Direct and indirect conversion of bull evaluations for yield traits between countries

Area of Science:

• Animal Genetics
• Dairy Science
• Quantitative Genetics

Background:

• Accurate genetic evaluations are crucial for dairy cattle breeding programs.
• International genetic evaluations require reliable methods for comparing bulls across different national datasets.
• Existing conversion methods may have limitations in accuracy and applicability.

Purpose of the Study:

• To compare the accuracy of different methods for converting US Holstein bull genetic evaluations to Canadian, Italian, Mexican, and Netherlands evaluations.
• To identify the most effective conversion methodology for international genetic comparisons.
• To assess the impact of data characteristics on the performance of conversion equations.

Main Methods:

• Genetic evaluations from the US were matched with those from Canada, Italy, Mexico, and the Netherlands.
• Milk yield conversion equations were computed using least squares, Goddard, and Wilmink methods.
• Accuracy was assessed by data splitting and analyzing mean differences and standard deviations between actual and converted evaluations.

Main Results:

• Imperfections in conversion were primarily attributed to inherent data variations and biases, rather than conversion method inadequacy.
• The least squares method showed slightly better performance but is not recommended by the International Bull Evaluation Service.
• The Goddard method generally outperformed the Wilmink method, though data limitations exist; a variation of the Goddard method matched Wilmink's accuracy.
• Indirect conversion equations were accurate for US to Mexico and US to Netherlands comparisons, but less so for US to Italy.

Conclusions:

• Data characteristics significantly influence the accuracy of international genetic evaluation conversions.
• Indirect conversion methods, particularly variations thereof, can be valuable for international bull genetic comparisons until direct comparisons are feasible.
• Further research into data harmonization and advanced conversion techniques is warranted for improving global genetic evaluations.