Development and evaluation of empirical equations to predict feed passage rate in cattle
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Empirical equations were developed to accurately predict passage rate (Kp) in ration formulation models for all classes of dairy and beef cattle. The database was comprised of studies that used external markers, and 553, 195 and 766 treatment means were used to develop the Kp equations for forages, concentrates and liquid, respectively. A random coefficients model that used each study effect as a random variable was used to select statistically significant input variables to predict rate of passage. The parameters of the variables were estimated using ordinary least square method. The equations developed were: Kp forage = (2.365 + 0.0214FpBW + 0.0734CpBW + 0.069FDMI)/100; Kp concentrate = (1.169 + 0.1375FpBW + 0.1721CpBW)/100 and Kp liquid = (4.524 + 0.0223FpBW + 0.2046CpBW + 0.344FDMI)/100, where Kp is the passage rate, h-1; FpBW the forage DMI as a proportion of BW, g/kg; CpBW the concentrate DMI as a proportion of BW and FDMI is the forage DMI, kg. These Kp equations for forages, concentrates and liquid explained 87%, 95% and 94%, respectively of the variation in passage rates in the database used in equation development after adjustment for random study effect. These and other published equations were evaluated with an independent database. In this evaluation, the R2 of the new equations were 0.39, 0.40 and 0.25 for prediction of the passage of forages, concentrates and liquid, respectively, which was higher than the R2 of the previously published equations by 0.03-0.19, 0.01-0.14, and 0.04-0.16 for forages, concentrates and liquid, respectively. The root mean square prediction error (RMSPE) was reduced by 3-22%, 2-33%, and 4-31% in the prediction of Kp of forages, concentrates and liquid, respectively, with the new equations. These new empirical equations are suitable for predicting passage rate in cattle, but predictability overall is still low and increases in the accuracy of predicting passage rates requires development of a mechanistic model that accounts for more biologically important variables affecting passage rate (e.g. physical property of particles, water intake and flux, and within day variation in intake). 2005 Elsevier B.V. All rights reserved.
