Seasonality of fibrolytic enzyme activity in herbivore microbial ecosystems

Fibre (cellulose, hemicellulose and lignin) is the most abundant polysaccharide in nature and is hydrolysed by gut micro-organisms of herbivores because they can produce a set of extracellular enzymes. This study examined seasonal changes in the fibrolytic enzyme activity of microbial ecosystems of five herbivores (buffalo, cow, impala, wildebeest and zebra). Crude protein extracts obtained from the aforestated ecosystems were assayed for exocellulase, endocellulase, cellobiase and xylanase by incubating with crystalline cellulose, carboxymethylcellulose, p-nitrophenyl ß-1, 4-D-gulcopyranoside and xylan at optimum pH (5.5 to 6.5) for 1, 2, and 48 h, respectively. The specific activities (μg reducing sugar/mg crude protein) of all enzymes varied (p<0.001) among ecosystems and between seasons. Generally, the exocellulase specific activities in all ecosystems increased from summer to winter whilst the specific activities of endocellulase and xylanase decreased. The cellobiase activity decreased for buffalo and impala but increased for the others. It is only the zebra that showed the most superiority to the cow for all enzyme systems. These results suggest that in vitro digestion of fibre would depend on the season the ecosystem is collected and the source of the ecosystem. Microbial ecosystem from the zebra is one with the highest activity that could benefit the ruminant production system.Keywords: Seasonality, herbivores, microbial ecosystems, enzyme

Classification of low-resource livestock producers in the North West Province

(South African J of Animal Science, 2000, 30, Supplement 1: 109-110

Rate of Passage of Digesta in Ruminants; Are Goats Different?

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

The potential of legume pods as supplements to low quality roughages

(South African J of Animal Science, 2000, 30, Supplement 1: 107-108

Reproductive indices of Merino rams fed sun-cured Leucaena leucocephala forage

(South African J of Animal Science, 2000, 30, Supplement 1: 111-112

Feed evaluation

This manual has been produced to standardise some analytical procedures at ILCA and among those NARS scientists who may be interested in the aspects of feed evaluation which the manual attempts to address. The first part of the manual is on determination of voluntary intake, digestion and retention coefficients. It lists feed-intake measurement and collection of faeces and urine and presents procedures on preparing samples for chemical analysis. The second part examines special methods for measuring digestibility. This includes the indicator method, the nylon-bag technique, the nylon-bag procedure, handling nylon-bag data, and the Menke in vitro gas-production technique. The third part summarises the kinetics of digestion and of passage. Flow rates, rumen-evacuation technique, the use of markers to estimate passage rates, and continuous dosing with chromium-mordanted straw are discussed in this part. The fourth part is on the estimation of microbial protein supply using total urine excretion of purine derivatives. This includes sample preparation and mathematical procedure

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

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 prediction

Ruminal degradability and intestinal digestion of eight plant protein supplements used in ruminant diets

(South African J of Animal Science, 2000, 30, Supplement 1: 51-52

Modelling of digesta passage rates in grazing and browsing domestic and wild ruminant herbivores

Ruminant utilization of poor-quality feeds is governed by rates of digestion and of passage through the rumen. The passage rate of feed material determines the quantity of bypass nutrients and the efficiency of synthesis of microbial protein in the rumen, making modelling of passage rate important. Artificial neural networks were used to develop models of liquid and solid passage rates. Studies that reported fractional passage rates, along with class and body mass of ruminants, were included in the dataset. Factors that affect rates of passage in all the studies were identified, which included animal and feed factors. The dataset was composed of observations of domestic and wild ruminants of variable body mass (1.5 to 1238 kg) from 74 studies and 17 ruminant species from various climatic regions. Observations were randomly divided into two data subsets: 75% for training and 25% for validation. Developed models accounted for 66 and 82% of the variation in prediction of passage rates for solid and liquid, respectively. On validation with an independent dataset, these models attained 42 and 64% of precision in predicting passage rates for solid and liquid, respectively. Liquid and solid prediction passage rate models had no linear and mean bias in prediction. This study developed better prediction models for solid and liquid passage rates for ruminants fed on a variety of diets and/or feeds from different climatic regions.Keywords: Artificial neural networks, intake, mean retention time, prediction equation, rume