Alternative Analytical and Experimental Procedures to Explore Rumen Fermentation as Driven by Nutrient Supplies

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Virginia Tech


Ruminant livestock play a vital role in fulfilling the nutrient requirements of humans by providing protein, energy, and essential microminerals. With the increasing demand for meat and dairy products, the ruminant industry must continue to improve the productivity and efficiency of ruminant animals with limited resources while minimizing the environmental impact. Rumen fermentation is the focal point of the productivity and efficiency of the animal and numerous chemical, physical and biochemical interactions make the rumen a complex ecosystem. Therefore, improving the understanding of fermentation dynamics in a holistic manner and characterizing how fermentation varies in response to different nutrient supplies can greatly expand our knowledge on rumen fermentation to develop better engineered rumen manipulation strategies. The central aim of these investigations was to employ alternative analytical strategies for holistic exploration of complex relationships among rumen, animal, and dietary variables and to estimate rumen volatile fatty acid (VFA) dynamics under different nutrient supplies. The objective of the first study was to explore the strengths and limitations of mixed-model meta-analysis, recursive feature elimination (RFE), and additive Bayesian networking (ABN) in identifying relationships among diet, rumen, and milk performance variables. Both mixed-models and ABN agreed upon most of the variables and relationships identified while RFE failed to capture interactions. Given the capacity of mixed models for quantitative inquiry and the potential of ABN to illustrate complex associations in a more intuitive way, future investigations combining both approaches hold potential to explore intercorrelated data in a holistic manner. Followed by the successful use of ABN in the first study, the goal of a follow up study was to investigate the potential of two different network approaches to explore rumen level interactions using data generated in continuous culture experiments. Two network analysis approaches, EBIC-LASSO network (ELN) and Bayesian learning network (BLN) were leveraged to explore the relationships among rumen fermentation parameters in continuous culture experiments. Unidirectional ELN illustrated prominent variables while BLN, which produces a directed acyclic graph, identified directional relationships implying causality. Overall, both networking approaches demonstrate strengths in capturing connectedness and directionality of rumen fermentation variables. In a complementary line of work, the next experiment focused on developing an alternative method for iso-tope based assessments to produce less expensive, and more efficient screening of fermentation conditions driven by diet. Cannulated wethers were used in this study and 4 dietary treatments combining lowly and highly degradable fiber (timothy hay and beet pulp, respectively) and protein (heat-treated soybean meal and soybean meal, respectively) were tested. Results indicated that fluid volume of the rumen and the rate of passage were influenced by protein, but not fiber, source. Higher rumen volumes and lower passage rates were associated with heat-treated soybean meals. The effect of dietary treatments on VFA absorption dynamics was prominent compared to the minimal changes in production dynamics. Overall, heat-treated soybean meal appears to influence VFA disappearance resulting in low concentrations within the rumen, but greater flux of VFA disappearance. In conclusion, this method demonstrated the capacity to estimate VFA dynamics beyond concentrations and molar proportions while being cost effective and more physiologically relevant. In a fourth study, we sought to investigate the growth performance and rumen VFA profile in response to different planes of nutrients and naturally occurring coccidiosis. Coccidiosis infection altered rumen isobutyrate concentrations and tended to alter major VFA concentrations suggesting the need of future work to explore coccidiosis effects on rumen fermentation. The first two investigations highlighted the potential and strength of leveraging alternative analytical tools to complement statistical approaches generally used in ruminant nutrition while concurrently improving ability to explain complex associations in the rumen. The third and fourth projects characterized the rumen VFA dynamics and profile in response to the different nutrient degradability and health status, respectively. Collectively, these investigations contribute to better understanding of rumen dynamics through novel analytical and experimental approaches.



Bayesian network, coccidiosis, meta-analysis, network analysis, rumen volatile fatty acid