Determinants of fertility in timed artificial insemination programs in beef cattle: predictive ability and risk...

Timed artificial insemination (TAI) is a technology widely used in cattle production based on controlling ovarian follicular growth. This study analyzed a large database aiming to determine the influence of several intrinsic and extrinsic female factors, as well as their interactions to determine risk factors and produce prediction ability in beef cattle. A total of 1 832 999 TAIs conducted on 2 002 farms across South American countries were considered for the analysis, including 15 main fixed effects or interactions in the statistical model, in addition to five random effects. The pregnancy/A.I. (P/AI) was affected by Order of service (1st TAI > resynchronizations), body condition class (BCS) (high > medium > low), female genetic group [Bos taurus and crossbreds > Bos indicus], breeding season (reduction of the P/AI every year), female category [Non-lactating multiparous > Suckled multiparous > Suckled primiparous > Nulliparous heifers], period of year (July-September, October-December and January-March > April-June) and climatic region as well as the interactions between Order of service and female category, BCS class and female genetic group (impact of BCS: Bos taurus or crossbreed animals > Bos indicus), BCS and female category (impact of BC:S Suckling > non-Suckling categories), female category and time of female availability, female category and female genetic group, female category and climatic region, and climatic region and period of the year. Farm, technician, and sire were the variables with the highest predictive ability for P/AI. At the same time, breeding season, climatic region, and time of female availability were the variables with the lowest predictive ability. In conclusion, the main female intrinsic factors that affected fertility in commercial beef cattle A.I. programs were the Order of service, BCS class, female category, and female genetic group. The female extrinsic factors that most affected P/AI were the breeding season and the climatic region. Farm, A.I. technician, sire, and the interaction between the female category and BCS class were the variables with the highest predictive abilities on pregnancy per TAI. Conjunctural factors, which are more adjustable, have a higher impact on P/AI prediction ability than structural factors. Thus, farm management and structure, A.I. technician, bull semen, and female BCS should be the main factors of attention to obtain good results in applying this biotechnology in beef cattle. Despite the influence of each factor, this study demonstrated the usefulness of analyzing big databases, allowing to determine effects that cannot be studied with experimental approaches, providing a complementary approach to decide where to focus future studies to enhance TAI pregnancy rates in beef cattle.