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Characteristics of genetic evaluations for daughter fertility in relation to other fitness traits

Explore the characteristics of genetic evaluations for daughter fertility in relation to other fitness traits. Learn about the economic impacts, data donors, and reproductive traits involved in these evaluations. Compare evaluation characteristics and determine the appropriate perspective for using current genetic tools.

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Characteristics of genetic evaluations for daughter fertility in relation to other fitness traits

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  1. Characteristics of genetic evaluations for daughter fertility in relation to other fitness traits Abstr. 519

  2. DHI-Provo lactation records

  3. Economic impact • Lower herd milk yield • Increased insemination costs • Losses from involuntary culling for fertility • International export disadvantage

  4. Data donors • Dairy Records Management Systems Raleigh, NC • AgSource Cooperative Services Verona, WI • AgriTech Analytics Visalia , CA • Pennsylvania DHIA Service Center University Park, PA

  5. Reproductive traits • Days to 1st breeding • Service period • Percentage of nonreturn to 1st service • Services per conception • Days open

  6. Pregnancy rate (PR) • Percentage of nonpregnant cows that become pregnant during next 21 d • Derived from days open: PR = (233 – days open) / 4

  7. Selection for fertility? • Pessimistic • R.H. Foote, 1969, JDS • R. Nebel, 2003, Holstein International • Optimistic • Norman et al., 2003, ADSA meeting

  8. Coefficients of variation (CV) DPR = daughter pregnancy rate, PL = productive life, SCS = somatic cell score

  9. Genetic progress G = accuracy  genetic SD  selection intensity  generation interval Dickerson & Hazel, 1944

  10. Objectives • Compare evaluation characteristics • DPR • Other recent fitness traits • Determine appropriate perspective for use of current genetic tools

  11. Genetic trend: DPR

  12. Genetic trend: SCS, PL

  13. Mean cow reliability (%)

  14. Heritability (%)

  15. Mean bull reliability (%)

  16. Birth year and AI organization • Effect on PTA variation: • No differences among AI organizations for DPR, but an effect found for PL and SCS

  17. Conclusions • DPR reliability comparable to reliabilities for other fitness traits • DPR should be included in all aspects of genetic programs to reverse decline in reproductive performance • Little difference in DPR among AI organizations • If fitness traits receive 50% of weight in future net merit indexes, acceptance of lower reliability will become more common

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