1. Methodology for Prediction of Bull Fertility from Field Data M. T. Kuhn* and J. L. Hutchison Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350 Abstract M26 2006 RESULTS & DISCUSSION Single vs Expanded SSR term and All vs First Service Only Use of the expanded SSR term improved accuracy in both models by 3.5% Expanded terms must be fit as random rather than fixed to avoid bias No difference between linear and threshold models Use of all services increases accuracy by 7.5% All biases  0 Accuracy (%) by CG Size and Number of Matings/bull Herd CG Size N. MatingsModel51020100 10Linear45.646.348.749.4 Threshold45.546.348.649.3 1000Linear88.089.989.690.5 Threshold88.089.989.690.5 All biases  0 Effect of Unknown SSR on Accuracy (%) Two approaches for handling unknown SSR were compared 1) Delete the matings, 2) use unknown SSR ID % UnknownApproachLinearThreshold 1Delete86.3 Unknown SSR ID86.386.4 10Delete84.2 Unknown SSR ID84.5 CONCLUSIONS By far, the most important factor affecting accuracy is not the model but simply the number of matings Compared to use of first service only, use of all services increases accuracy by 7.5% Predictions from linear model as accurate as those from threshold model, even with small subclasses Use of expanded SSR term improves accuracy of prediction CG size has relatively minor impact on accuracy; magnitude of impact decreases as number of breedings per bull increases. Small CG size does not introduce bias in either model 10% unknown/incorrect SSR ID reduces accuracy by about 2% Effect of unknown pregnancy status is relatively minor; when all unsuccessful last services were (erroneously) changed to successes accuracy was reduced 2 to 3% Only when mean CR was 10% did the threshold model show an advantage over the linear model; however, with mean US Holstein cow CR  35%, a linear model will provide predictions of bull fertility equal in accuracy to a threshold model FUTURE RESEARCH Determine whether use of standard deviation of predictor in threshold model probability calculations improves accuracy of predictions for threshold model Introduce relationships amongst cows and compare linear animal and threshold sire models All biases  0 Similar results for incorrect SSR Effect of Unknown Pregnancy Status on Accuracy (%)  13.5% of last services were failed pregnancies; these were all re-coded as successes to compare to accuracies when true outcomes were used Last ServiceLinearThreshold True Outcome85.7 All successes83.082.8 Effect of Population Mean CR on Accuracy (%) Mean CRLinearThreshold 0.10 84.4*86.0 0.3585.7 0.5084.3 * Linear model variance estimates  0; threshold model variance estimates used for linear model when  = 0.10 INTRODUCTION AIPL has assumed responsibility for estimation of bull fertility Current methodology same as used by DRMS; NRR, linear model Current objectives investigate methodology to improve accuracy OBJECTIVES Compare alternative models for prediction of bull fertility Linear vs threshold models Use of “expanded” service sire (SSR) term Factors like SSR inbreeding, age, etc. may affect bull fertility. If so, estimating these factors as separate components of the bull’s CR may improve accuracy. Use of all services vs first service only Determine effects of various data characteristics on accuracy and bias of linear and threshold model predictions Contemporary group (CG) size, number of matings/bull Unknown and incorrect SSR Unknown pregnancy status for last service on file Population mean conception rate (CR) MATERIALS & METHODS Simulation model: y =  + Herd + ssrA + ssrB + SSR + Cow + e Conception = 0 (failure) if y   = 1 (success) if y   ssrA, ssrB = generic factors affecting bull fertility General population characteristics 100,025 cows/rep,  2 = 0.064 for cow,  e 2 = 1 250 SSR/rep,  2 = 0.0039 for SSR 5 levels for ssrA and ssrB,  2 = 0.00195 for each No relationships amongst cows or SSR  = mean CR = 0.35  CR for US Holstein cows Model for analysis: Concep = Herd + ssrA + ssrB + SSR + Cow + e Estimated variances used in analytical model All effects except herd were fit as random effects Predicted CR computed as  + ssrA + ssrB + SSR [1] Accuracy computed as Cor(True CR, CR) To ascertain benefit of expanded SSR term: include ssrA and ssrB in simulation but not in analytical model Compute CR =  + SSR [2] Compare predictions from [1] to predictions from [2]   