A multiple-trait herd cluster model for international dairy sire evaluation

International dairy sire evaluations have traditionally been calculated usi ng a two-step process. Lactation records within each country are used to pr edict national estimated breeding values, then these national breeding valu es 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, cl imate, 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, Fi nland, Israel, Switzerland, and five regions of the US (Midwest, Northeast, Northwest, Southeast, and Southwest). Herds were grouped into clusters bas ed on data of 13 descriptive variables: herd size, calving interval, milkin g frequency, age at first calving, milk yield, month of calving, predicted transmitting ability of sire for milk, percentage North American genes of s ire, latitude, altitude, temperature, rainfall, and percentage of arable la nd used for pasture. Five clusters were formed; each cluster contained herd s from 5 to 11 countries or regions. Genetic correlations between herd clus ters ranged from 0.81 to 0.97. The herd cluster model is intuitively appeal ing, because genetic merit of an animal is predicted for each unique enviro nment or management system, regardless of country borders. This model is pa rsimonious (the number of traits was reduced from 13 to 5) and is computati onally feasible for large data sets.