Sire mating advice framework for cattle to recommend which beef bull to mate to individual dairy females.

A decision support tool or system is a computerized information system used to support decision-making in a business; one central component to profitable dairy cattle production systems is the appropriate mating of bulls and females. While tools have been described to aid mating decisions between dairy bulls and dairy females, or between beef bulls and beef females, there is a void of such tools that recommend which beef bull to mate to individual dairy females. The objective of the present study was to develop and validate a framework, founded on linear programming, to aid herd-level mating decisions where the bull-female mating is tailored based on complementarity and compatibility of both mates; consideration in the process was given to the genetic merit of both mates for a series of traits as well as the life history of the female herself. Traits considered in the linear function to be maximized in the linear programming procedure were those related to calving performance traits (i.e., calving dystocia, perinatal mortality and gestation length) and subsequent beef performance (i.e., docility, feed intake, and carcass merit); each trait was weighted in the linear function by its respective economic importance. First, a calibration and validation data set from a national database were generated using data truncated on calendar year to validate predictions of progeny performance. Expected performance of progeny was based on a combination of estimated genetic merit and non-genetic effects which would be available at the time of mating. The direction of the associations in the validation population was in line with expectations and, in many instances, the extent of the association was close to expectation. Using real dairy cow data of 284,334 cows from 1,535 herds, 6 randomly chosen candidate beef bulls of multiple breeds were selected per herd for mating assignments to all cows, each with an equal number of matings. Bull-cow matings were assigned either at random or using the developed linear programming framework. While the mean expected genetic merit of the hypothetical progeny was the same for both scenarios (as expected), the bull-cow assignments proposed by the linear programming mating framework were assortative in nature. Bulls with a greater genetic risk of dystocia in their progeny were, on average, recommended for mating to cows that, genetically, were less likely to experience calving dystocia based on their direct and maternal estimates of genetic merit. Similarly, where possible, bulls that genetically were expected to produce, on average, heavy and more conformed carcass progeny were mated to cows whose progeny were expected to have lighter and less conformed carcasses based on the genetic merit inherited from the cow. A case study of one large dairy herd illustrating in more detail how the linear programming-based mating algorithm operates is also presented especially in relation to assortative mating for calving dystocia and carcass merit. The validated linear programming-based mating decision support tool presented in this study describes a digital framework for aiding decision making in beef-on-dairy herd breeding programs.