title: Prediction of residual feed intake for first-lactation dairy cows using orthogonal polynomial random regression.
abstract: Residual Feed Intake (RFI) is a measure of energy efficiency. Developing an appropriate model to predict expected energy intake while accounting for multifunctional energy requirements of metabolic body weight (MBW), empty body weight (EBW), milk production energy requirements (MPER), and their nonlinear lactation profiles, is the key to successful prediction of RFI in dairy cattle. Individual daily actual energy intake and monthly body weight of 281 first-lactation dairy cows from 1 to 305 d in milk were recorded at the Dairy Research and Technology Centre of the University of Alberta (Edmonton, AB, Canada); individual monthly milk yield and compositions were obtained from the Dairy Herd Improvement Program. Combinations of different orders (1-5) of fixed (F) and random (R) factors were fitted using Legendre polynomial regression to model the nonlinear lactation profiles of MBW, EBW, and MPER over 301 d. The F5R3, F5R3, and F5R2 (subscripts indicate the order fitted) models were selected, based on the combination of the log-likelihood ratio test and the Bayesian information criterion, as the best prediction equations for MBW, EBW, and MPER, respectively. The selected models were used to predict daily individual values for these traits. To consider the body reserve changes, the differences of predicted EBW between 2 consecutive days were considered as the EBW change between these days. The smoothed total 301-d actual energy intake was then linearly regressed on the total 301-d predicted traits of MBW, EBW change, and MPER to obtain the first-lactation RFI (coefficient of determination=0.68). The mean of predicted daily average lactation RFI was 0 and ranged from -6.58 to 8.64 Mcal of NE(L)/d. Fifty-one percent of the animals had an RFI value below the mean (efficient) and 49% of them had an RFI value above the mean (inefficient). These results indicate that the first-lactation RFI can be predicted from its component traits with a reasonable coefficient of determination. The predicted RFI could be used in the dairy breeding program to increase profitability by selecting animals that are genetically superior in energy efficiency based on RFI, or through routinely measured traits, which are genetically correlated with RFI.
authors.affiliation: Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, T6G2P5, Canada.
authors.forename: G
authors.initials: G
authors.lastname: Manafiazar
authors.forename: T
authors.initials: T
authors.lastname: McFadden
authors.forename: L
authors.initials: L
authors.lastname: Goonewardene
authors.forename: E
authors.initials: E
authors.lastname: Okine
authors.forename: J
authors.initials: J
authors.lastname: Basarab
authors.forename: P
authors.initials: P
authors.lastname: Li
authors.forename: Z
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001835:Body Weight / Q000502:physiology; D002170:Canada; D002417:Cattle / Q000502:physiology*; D004435:Eating / Q000502:physiology*; D002149:Energy Intake / Q000502:physiology*; D005260:Female; D007774:Lactation / Q000502:physiology*; D008892:Milk; D010641:Phenotype; D012044:Regression Analysis
keywords: dairy cattle; feed efficiency; random regression model; residual feed intake