A Multiple-Trait Herd Cluster Model for International Dairy Sire Evaluation

¹ Department of Dairy Science, University of Wisconsin, Madison 53706

International dairy sire evaluations have traditionally been calculated using a two-step process. Lactation records within each country are used to predict national estimated breeding values, then these national breeding values are transformed to the genetic base, scale, and units of measurement of other countries by using conversion formulae or the multiple-trait, across-country evaluation method. A major limitation of this approach is the need to define environments (traits) according to country borders. Herds located in small, neighboring countries may be much more similar in management, climate, and genetic background than herds located far apart within a single large country. In the present study, international genetic evaluation with herd clusters is proposed. Data consisted of 4.6 million lactation records from 46,000 herds in Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, Israel, Switzerland, and five regions of the US (Mid-west, Northeast, Northwest, Southeast, and South-west). Herds were grouped into clusters based on data of 13 descriptive variables: herd size, calving interval, milking frequency, age at first calving, milk yield, month of calving, predicted transmitting ability of sire for milk, percentage North American genes of sire, latitude, altitude, temperature, rainfall, and percentage of arable land used for pasture. Five clusters were formed; each cluster contained herds from 5 to 11 countries or regions. Genetic correlations between herd clusters ranged from 0.81 to 0.97. The herd cluster model is intuitively appealing, because genetic merit of an animal is predicted for each unique environment or management system, regardless of country borders. This model is parsimonious (the number of traits was reduced from 13 to 5) and is computationally feasible for large data sets.

Key Words: international genetic evaluation • multiple traits • cluster analysis

Submitted on August 25, 1999
Accepted on December 8, 1999

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