Prediction of In Situ Ruminal Degradability of Forages in Buffaloes Using the In Vitro Gas Production Technique

G. F. Guadayo, A. A. Rayos, F. E. Merca, A. G. Tandang, M. M. Loresco, A. A. Angeles

Abstract


Two experiments, namely the situ nylon bag technique and the in vitro gas production technique, were carried out to determine the correlations between the in situ ruminal degradability and the in vitro gas production of different forages, and to predict the ruminal degradability of the forages using the gas production parameters. Forage samples from Napier grass (Pennisetum purpureum), Guinea grass (Panicum maximum), Para grass (Brachiaria mutica), Leucaena (Leucaena leucocephala), Rain tree (Samanea saman), and Gliricidia (Gliricidia sepium) were incubated in the rumen of three rumen-cannulated buffaloes using the in situ nylon bag technique for 3, 6, 12, 24, 48, and 72 h. The six forage samples were also subjected to the in vitro gas production analysis following the modified methods developed by Menke & Steingass (1988), along with 30 other commonly used forages in the Philippines. Both experiments followed a randomized complete block design. Their dry matter (DM), organic matter (OM), neutral detergent fiber (NDF), and crude protein (CP) degradation kinetics and effective degradability (ED) as well as the gas production parameters were then estimated. Results revealed that the ED of each nutrient was found to be moderately to strongly correlated with some of the gas production times and estimated gas parameters. The predictor models generated using the gas production parameters for the ED of DM, OM, and NDF were sufficiently strong (R2= 0.740, p value= 0.0002; R2= 0.659, p value= 0.0009; and R2= 0.813, p value < 0.0001, respectively) while that of CP was only moderate (R2= 0.500, p value= 0.0055). It was concluded that the relationship between the two techniques is sufficiently strong and therefore the gas production parameters can be used to predict the in situ ruminal nutrient degradability of forages.


Keywords


grasses; legumes; nutrient degradability; predictor models

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DOI: https://doi.org/10.5398/tasj.2019.42.2.128

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