INTRODUCTION

1. RESULTS • INTRODUCTION • In May 2006, AIPL began evaluation of U.S. bull fertility. General research objectives: investigate options for modeling and trait definition that might improve accuracy • Specific goal of this research: determine which available nuisance variables to include in the evaluation model and how to model them • Factors considered were management (mgt) groups based on herd-yr-season-parity-registry status (HYSPR) , Yr-State (St)-Mo, cow age, DIM, lactation, service number, milk yield, cow effects, and short heat intervals • MATERIALS & METHODS • Comparing Predictors from Alternative Models • Bulls’ predicted conception rates(CR) computed from estimation data (n=3,613,907) and compared to their average CR in set-aside data (n=2,025,884) using accuracy, bias, and MSE; the 803 bulls with a min. of 50 matings for estimation and 100 matings in the set-aside data were included in comparisons. Only AI cow breedings were included • Management Groups • Minimum (target) group sizes tested were 3, 5, 10, 20 • Many small groups occurred; thus 3 basic strategies tested: • Exclude records if HYSPR does not have the min. number (exact HYSPR groups) • Combine groups until the target size is reached; exclude the group if target not reached • Combine to target size but if HY has a specified minimum number of records, allow it into the evaluation; HY minimums were 2, 5, 10 when target sizes were 5, 10, and 20, respectively • Model: y = HYSPR + 1*Milk + 2*Milk2+ 3*AgeCow+ 4*AgeCow2 + 5*DIM + 6*DIM + 7*FBull + 8*FMating + AgeBull + Stud-Yr + Service Sire (SSR) + ACow + PECow + e, y = conception, yes or no • Other Factors • Tested by dropping/adding factors of interest from/to the basic model of: y = HYSPR + SSR variables + PE + A +AgeCow + DIM + Yr-St-Mo + Milk + Lact+ e • SSR variables included: FSSR, FMating, AgeBull, Stud-Yr, SSR • The HYSPR strategy used was to combine to a target group size of 20 and allow the HY into the evaluation if it had at least 10 breedings • Preliminary results showed: • Use of 305d-2x-ME milk yield provided as good or better predictions than use of test-day yields; ME records also did as well as FCM. Thus, ME milk yield used • For quantitative nuisance variables (e.g., cow age), categorical variables found to be preferable over linear and quadratic covariates; relationships with CR were not linear or quadratic. Thus, quantitative vars. fit as categorical • Combining mgt groups implies some groups contain multiple seasons and lactations; inclusion of Yr-St-Month and Lactation (Lact) found to improve prediction and therefore included in all models Management Groups • Generally, combining groups resulted in higher correlations of predicted CR with bulls’ average CR in set aside data, than did using exact HYSPRs (no combining); except in the case where min. group size was 3, restricting to exact HYSPRs resulted in the loss of too many records • Allowing HYs into the evaluation that had fewer than the target number of records was beneficial only when target group size was 20; considerably more records were salvaged when target group size was 20 than when it was 5 or 10 • In general, though, differences among the options tested were small; provided that excessive data exclusion is avoided, formation of mgt groups will not have a large impact on accuracy • Combining groups to a target group size of 20 and allowing HYs in if they have a min. of 10 records maximized accuracy. The small mean difference for this option was eliminated by categorization of quantitative nuisance variables, as can be seen below (Basic model) Other Factors • Models are sorted from best to worst for each statistic (mean difference, correlation, and mean square error); the model listed first was the best for that statistic and the model listed last was the poorest • The model without cow age (but with Lact; see basic model in methods) had the smallest mean difference but mean difference between bulls’ predicted CR and CR in the set-aside data was nearly 0 for all models, except when all nuisance variables dropped from the model (Omit All) • The model with service number (ServN) and without DIM maximized accuracy and minimized MSE; correlations with both in the model were lower than with just service number because these 2 variables are highly correlated; the importance of including at least one is seen from the correlation when DIM was omitted without including ServN (Omit DIM) • The range in correlations and MSEs, however, was generally small, except when all nuisance variables were omitted • While simple average CR was 9% lower for breedings preceded by a short breeding interval (10-17 days, min. of 10 required), they accounted for only 2.5% of all breedings and the max percentage for any one bull was 9%; thus, this variable had minimal impact overall. For bulls where these breedings accounted for at least 5% of their matings (52 out of 803), accuracy improved by 0.4% when this variable was included • CONCLUSIONS • Combining HYSPR groups to a target size of 20 and allowing HYs in with a min of 10 records maximized accuracy and thus will be implemented • Other nuisance variables to include are: cow PE, cow breeding value, cow age, Yr-St-Mo, ME milk yield, lactation, service number, and a short breeding interval variable; quantitative nuisance variables will be fit as categorical variables.