Predicting dry matter intake by growing and finishing beef cattle: Evaluation of current methods and equation development

Abstract

The NRC (1996) equation for predicting DMI by growing-finishing beef cattle, which is based on dietary NEm concentration and average BW0.75, has been reported to over- and underpredict DMI depending on dietary and animal conditions. Our objectives were to 1) develop broadly applicable equations for predicting DMI from BW and dietary NEm concentration and 2) evaluate the predictive value of using NE requirements and dietary NE concentrations to determine the DMI required (DMIR) by feedlot cattle. Two new DMI prediction equations were developed from a literature data set, which represented treatment means from published experiments from 1980 to 2011 that covered a wide range of dietary NEm concentrations. Dry matter intake predicted from the 2 new equations, which were based on NEm concentration and either the ending BW for a feeding period or the DMI per unit of average BW (End BW and DMI/BW, respectively), accounted for 61 and 58% of the variation in observed DMI, respectively, vs. 48% for the 1996 NRC equation. When validated with 4 independent data sets that included 7,751 pen and individual observations of DMI by animals of varying BW and feeding periods of varying length, DMI predicted by the 1996 NRC equation, the End BW and DMI/BW equations, and the DMIR method accounted for 13.1 to 82.9% of the variation in observed DMI, with higher r2 values for 2 feedlot pen data sets and lower values for pen and individual data sets that included animals on lower-energy, growing diets as well as those in feed-lot settings. The DMIR method yielded the greatest r2 values and least prediction errors across the 4 data sets; however, mean biases (P < 0.01) were evident for all the equations across the data sets, ranging from as high as 1.01 kg for the DMIR method to -1.03 kg for the 1996 NRC equation. Negative linear bias was evident in virtually all cases, suggesting that prediction errors changed as DMI increased. Despite the expanded literature database for equation development, other than a trend for lower standard errors of prediction with the DMI/BW equation, the 2 new equations did not offer major advantages over the 1996 NRC equation when applied to the validation data sets. Because the DMIR approach accounted for the greatest percentage of variation in observed DMI and had the least root mean square error values in all data sets evaluated, this approach should be considered as a means of predicting DMI by growing-finishing beef cattle. © 2014 American Society of Animal Science. All rights reserved.