Nonlinear equations were compared with categori-cal analysis to account for DIM effects on milk production. Five different models for lactation curves were evaluated. Derived from a multiphasic lactation curve, the selected lactation curve appeared to result in random residuals and performed more consistently than the multiphasic curve. Residuals from the fitting of lactation curves were then used for split-plot analy-sis (continuous model) to estimate treatment effects. Statistical performance of this model was compared with split-plot analysis based on a discrete model with regularly spaced intervals to account for DIM effects (discrete model). The fitting of lactation curves accounted for herd, lactation number, and interaction effects of herd and lactation number and accounted for 34.1 and 44.3 % of variance among cows for primiparous and mul-tiparous cows, respectively. The continuous model de-tected interactions of genetic and management factors with treatment of multiparous cows that were not detected by the discrete model. No statistically significant differences were de-tected between the two modeling approaches. The continuous model appeared to violate fewer assump-tions regarding data distribution than did the discrete model, which reduced the risk of introducing bias during the estimation of treatment effects. The con-tinuous model seemed to be more sensitive to subtle interactions of treatment and other factors. ( Key words: lactation curves, data analysis, milk production) Abbreviation key: BCSC = body condition score at calving, IG = incomplete gamma (Wood’s) lactation curve model, IP = inverse polynomial lactation curve model, LCSP = lactation curve fitted prior to split
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.