Examination of Methods for Estimating Rate of Passage in Grazing Steers

Abstract

Understanding how rate of passage estimates are influenced by procedural variations may facilitate standardization of methodology and enhance comparisons among studies. Therefore, 12 ruminal-fistulated beef steers (mean wt. = 294 kg) were used in two 6-day grazing trials to evaluate influences of sampling site, intraruminal mixing, and mathematical model on particle passage rate estimates. Steers grazed a 13-ha pasture of immature crested wheatgrass. We estimated particle passage rate from the rumen by intraruminal administration of a pulse dose of Yb-labeled forage followed by serial collection of ruminal digesta or fecal samples. Treatments were (1) rectal sampling; (2) ruminal sampling-ruminal contents mixed before subsampling digesta; (3) ruminal sampling-ruminal contents not mixed before subsampling digesta. All steers were fitted with vibracorders to monitor grazing time before and during sampling periods. Fecal Yb curves were fitted with a one compartment, time-dependent (ICMPT-TD), a two-compartment, sequential time-dependent-time-independent (2CMPT-TD), and a two-compartment, time-independent (2CMPT-TI) model. All ruminal Yb curves were fitted with a single exponential decay model. Comparisons among models were limited to rate constants associated with the slower escape process. Intraruminal mixing did not alter (P>0.10) passage rates. The 2CMPT-TD model failed to fit some fecal profiles. Particle passage rates from the 2CMPT-TI model were greater (P<0.05) than those from the ICMPT-TD model. Similarity among passage rate constants derived from fecal Yb curves and those derived by semilogarithmic regression of ruminal Yb concentration on time depended on the model used to fit fecal Yb curves. Grazing time decreased (P<0.01) during intensive sampling periods. We conclude that for steers grazing immature grass pastures, intraruminal mixing before subsampling does not significantly alter rate of passage estimates; however, site of sampling and mathematical model may be important factors to consider in choosing appropriate methodology for estimating rate of passage.