Retrospective analysis of the accuracy of conversion equations and multiple-trait, across-country evaluations of Holstein bulls used internationally

Potential response from selection schemes based on progeny testing and genomic selection for the Chilean dairy cattle under pastoral systems: a deterministic simulation

Recently, a selection index called Valor Económico Lechero (VEL) was developed for Chilean dairy cattle under pasture. However, a specific selection scheme has not yet been implemented. This study aimed to estimate genetic progress from selection on the VEL selection index based on selection schemes using progeny testing (PT) and genomic selection (GS). Under a PT-scheme, estimated genetic progress was 41.50, 3.44, and 2.33 kg/year for milk, fat, and protein yield, respectively. The realised genetic gain takes eight-year after the PT-scheme implementation, which may be a disincentive for implementing a PT-scheme, suggesting that importing frozen semen of proven bulls could be a preferred alternative. In this case, an option may be to conduct the genetic evaluation of those bulls using their progeny in Chile for the traits included in VEL selection index. In the case of implementing a specific selection scheme, compared to PT, a more profitable alternative might be the implementation of a GS-scheme, that would result in a faster genetic gain in the aggregate breeding value or merit for all the traits included in the selection objective (0.323-0.371 vs. 0.194 σg/year).

Impact of Estimated Genetic Correlations on International Evaluations to Predict Milk Traits

The Interbull procedure for combining dairy bull evaluations uses estimated genetic correlations between countries. It is important to know whether the resulting difficulties from differences in ranking in each country are justified by improved accuracy relative to a system assuming unity correlations. Data submitted for the May 2001 yield and somatic cell score (SCS) Interbull evaluations were processed once with the usual estimated genetic correlations (E01) and again assuming these correlations to be essentially unity (0.995; U01). The 2 sets of resulting evaluations were compared with August 2004 national evaluations (N04) for bulls not having local evaluations used in the 2001 evaluations. Thus, the examination was of Interbull evaluations from foreign data in predicting national evaluations. Countries in the study for yield were Australia, Canada, France, Germany, Great Britain, Ireland, Italy, The Netherlands, New Zealand, and the United States. Countries included for SCS were Canada, France, Germany, Great Britain, The Netherlands, and the United States. For most countries' evaluations, standard deviations of differences between E01 or U01 and N04 were smaller for E01 by about 5 to 7% and correlations between E01 and N04 were higher by 0.01 or the same as for U01 and N04. Although use of estimated correlations tended to improve prediction, the advantage was small. A previous study had concluded no difference in accuracy for yield but did not include Australia and New Zealand, countries with the lowest correlations with other countries. Excluding bulls from those countries produced results for the other 8 countries more like the previous study, but still favoring E01 slightly. Those 2 countries were not in the SCS data. Estimated genetic correlations improved the prediction of future national evaluations slightly in most countries but more substantially for the evaluations and bulls of Australia and New Zealand.

Effectiveness of national and regional sire evaluations in predicting future-daughter milk yield

National and regional bull evaluations were compared for ability to predict standardized milk yield of future daughters. Correlations between evaluations and first-, second-, and third-parity yields of future daughters were calculated within herd-year-month group. Mean correlations with predicted yield of future daughters across the United States were higher for national (0.109, 0.111, and 0.082 for first, second, and third parities, respectively) than for Northeast (0.098, 0.085, and 0.061) Holstein evaluations; corresponding correlations for future Northeast daughters were similar. Bull evaluations based on the first 5 parities of daughters that first calved through 1991 from either California, North Central, Northeast, or Southeast regions as well as from the entire United States were compared with standardized milk yields of daughters that calved later. Correlations with first-, second-, and third-parity yields of future daughters were higher (from 0.001 to 0.011) for national than for regional evaluations. National evaluations were better predictors of future-daughter yield, especially for California and the Southeast. Evaluations based on only first parity were slightly better than those based on the first 5 parities in predicting first-parity yield for 3 of 4 regions but were far less useful in predicting second-or third-parity yield regardless of region. Regional evaluations included fewer bulls because of limited numbers of daughters in each region. The top 100 bulls for genetic merit for milk yield based on regional rankings were inferior to the top 100 bulls based on national ranking by 25 to 173 kg. Reliance on regional rather than national evaluations would reduce current US genetic gains.

Accuracy of Foreign Dairy Bull Evaluations in Predicting United States Evaluations for Yield

Combining foreign daughter data with domestic information in dairy bull genetic evaluations has been shown to improve prediction of future domestic evaluations for US bulls. This study focused on the accuracy of Interbull evaluations of bulls with only foreign daughters, in predicting the latest domestic evaluations (based on US daughters). August 2003 USDA evaluations based only on US daughters were matched with the most recent February or August Interbull evaluations without US daughters. A minimum reliability of at least 80% for yield and 70% for somatic cell score (SCS) was required in both evaluations. This provided pairs of evaluations based on different daughters (foreign or US) for 286 bulls (60 bulls for SCS). Mean Interbull reliabilities on the US scale were 88% for yield and 84% for SCS, and the mean US reliability for the current evaluations was 91% for yield and 80% for SCS. Correlations between the Interbull and domestic evaluations were 0.90, 0.87, 0.90, and 0.87 for milk, fat, protein, and SCS respectively. Expected correlations were 0.89 for yield and 0.82 for SCS. Mean differences between the Interbull and current domestic evaluations were near zero. These foreign bulls had graduated from progeny test programs (selected for positive Mendelian sampling) before being marketed in the United States. Thus, parent average was a substantial underestimate of merit. The small average differences between evaluations from foreign and US daughters and high correlations indicate that Interbull evaluations based solely on foreign daughters are useful predictors of the US evaluations for yield and SCS, providing accuracy in agreement with reliabilities and much better estimates than the alternative, parent averages.

International dairy bull evaluations expressed on national, subglobal, and global scales

Genetic evaluations on a global scale were calculated for Holstein bulls using the May 2001 International Bull Evaluation Service (Interbull) evaluations expressed on each of 27 national scales. National scale data were weighted by the country's proportion of total daughters from all bulls (population size) to represent market share. Correlations between Interbull evaluations on national scales and evaluations on a global scale ranged from 0.961 to 0.998 (mean of 0.988). Number of top 100 bulls for protein yield that were in common between national and global scales ranged from 54 to 94 and was related significantly to mean genetic correlation between a country and the other 26 countries. Weighting of evaluations on national scales by population size, inverse of population size weight, or equal weight produced practically the same group of top bulls and correlations among the three global scales were 0.999. Thus, the method for combining Interbull evaluations expressed on national scales had only minor impact and was much less important than use of all data. Subglobal scales were established by a clustering technique that gave two to five groups. For grazing countries or other atypical systems, a subglobal scale may provide better guidance, although a scale representing three grazing countries did not provide the expected improvement over a global scale in the relationship with the three country scales. If conditions in non-participating countries are generally represented by participating countries, most needs are met by a global scale.

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

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 (Midwest, Northeast, Northwest, Southeast, and Southwest). 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.