Chewing time, number of eating and ruminating session, and duration of eating bouts are physiologically controlled in small ruminants, though chewing time requires isometric scaling during modelling of intake.

ObjectiveThis study ascertained effects of roughage quality, period of day at meal termination and time lapse after feeding on digesta load in the rumen.MethodsVeld hay was untreated (poor roughage quality, PRQ), improved (improved roughage quality, IRQ) by treating with urea or semi-improved by spraying with urea (semi-improved roughage quality, SIRQ). Experiment 1a used four rumen fistulated sheep to determine in-sacco degradability. Twelve sheep (56.3±4.59 kg) were blocked by weight and randomly allocated to IRQ (n = 6) and PRQ (n = 6) to determine solid and liquid passage rates. In experiment 1b, nine sheep (37.6±9.34 kg) were blocked by weight and randomly allocated to IRQ (n = 4) and PRQ (n = 5) to determine digestibility. Sixteen sheep (36.47±9.46 kg) were blocked by body weight and randomly allocated to IRQ (n = 8) and PRQ (n = 8). Two sheep were slaughtered for each sampling time in each treatment (IRQ and PRQ) at 0, 6, 12, and 24 h after feeding to determine rumen load. In experiment 2, eighteen goats (25.4±9.08 kg) were blocked by weight and randomly allocated to IRQ (n = 6), SIRQ (n = 6), and PRQ (n = 6). Then all 18 goats were slaughtered soon after meal termination in the morning; afternoon and evening to determine the effect of period of day on rumen fill.ResultsRate of degradation and effective degradability were enhanced by improvement of roughage quality. Roughage quality had no effect on digestibility, but digestibility was higher in goats than sheep. Fractional passage rate of particles was higher for IRQ than PRQ, but similar for liquids. Digesta fractional clearance rates at 24 h after feeding were 0.018/h (IRQ) and 0.006/h (PRQ). Period of day had an influence on rumen load. Neutral detergent fibre load for goats were above 2.03 kg/100 kg body weight for all diet treatments.ConclusionFollowing starvation, passage rate had negligible effects on emptying of rumen load.

Artificial Neural Network (ANN) and Random Forest models for predicting rumen fill of cattle and sheep were developed. Data on rumen fill were collected from studies that reported body weights, measured rumen fill and stated diets fed to animals. Animal and feed factors that affected rumen fill were identified from each study and used to create a dataset. These factors were used as input variables for predicting the weight of rumen fill. For ANN modelling, a three-layer Levenberg-Marquardt Back Propagation Neural Network was adopted and achieved 96% accuracy in prediction of the weight of rumen fill. The precision of the ANN model’s prediction of rumen fill was higher for cattle (80%) than sheep (56%). On validation, the ANN model achieved 95% accuracy in prediction of the weight of rumen fill. A Random Forest model was trained using a binary tree-based machine-learning algorithm and achieved 87% accuracy in prediction of rumen fill. The Random Forest model achieved 16% (cattle) and 57% (sheep) accuracy in validation of the prediction of rumen fill. In conclusion, the ANN model gave better predictions of rumen fill compared to the Random Forest model and should be used in predicting rumen fill of cattle and sheep.

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