The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

Abstract

All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function.

Invited review: Nitrogen in ruminant nutrition: A review of measurement techniques

“…A more recent meta-analysis by White et al (2016) compared empirical and mechanistic (i.e., microbial N as a function of ruminal carbohydrate digestibility) approaches to predict MPS and concluded that predicting MPS empirically had the lowest prediction error. In a review by Bannink et al (2016), it was delineated what added value mechanistic approaches may have above empirical approaches by representing the interactions between various rumen pools (substrate, microbial, and metabolites) to explain the more dynamic aspects of rumen fermentation, which an empirical approach cannot give. The latter approaches normally adopt the concept of additivity of predicted MPS values for the individual dietary components and are, in essence, linear models not accounting for nonlinear effects.…”

Modeling the Effect of Nutritional Strategies for Dairy Cows on the Composition of Excreta Nitrogen

“…The present model takes into account interactions between different types of nutrients and the interaction with microbial activity. Therefore, its use may significantly improve the prediction of feed digestion in comparison to current static feed evaluation systems (Bannink et al, 2016). This feature is clearly illustrated by the prediction of a reduced digestion of rumen digestible fiber on diets that contain a large fraction of concentrate feeds (Figure 3).…”

Theoretical turnover rate of the rumen liquid fraction in dairy cows and its relationship to feed intake, rumen fermentation, and milk production

“…However, 103 L/day, the turnover of ruminal fluid, was considerably lower than the PRV observed in our present investigation in the range of 579–1,146 L/day of 538–783‐kg cows (average = 628 kg) (Table ). Although some studies indicated that the ruminal liquid turnover varied from 2.8 to 4.5 (Bannink, van Lingen, Ellis, France, & Dijkstra, ; Gregorini, Beukes, Waghorn, Pacheco, & Hanigan, ; Holden, Muller, Varga, & Hillard, ), the PRV was still high even considering the rumen volumes (RV) estimated based on these studies. On the other hand, SCFAs yield (mol/day) ( Y ) estimated by using the PRV in this study was highly related to it ( X ) estimated by a formula: total SCFAs production (mol/day) = 4.7 × DMI (kg) (Noziere et al., ).…”