P0926 Prediction of Residual Feed Intake for First Lactation Dairy Cattle

Ghader Manafiazar , University of Alberta, Edmonton, AB, Canada
Thomas McFadden , University of Alberta, Edmonton, AB, Canada
Erasmus Okine , University of Alberta, Edmonton, AB, Canada
Laki Goonewardene , University of Alberta, Edmonton, AB, Canada
Zhiquan Wang , University of Alberta, Edmonton, AB, Canada
Developing an appropriate model to predict expected energy intake, while accounting for multifunctional requirements that are non-linearly related, is the key to successful prediction of residual feed intake (RFI) in dairy cattle. Individual daily energy intake (DEI) and monthly body weight of 172 first lactation dairy cows from 1 to 305 days-in-milk were recorded at the UAlberta Dairy Research and Technology Centre; individual milk yields and compositions were obtained from the Dairy Herd Improvement Program. Combinations of different orders (1-5) of fixed (F) and random (R) Legendre-polynomial regression models were fitted to model the non-linearity trend of metabolic body weight (MBW), empty body weight (EBW) and milk production energy requirements (MPER) over 305 days. The F5R3, F5R2 and F5R2 (subscripts indicate in the order fitted) models were selected, based on the log likelihood ratio test and the Bayesian information criteria, as the best prediction equations for MBW, EBW and MPER, respectively. The total 305-day DEI was then linearly regressed on total 305-day predicted traits of MPER, MBW and EBW to obtain the RFI over 305 days (R2=0.81). The daily RFI averaged 0 and ranged from -4.28 to 5.54 MJd-1. 54% of the animals had RFI value less than the mean (efficient) and 46% of them had RFI value greater than the mean (inefficient) These results indicate that the first lactation RFI is predictable and can be used in dairy industry to increase profitability by selecting animals that are genetically superior in energy efficiency based on RFI through marker-assisted selection or indicator traits.