Browsing by Author "Moyo, Mehluli."

Now showing 1 - 2 of 2

Effects of diet quality and time lapse after meal termination on rumen load, rate of passage and feeding behaviour.
(2016) Moyo, Mehluli.; Nsahlai, Ignatius Verla.
Ruminant utilisation of poor quality feeds is governed by rates of degradation and passage through the rumen. Firstly, the passage rate of feed material determines the degree of bypass nutrients and the efficiency of synthesis of microbial protein in the rumen, making modelling of passage rate important. Secondly, diurnal feeding behaviours are not normally used in predicting feed intake although their influences are vital in understanding the dynamics of intake. Lastly, critical to rumen kinetics studies lies in understanding the dynamics of rumen fill levels post meal termination. The objectives of the study were to: (1) develop digesta passage rate prediction models for climatically, nutritionally and genetically diverse classes of ruminant herbivores; (2) ascertain the effects of diet quality on diurnal feeding behaviour in sheep and goats; and (3) determine the influence of diet quality on passage rates and, the extent and trend of solid digesta disappearance after meal termination. Artificial neural networks were used to develop prediction models for liquid and solid passage rates. Studies that reported fractional passage rates, class and body mass of ruminants were included in the dataset. Factors that affect rates of passage included animal and feed factors. The database 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 different climatic regions. Observations were randomly divided into 2 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 using an independent dataset, these models attained 42 and 64% of precision in predicting passage rates for solid and liquid, respectively. The effects of tropical roughage and diet quality on dry matter intake, duration and number of daytime and night time eating bouts, idling sessions and ruminating activities in small ruminants were investigated. Roughage quality was improved by urea treatment of veld hay, while diet quality was improved by supplementing with lucerne hay, sunflower meal, lespedeza, fish meal and sunflower meal. Day-time (0600-1800 h) and night time (1800-0600 h) feeding behaviour activities of sheep and goats were recorded. Generally, roughage and diet quality, and time of the day had significant effects on time spent ruminating and eating. Intake rates (g/bout and g/min) were not affected by diet and roughage qualities. Generally, goats and sheep fed on roughage alone ruminate at night and eat more during the day but those fed roughage and supplemented with lucerne hay spent more time ruminating than eating. Time spent eating and ruminating had positive correlations to feed intake. Intake rates (g/min and g/bout) had strong positive correlations to intake, which were significant. Improvement of roughage quality increased solid passage rate but did not affect liquid digesta passage rates from the rumen. Roughage quality had no effects on wet and dry digesta load in the foregut and hindgut compartments, except on abomasum dry matter load. Time lapse post feeding had no effects on rumen digesta load in the foregut and hindgut compartments, except on the dry and wet digesta load in the omasum. Proportions of digesta load in the rumen decreased linearly up to 24 h post feeding termination regardless of roughage quality. There is a possibility that this trend shapes into an exponential “decay” curve after 24 h post feeding termination. In conclusion, this study developed more précised prediction models for solid and liquid passage rates for ruminants fed on a variety of diets and/or feeds from different climatic regions. Roughage intake was limited as a result of increased rumination time of low quality roughages. There is a potential of using feeding behaviour to predict passage rates and predict intake. Digesta passage rate prediction models should include all animals, including those in a negative energy balance. The rate of clearance of digesta after meal termination was significantly greater for sheep fed on the improved roughage quality (IRQ) compared to the poor roughage quality (PRQ).
Modelling of feeding behaviour, rumen load and the kinetics of digestion and passage of digesta in domestic and wild ruminants.
(2021) Moyo, Mehluli.; Nsahlai, Ignatius Verla.
Roughage intake is affected by a collection of factors that include feeding behaviour, and the weight of rumen digesta which is a function of digesta clearance from the rumen as governed by the rates of degradation and passage. Accurate prediction of intake depends on the ability to predict these factors. In literature, there are few models, if any, that can be used to estimate the weight of rumen digesta load, simulate feeding behaviour, predict passage rates of solid and liquid digesta, and degradability in the rumen of ruminants inhabiting environments with different diet qualities.The five main objectives of this study were to (1) investigate effects of diet and roughage quality on feeding behaviour; and to determine the main factors affecting and developing Random Forest models to estimate (2) time spent on diurnal feeding behaviours, (3) digestion of feeds in the rumen, (4) weight rumen digesta load and (5) rate of passage of digesta in the rumen. The effects of diet and roughage quality on dry matter intake, duration and number of daytime and night-time eating bouts, and ruminating activities in small ruminants were investigated. In Exp 1 and 2, roughage quality was improved by urea treatment of veld hay, while diet quality was improved by supplementing with Lucerne hay (Exp 3), sunflower meal and lespedeza (Exp 4), fish meal (Exp 5a), and sunflower meal (Exp 5b). Daytime (06:00 to 18:00 h) and night-time (18:00 to 06:00 h) feeding behaviour activities were recorded. Roughage quality affected rumination index in Exp 1, but not in Exp 2, 3, and 5. Time spent eating and ruminating was affected by roughage quality (Exp 1, 3, and 4), period of day (all experiments) and their interaction (Exp 1). Period of day affected the duration of rumination sessions (Exp 1, 2, and 3); diet quality or roughage quality affected the duration of eating bouts (Exp 3) and rumination sessions (Exp 1 and 2). roughage quality had a significant effect on the duration eating sessions in Exp 3 only, whilst period of day affected this same behaviour in Exp 2 and 3. To ascertain the influence of the period of the day, ambient temperature, climatic region, and ruminant feeding type on daytime and night-time feeding behaviour of ruminants a dataset was collected from studies that measured feeding behaviour. Studies that qualified for inclusion into the dataset should have (1) reported times spent eating (TSE), ruminating (TSR) and idling, number and duration of ruminating and eating sessions during a 12h day and 12h night period, and 24 h period (2) measured body weights of animals used, and (3) stated feeds or proportion of feeds in diets fed to or consumed by the animals. Diet properties, animal and environmental factors affecting feeding behaviour were identified in the studies. A mixed effects and regression models captured the influence and response to the period of the day, ambient temperature, climatic region and ruminant feeding type of feeding behaviour. During the day, time spent ruminating and chewing became longer in large ruminants than at night. Predictions showed that time spent eating during the day and at night are expected to decline with an increase in ambient temperature, while times spent ruminating during the day will increase. Grazers and intermediate feeders spent more time eating during the day than at night, while browsers spent more time eating at night than during the day. The influence on 24 h diurnal feeding behaviour patterns of ruminants in response to ambient temperature and ruminant feeding type were ascertained. Feeding behaviours scaled allometrically with body weight for all ruminant feeding types, except for TSE by browsers and intermediate feeders, and TSR by grazers. Times spent eating and TSR become shorter in large compared to small ruminants. Time spent ruminating became shorter in large browsers, while large intermediate feeders spent more TSR than their smaller counterparts. Browsers had less TSE, highest DEB and lowest number of eating bouts compared to grazers and intermediate feeders. Trends from this study showed that TSE, DEB, and idling are projected to increase with ambient temperature, while TSR is likely to decrease. Models to predict TSE and TSR for grazing and browsing ruminants were developed. A dataset was created from studies that reported TSE and TSR, number of eating (NEB) and ruminating bouts (NRB), and the duration of ruminating (DRB) and eating bouts (DEB) over a 24h period. Factors affecting feeding behaviour were identified from each study and grouped into (1) diet properties, (2) animal and (3) environmental factors. These factors were used as input variables for the prediction of feeding behaviour. The dataset was randomly divided into two subsets: 70% for model training and 30% for model testing. Developed models accounted for 95% (TSE), 90% (TSR), 93% (DEB), 93% (DRB), 78% (NEB) and 90% (NRB) of the variation in prediction of feeding behaviour. The models attained 87% (TSE), 62% (TSR), 93% (DEB), 83% (DRB), 82% (NEB) and 77% (NRB) precision in prediction during testing using an independent dataset. This study developed good simulation models for feeding behaviour of ruminants. The consequences of increases in ambient temperature and effect of climate type on digestibility of forages by ruminants using meta-analysis in relation to global warming were evaluated. A dataset on nylon bag degradability parameters bearing the chemical composition of roughages, grains, leaves, stems, fruits, concentrates, and diets given to animals, climate type, and ambient temperature were compiled. Data were analysed using mixed model regression and simple linear regression methodologies. Negative correlations between ambient temperature and degradability parameters were observed. Potential degradability was highest for studies carried out in cold and temperate climates compared to tropical and arid climates. A 1 °C increase in ambient temperature decreased PD by 0.39% (roughages), 0.76% (concentrates), and 2.41% (mixed diets), with an overall decrease of 0.55% for all feed types. The “b” fraction decreased by 0.1% (roughages), 1.1% (concentrates), 2.27% (mixed diets), and 0.35% (all feed types) for every 1 °C increase in ambient temperature. Increasing ambient temperature by 1 °C increased the neutral detergent fibre content of feeds by 0.4%. A test of slopes showed that the predicted decrease in rumen digestibility of feeds with ambient temperature would be most severe in tropical and arid regions compared to cold and temperate regions. An evaluation and prediction of the nutritive and feeding value of underutilised forages that have a potential of being ruminant feeds was done. Underutilised forage legumes, leaves/trigs of forage trees and shrubs (non-leguminous), commonly used grass forages and concentrates were collected from various regions. The nylon bag method was used to determine degradability of the underutilised forage legumes, leaves/trigs of forage trees and shrubs (nonleguminous) in the rumen. A step-wise regression procedure was used to develop regression equations to predict degradability of forages in the rumen. Of the underutilised forages, the crude protein content tended to be double for Brassica oleracea var. acephala compared to Colophospermum mopane leaves and pods. Forage grasses (62.9±34 g/kgDM) tended to have very low crude protein contents compared to legumes (137.6±69 g/kgDM) and concentrates (177±39.9 g/kgDM). Underutilised Brassica oleracea var. acephala (305 g/kgDM) tended to have higher crude protein levels compared to commonly used protein sources (cotton seed cake = 222 g/kgDM). The regression model for predicting the soluble fraction accounted for 59% and 71% of the variation in model development and validation of predictions, respectively. The regression model for predicting the potential degradability accounted for 65% and 24% of the variation in model development and validation, respectively. A dataset to enable prediction of degradation parameters in the rumen were collected from studies that (1) reported values for in-sacco degradability parameters viz. soluble fraction (a), slowly degradable fraction (b), potential degradability (PD) and rate of degradation (c) of roughages, grains, leaves, stems, fruits and concentrate formulations, and (2) stated the diets given to animals fed at ad-libitum. Two datasets were collated, one on studies that used the time-lag model and another on studies that used the no-time lag model in computing degradation parameters. Factors that affect degradability were identified in each of these studies and categorised into (i) diet properties (ii) feed sample properties (iii) ruminant feeding type and (iv) environmental factors. These factors were used as input variables to enable prediction of degradability. Each dataset was randomly divided into two subsets: 70% for training and 30% for testing. The no time-lag models attained 88% (“a”), 93% (“b”), 76% (“c”) and 90% (“PD”) precision in prediction during training and 58% (“a”), 52% (“b”), 48% (“c”) and 53% (“PD”) precision in testing. Time lag models accounted for 91% (“a”), 84% (“b”), 79% (“c”), 91% (“PD”) and 87% (lag) of the variation in prediction during training and 64% (“a”), 57% (“b”), 29% (“c”), 52% (“PD”) and 59% (lag) precision in testing. Both sets of models predicted “a”, “b”, PD, and lag with appreciable precision, but models for the prediction of the rate of degradation require improvement. 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 was examined. 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. Factors affecting the rate of passage were identified from each study and grouped into (i) diet properties, (ii) animal, (iii) feed particle properties and (iv) environmental factors. Animal and feed factors that affect the rate of passage were identified in studies and used as input variables to estimate rate of passage in the rumen. The database was composed of observations of domestic and wild ruminants of variable body mass (1.5 to 1238 kg) from 74 (solid using predicted liquid passage rate) and 31 (solid using observed liquid passage rate) studies. Observations were randomly divided into 2 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 (+7%) compared to independent predictions of solid passage rate. Scaling relationships of rumen digesta load with body weight and the influence on ruminant digesta load in response to climatic region and ruminant feeding type were evaluated. A dataset on rumen digesta load (RDL) parameters bearing body weights of ruminants, proximate chemical composition of feeds and diets fed to or eaten by ruminants and climate type was created. Data were analysed using a linear regression and mixed model regression methodology. Grazers and intermediate feeders had hypoallometric scales of RDL with BW, while the scale was hyperallometric for browsers. Wet and liquid RDL of grazers and browsers scaled isometrically with BW. Intercepts of scaling relationships of RDL and BW were highest for intermediate feeders and lowest for browsers. For all RDL, body mass and animal production were both influential covariates. Ruminant species and ruminant feeding type (p<0.05) influenced all measures of RDL and was highest in grazers and lowest in browsers. The response of RDL to increases in ambient temperature where more linear than they were quadratic. Liquid and dry rumen digesta load were predicted to decrease in proportion by 0.02 (p<0.0001) for every 1°C increase in ambient temperature. Models to estimate the weight of rumen digesta in ruminants were developed. A dataset was created from studies that (1) measured either the rumen dry matter load (RDML), rumen wet matter load (RWML) or rumen liquid matter load (RLML) by complete evacuation of the rumen through fistulas or after slaughtering, (2) reported body weights of animals and (3) stated the diets fed to or eaten by the animals. Factors affecting rumen digesta load were identified from each study and included animal (ruminant feeding type, body weight, degree of maturity, animal production level, days in lactation and pregnancy), diet composition (dry matter, neutral detergent fibre, crude protein, starch and ash content), management (grazing or fed-indoors) and environmental (climate type and ambient temperature) factors. These factors were used as input variables in predicting rumen digesta load. The dataset was divided into 2 subsets: 70% for model training and 30% for testing. The models accounted for 81% (scaled RDML) and 90% (unscaled RDML) of the variation in prediction of RDML. On testing, the models attained 59% (scaled RDML) and 84% (unscaled RDML) precision in prediction. Models attained high precision in prediction of RWML (R2 = 0.94) and RLML (R2 = 0.94) during training and testing of RWML (R2 = 0.85) and RLML (R2 = 0.88) using an independent dataset. In conclusion, the models gave good predictions of the weight of rumen digesta load. However, there is a need to correct for the effect of time delay from the point when feeding stops till when rumen digesta load is measured; this is quite cardinal in regressing in time to the exact rumen digesta load when the animal stopped eating. In summary, results from this study showed that increases in ambient temperature will decrease rumen digestibility of forages and these will be more pronounced in arid regions. Small-sized ruminants adapted their feeding behaviour and rumen digesta load better than large-size ruminants. This implies that local breeds which are generally small in size can be better utilised to mitigate climate change by farmers in arid regions. High accuracy in prediction of feeding behaviour, rumen degradability, passage rate of digesta in the rumen and rumen digesta load would enable better prediction of dry matter intake by ruminants.