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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #325946
Title: Stochastic dynamic simulation modeling including multitrait genetics to estimate genetic, technical, and financial consequences of dairy farm reproduction and selection strategies

Author
item KANIYAMATTAM, KARUN - University Of Florida
item ELZO, MAURICIO - University Of Florida
item Cole, John
item DE VRIES, ALBERT - University Of Florida

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/20/2016
Publication Date: 10/1/2016
Citation: Kaniyamattam, K., Elzo, M.A., Cole, J.B., De Vries, A. 2016. Stochastic dynamic simulation modeling including multitrait genetics to estimate genetic, technical, and financial consequences of dairy farm reproduction and selection strategies. Journal of Dairy Science. 99(10):8187-8202.

Interpretive Summary: Twelve correlated genetic traits were included in a stochastic dynamic model which simulates a herd of dairy cattle on a daily basis. The herd was simulated for 15 yr to study the genetic, technical and financial performances of 13 different reproduction and selection scenarios. Genetic selection occurred by selling the genetically worst heifers and inseminating the best heifers when animals were ranked for different traits. We concluded that excluding some or all multiple correlated genetic traits resulted in biased estimates of genetic, technical and financial performance over time.

Technical Abstract: The objective of this study was to develop a daily stochastic dynamic dairy simulation model which included multi-trait genetics, and to evaluate the effects of various reproduction and selection strategies on the genetic, technical and financial performance of a dairy herd. The 12 correlated genetic traits included in 2014 Net Merit (NM$) index were modeled for each individual animal in the model. A true breeding value (TBV) for each trait was calculated as the average of the sire’s and dam’s TBV, plus a fraction of the inbreeding and Mendelian sampling. Similarly, an environmental component for each trait was calculated and was partitioned into a permanent and a daily temporary effect. The combined effect of TBV and the environmental components were converted into an effect on the phenotypic performance of each animal for 8 traits. Hence, genetics and phenotypic performance were associated. Estimated breeding values (EBV) were also calculated. Genetic trend in the service sire was assumed given. Surplus heifers were culled to maintain a herd size of 1000 milking cows. In the first set of 8 scenarios, culling of surplus heifers was based on a random criteria or on EBV of NM$. Four different genetic architectures depending on the presence or absence of genetic trends or genetic correlations in all the 12 traits of interest were evaluated to measure the impact of multi-trait genetics. In the last 5 scenarios with the full model, culling of surplus heifers was based on random criteria, EBV of NM$, or EBV of milk. Sexed semen use and reliability of the EBV were varied. Each scenario was simulated for 15 yr into the future. Results showed that models without a full genetic architecture of genetic trends and correlations in all the 12 traits provided biased estimates of the genetic, technical and financial performance of the dairy herds. A gain of $263 in average TBV of NM$ was observed in year 15 as a result of combining sexed semen and culling of surplus heifers based on the lowest EBV of NM$, compared to a strategy which only used conventional semen and culled the surplus heifers randomly. A gain of 1.25 percentage units was obtained in the average TBV of daughter pregnancy rate in year 15 by using sexed semen in heifers as well selling surplus heifers ranked by EBV of NM$ when compared to a scenario using conventional semen only as well as selling surplus heifers ranked by EBV of milk. In conclusion, the multi-trait genetics model resulted in improved estimates of genetic, technical and financial effects of dairy farm reproduction and selection strategies.