Technical note: Evaluation of bimodal distribution models to determine meal criterion in heifers fed a high-grain diet

Meals are clusters of feedbunk visit (BV) events that are differentiated from the next meal by a nonfeeding interval that is longer compared with the nonfeeding intervals within a meal. The longest nonfeeding interval considered to be part of a meal is defi ned as the meal criterion. The objective of this study was to determine which combination of 2 probability density functions ((PDF): Gaussian normal (G), Weibull (W), Log-Normal, Gamma, and Gumbel) used in a bimodal distribution model had the best fi t of nonfeeding interval data collected in beef heifers. Feeding behavior traits (572,627 total BV events) were measured in 119 heifers fed a high-grain diet (3.08 Mcal ME/kg DM), using a GrowSafe system for 66 d. The frequency and duration of BV events averaged 75 ± 15 events/d and 73.0 ± 22.3 min/d, respectively. The bimodal PDF combinations were fi tted to the log 10 -transformed interval lengths between BV events for each animal, using R mixdist package (2.13). The Akaike Information Criterion (AIC) was used to assess goodness of fi t of the 25 bimodal PDF combinations. The PDF model with the least AIC value was selected as the best fi t for each individual. A χ 2 analysis of the selected best PDF distribution across individuals revealed that 78.2% of the heifers best fi t were G-W or W-W PDF models. The likelihood probability estimates were calculated from the average AIC deviation of each model from the standard G-G model. The G-W likelihood probability estimate was greater (P = 0.001) than the W-W combination (0.997 vs. 0.727). Our analysis indicated the G-W model had a statistically better fi t and is most likely the best approach to defi ne meal criterion in beef heifers fed high-grain diets.