Three metrics successfully assess intake variance in feedlot cattle

Methods to analyze variation in dry matter intake over time are evaluated.

December 5, 2023

2 Min Read
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Many are interested in boosting the performance and metabolic health of feedlot cattle. It is often thought that minimizing variation in dry matter intake (DMI) might decrease incidence of metabolic disorders such as acidosis and bloat, which has led to management strategies such as slick bunk management. To determine whether DMI does, in fact, affect the metabolic health of feedlot cattle, scientists from Texas Tech University set out to evaluate methods of assessing variation in DMI. The results of their research are presented in a new article in Applied Animal Science.

"This research article describes evaluation of three methods to statistically assess variation in feed intake of feedlot cattle over the feeding period," said David Beede, PhD, editor in chief of the journal.

The three methods studied were described by lead author Michael Galyean, PhD, with the Department of Veterinary Sciences at Texas Tech University.

"Approaches to assess DMI variation included (1) the sum of daily Euclidean distance between DMI values; (2) the average of the absolute daily deviations in DMI; and (3) repeated measures analysis of DMI over days on feed to estimate variance and covariance," Dr. Galyean explained. The scientists applied the metrics to simulated data and data from a previously published experiment.

Using the simulated data sets, the researchers were able to study two different patterns of intake variation. They created one data set in which the mean DMI did not differ among pens, but the variance (or standard deviation) differed. In the other data set, the standard deviation or coefficient of variation stayed the same, but the mean DMI varied. Analysis of DMI data from the previously published experiment allowed them to investigate the effects of bunk management and bulk density.

"This study included feed bunk management treatments that were designed to affect DMI variation," said Dr. Galyean.

The scientists' statistical analyses of the simulated and published data are detailed in the article. They found that all three of the evaluated approaches could detect differences in DMI variation. They also provide considerations and tips on choosing the best metric to use and how to use the methods optimally. Giving an overall evaluation, Dr. Galyean stated, "Our results suggest that all three 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."

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