Prediction of solid digesta passage rate using liquid passage rate as one of the input variables in ruminants

Abstract

This study ascertained the influence of liquid passage rates on solid digesta passage rates and the possibilities of simultaneous prediction of solid and liquid passage rates in ruminants. Artificial neural networks were used to develop models of solid and solid-plus-liquid passage rates. Studies that reported fractional passage rates, class and body mass of ruminants were included in the dataset. Animal and feed factors that affect the rate of passage were identified. The database had observations of domestic and wild ruminants of variable body mass from 74 (solid using predicted liquid passage rate) and 31 (solid using observed liquid passage rate) studies. Observations were randomly divided into two data subsets: 75% for training and 25% for validation. Developed models accounted for 76% and 77% of the variation in prediction of solid passage rates using predicted and observed liquid passage rate as inputs, respectively. Simultaneous prediction accounted for 83% and 89% of the variation of solid and liquid passage rates, respectively. On validation using an independent dataset, these models attained 45% (solid using predicted liquid), 66% (solid using observed liquid), 50% (solid predicted with liquid) and 69% (liquid predicted with solid) of precision in predicting passage rates. Simultaneous prediction of solid and liquid passage rate yielded better predictions compared with independent predictions of solid passage rate. Simultaneous prediction of solid and liquid passage rates accounted for more variation compared with independent predictions of solid rates. Inclusion of liquid passage rate as an input variable gave better predictions of solid passage rates.

Keywords: Fractional passage rate, prediction model, simultaneous predictions