Ties Between Countries

  8. US Ties to Other Countries

  9. Model • Full-brother family (absorbed) • Country of bull daughters • Analysis of MACE evaluations on the US scale • Difference between evaluations based on foreign daughters and those based on domestic (US) daughters • Evaluations on several other country scales also analyzed

  10. Results • Overall significance for country differences on all country scales for yield traits • Overall, no significance for SCS • For SCS, South African bulls significantly disadvantaged on all scales

  11. Results for US-Scale Evaluations

  12. Results for Other Evaluation Scales

  13. Results for Other Evaluation Scales

  14. Results for Other Evaluation Scales

  15. Possible Complications • Genetic correlations • Parent averages (completeness and phantom grouping) • National animal model vs. sire-MGS MACE • Preferential treatment • Pre-selection (markers, etc.)

  16. Conclusions • Apparent biases for yields but not SCS • Some countries appear disadvantaged while others are favored across all country scales • Caused by shortcomings in national systems or international system? • Source of biases unknown and may be different for different country pairs

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