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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 #304381

Research Project: Understanding Genetic and Physiological Factors Affecting Nutrient Use Efficiency of Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: An alternative approach to modeling genetic merit of feed efficiency in dairy cattle

Author
item Lu, Y.f - Michigan State University
item Vandehaar, M.j. - Michigan State University
item Weigel, K.a. - University Of Wisconsin
item Armentano, L.e. - University Of Wisconsin
item Spurlock, D.m. - Iowa State University
item Staples, C - University Of Florida
item Connor, Erin
item Tempelman, R.j. - Michigan State University

Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: 4/3/2014
Publication Date: 8/17/2014
Citation: Spurlock, D.M., Tempelman, R.J., Weigel, K.A., Armentano, L.E., Wiggans, G.R., Veerkamp, R.F., de Haas, Y., Coffey, M.P., Connor, E.E. Hanigan, M.D., Staples, C.R., VandeHaar, M.J. 2014. Genetic architecture and biological basis of feed efficiency in dairy cattle. In: Proceedings of the 10th World Congress on Genetics Applied to Livestock Production, August 17-22, 2014, Vancouver, BC, Canada. Paper 287.

Interpretive Summary:

Technical Abstract: Genetic improvement of feed efficiency (FE) in dairy cattle requires greater attention given increasingly important resource constraint issues. A commonly used measure of FE in dairy cattle is residual feed intake (RFI); however, the use of RFI may be limiting for a number of reasons, including differences in recording frequencies between various component traits of RFI and potential differences in genetic versus non-genetic relationships between traits. We propose an alternative multiple-trait modeling strategy that exploits the Cholesky decomposition (CD) to provide a potentially more robust measure of FE. We assessed both approaches by simulation as well as by application to 23,770 mid-lactation weekly records on 1,967 cows from a dairy feed efficiency consortium study involving 7 different research stations within US. Although the CD model fared better than the RFI approach when simulated genetic and non-genetic associations between dry matter intake and component traits were substantially different from each other, there were no meaningful differences in predictive performance between the two models on application to the consortium data.