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Optimization of selection response using artificial insemination and new reproductive technologies in dairy cattle
Abstract
A stochastic simulation was used in the first study to evaluate the effects of international scenario, type of sire, number of selected sires, selection criterion, herd size and planning horizon. Response variables were profit, lower 95% confidence limit for profit, and utility. Responses for selection of sires on profit index at year 10 or 20 were similar and superior to responses from selecting on the basis of milk-fat-protein indexes. Less favorable economic conditions within an international scenario, use of a 10-year planning horizon, or selection in scenarios with lower absolute responses favor the optimum of using inexpensive randomly chosen young sires. Under a scenario of average economic conditions, use of selected proven and young sires was generally superior to use of randomly chosen young sires for profit and lower 95% confidence limit for profit at 20-year planning horizon in all combinations, but was inferior for utility at year 10. Effect of herd size was generally small. A second study investigated optimum conditions for maximizing genetic response in nucleus herds of dairy cattle using multiple ovulation and embryo transfer (MOET), in vitro embryo production (IVEP) and sperm sexing technologies. Deterministic modeling was used to compare 15-year selection responses in nucleus herds with different combinations of herd size, number of sires used by year, female to male ratio and number of progeny per donor female. Adult nucleus herds of 655 to 2300 first lactation cows had rates of genetic gain adjusted for inbreeding depression comparable to those of an efficient progeny testing program. Effects of semen sexing were comparatively small.
Subject Area
Livestock|Genetics
Recommended Citation
Montaldo, Hugo Horacio, "Optimization of selection response using artificial insemination and new reproductive technologies in dairy cattle" (1997). ETD collection for University of Nebraska-Lincoln. AAI9725130.
https://digitalcommons.unl.edu/dissertations/AAI9725130