title: Stochastic dynamic simulation modeling including multitrait genetics to estimate genetic, technical, and financial consequences of dairy farm reproduction and selection strategies.
abstract: The objective of this study was to develop a daily stochastic dynamic dairy simulation model that included multitrait genetics and to evaluate the effects of reduced genetic models and various reproduction and selection strategies on the genetic, technical, and financial performance of a dairy herd. The 12 correlated genetic traits included in the 2014 lifetime net merit (NM$) index were modeled for each animal. For each animal, a true breeding value (TBV) for each trait was calculated as the average of the sire's and dam's TBV, plus a fraction of the inbreeding and Mendelian sampling variability. Similarly, an environmental component for each trait was calculated and was partitioned into a permanent and a daily (temporary) effect. The combined TBV and environmental effects were converted into the phenotypic performance of each animal. Hence, genetics and phenotypic performances were associated. Estimated breeding values (EBV) were also simulated. Genetic trends for each trait for the service sire were based on expected trends in US Holsteins. Surplus heifers were culled based on various ranking criteria to maintain a herd size of 1,000 milking cows. In the first 8 scenarios, culling of surplus heifers was either random or based on the EBV of NM$. Four different genetic models, depending on the presence or absence of genetic trends or genetic and environmental correlations, or both, were evaluated to measure the effect of excluding multitrait genetics on animal performance. In the last 5 scenarios, the full genetic model was used and culling of surplus heifers was either random or based on the EBV of NM$ or the EBV of milk. Sexed semen use and reliability of the EBV were also varied. Each scenario was simulated for 15yr into the future. Results showed that genetic models without all 12 genetic trends and genetic and environmental correlations provided biased estimates of the genetic, technical, and financial performance of the dairy herd. Average TBV of NM$ of all cows in the herd was $263 greater in yr 15 in a scenario that combined sexed semen use in heifers and culling of surplus heifers with the lowest EBV of NM$, compared with a scenario that used only conventional semen and surplus heifers were culled randomly. The average TBV of daughter pregnancy rate of all cows in the herd was 1.25 percentage points greater in yr 15 in a scenario that combined using sexed semen in heifers as well as culling of surplus heifers ranked by EBV of NM$, compared with a scenario using conventional semen only as well as culling surplus heifers ranked by EBV of milk. In conclusion, the multitrait genetics model resulted in improved estimates of genetic, technical, and financial effects and appears useful to evaluate consequences of various reproduction and selection strategies within a dairy farm.
authors: Department of Animal Sciences, University of Florida, Gainesville 32611.
authors: K
authors: K
authors: Kaniyamattam
authors: Department of Animal Sciences, University of Florida, Gainesville 32611.
authors: M A
authors: MA
authors: Elzo
authors: Animal Improvement Program, Animal Genomics and Improvement Laboratory, ARS-USDA, Beltsville, MD 20705-2350.
authors: J B
authors: JB
authors: Cole
authors: Department of Animal Sciences, University of Florida, Gainesville 32611. Electronic address: devries@ufl.edu.
authors: A
authors: A
authors: De Vries
mesh_terms: D000818:Animals; D001947:Breeding; D002417:Cattle; D003612:Dairying; D000072480:Farms; D005260:Female; D007315:Insemination, Artificial / Q000662:veterinary; D015203:Reproducibility of Results; D012098:Reproduction
keywords: modeling; multitrait genetics; profit; selection