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Title: Prediction of preweaning ADG in beef calves from milk fatty acid methyl esters

item Brown, Michael
item Coleman, Samuel

Submitted to: Journal of Animal Science
Publication Type: Abstract Only
Publication Acceptance Date: 4/29/2012
Publication Date: 7/9/2012
Citation: Brown, M.A., Coleman, S.W. 2012. Prediction of preweaning ADG in beef calves from milk fatty acid methyl esters.[abstract] Journal of Animal Science. 90(3):518.

Interpretive Summary: Abstract only.

Technical Abstract: Research has shown milk yield (MWT) has an important influence on calf preweaning ADG (PRWADG), but MWT accounts for only a moderate amount of variation in PRWADG. The objective of this study was to determine if milk fatty acid methyl esters (FAME), alone and in combination with MWT, could improve accuracy of prediction of PRWADG. Forty-five beef cows sired by Bonsmara, Brangus, Charolais, Gelbvieh, Hereford and Romosinuano bulls were used in a 2 yr study. Spring-calving cows were milked 6 times per year every 28 d beginning late May, and milk samples were analyzed for milk fat and protein. Milk samples collected in May, July and September each year were 518 J. Anim. Sci. Vol. 90, Suppl. 3/J. Dairy Sci. Vol. 95, Suppl. 2 analyzed for FAME. Percentages of 42 FAME in each milk sample were acquired using a gas chromatograph flame ion detector. Milk weights, quality data, and FAME were averaged over collection dates before analyses. Stepwise regression was used to identify linear models to predict PRWADG using MWT, age of dam (AOD), and percent FAME. The R2 and associated condition index (CI, an indicator of collinearity) were used in model evaluation. Condition indexes less than or close to 30 were considered to have low collinearity. Regression of PRWADG on MWT resulted in an R2 of 0.35 with a CI of 9.4 while inclusion of AOD gave an R2 of 0.4 and a CI of 21.6. A regression equation using 8 FAME accounted 54% of the variation in calf ADG with a CI of 33. When MWT and AOD were included with FAME as predictors, a prediction equation with 8 FAME, MWT, and AOD accounted for 69% of the variation in PRWADG with a CI of 29. Partial least squares regression (PLS) was also used to predict PRWADG from FAME, MWT, and AOD. Results from PLS analyses yielded a dependent variable R2 of 0.61 using all 42 FAME with 7 extracted factors and a dependent variable R2 of 0.78 when MWT and AOD were included with the 42 FAME with 7 extracted factors. Results from these preliminary analyses suggest that FAME composition of milk influences calf ADG and that data on percent FAME in combination with MWT and AOD can improve the accuracy of prediction of calf PRWADG compared with MWT and AOD alone.