Evaluation of methods to assess variation in dry matter intake over time in feedlot cattle

Objective

Variation in DMI over time could be associated with performance and metabolic disorders in feedlot cattle, but methods of measuring DMI variation have not been adequately defined. Our objective was to evaluate methods of assessing DMI variation using simulated data, as well as data from a published feedlot experiment.

Materials and Methods

Two data sets were created to simulate DMI by pens of cattle over 100 d: one with the same mean DMI (9 kg/d) and SD that varied from 0.125 to 0.5 kg/d and the other with different mean DMI (9, 10, and 11 kg/d) and the same SD (0.25 kg) or CV (2.78%). Approaches to assess DMI variation included (1) the sum of daily Euclidean distance between DMI values (DIST); (2) the average of the absolute daily deviations in DMI (DEV); and (3) repeated measures analysis of DMI over days on feed to estimate variance and covariance. The DIST and DEV metrics were analyzed by ANOVA, with model residuals tested for normality. Treatments in the published feedlot experiment included management for ad libitum intake versus slick bunk management, factored with different bulk densities of steam-flaked corn (335 vs. 425 g/L).

Results and Discussion

All 3 methods identified differences (P ≤ 0.05) among SD groups with the same mean, whereas with different mean DMI that had the same SD, no differences (P ≥ 0.80) in DMI variation were observed. When the CV was held constant among the different mean DMI, all 3 methods identified differences (P≤ 0.05) between the greatest and least CV values. For the published data, all 3 methods detected an effect of bunk management on DMI variance (P ≤ 0.05), with greater variance for ad libitum versus slick bunk management.

Only repeated measures detected an effect of bulk density on DMI variance, with greater variance (P ≤ 0.05) for the 425 g/L treatment.

Implications and Applications

Results suggest that all 3 methods can provide a tool for statistically assessing variation in DMI over time, potentially allowing for a greater understanding of how such variation affects growth performance and metabolic health of feedlot cattle.