Mehluli Moyo

By Mehluli Moyo and Ignatius V. Nsahlai

Fluid passage rates through the rumen influence digestion of soluble food nutrients, amount of short-chain fatty acids absorbed in the rumen and that pass out of the rumen, the amount of by-pass protein of dietary origin and the amount of microbial protein available to the host as a protein source, making modelling of passage imperative. Current research on passage rate should seek to incorporate various factors that affect rumen fill, and solid and liquid passage rates to develop intake and passage rate prediction models. The aim of this paper was to discuss factors that affect rates of passage of digesta and rumen digesta load. Ambient temperature, animal physiological status and reproductive status, fermentation and diet quality are major factors affecting digesta passage rates. The animal physiology also influences digesta passage rate. Computation of animal production level to account for all the physiological processes that affect passage rate is vital. Discrepancies on how ambient temperature and particle density (buoyancy) affect the passage rate of digesta in the rumen may cause uncertainty in calibration of temperature and buoyancy in prediction models. Corrected for diet properties, goats have similar passage rates to other ruminants.

Part of the book: Goat Science

By Mehluli Moyo, Siyabonga T. Bhiya, Masande Katamzi and Ignatius V. Nsahlai

The aim of the chapter was to evaluate and predict the nutritive and feeding value of unknown and underutilised forages. Underutilised forages were collected from various regions. Chemical composition and degradability of forages in the rumen were determined. A dataset was created bearing degradability parameters of feeds from 40 studies. Using the dataset, a step-wise regression procedure was used to develop regression equations to predict rumen degradability. Of the underutilised forages, crude protein content tended to be double for Brassica oleracea var. acephala compared to Colophospermum mopane leaves and pods. Forage grasses tended to have very low crude protein contents compared to legumes and concentrates. Underutilised Brassica oleracea var. acephala tended to have higher crude protein levels compared to commonly used protein sources. The regression model for predicting the soluble fraction accounted for 59% (development) and 71% (validation) of the variation. The regression model for predicting the potential degradability accounted for 65% (development) and 24% (validation) of the variation. In conclusion, the nutritive value of underutilised forages was good, high in crude protein and high potential degradability. After correcting for factors that significantly affected degradability parameters, predicted solubility and effective degradability lay near the ideal prediction line, giving good predictions.

Part of the book: Forage Groups