Sensitivity analysis of a mechanistic model of rumen fermentation and methane production by rumen microbiota in the presence ofAsparagopsis taxiformis

Abstract

Ruminant animals rely on microbes for the conversion of complex plant material into host accessible metabolites. During this anaerobic conversion of plant biomass, termed enteric fermentation, methanogenic archaea convert hydrogen into the potent greenhouse gas methane (CH4). The search for methane mitigation strategies to combat climate change has identified the red seaweedAsparagopsis taxiformisas a promising feed additive that, when added to a regular cattle diet, reduced enteric CH4by over 80%. A more complete understanding of microbial interactions during enteric fermentation is needed for ongoing improvement to mitigation methods. Mathematical models that permitin silicosimulation of enteric fermentation allow for the identification of key parameters that drive rumen methane production. Here we built upon an existing rumen fermentation model and calibrated it using a preliminary classification of functional microbial groups and gas emission data from a previously publishedin vitrorumen fermentation experiment, but many microbes remained functionally unclassified. The model was then used to conduct anin silicoexperiment to explore how the partition of functionally unclassified microbes into functional groups affects methane output. Thesein silicoexperiments identified that model methane production is more sensitive to microbial variation in the presence ofA. taxiformisversus without. The use of local and global sensitivity analysis approaches revealed other rumen parameters to also be drivers of enteric methane production. In the presence ofA. taxiformis, parameters modulating methane production include bromoform concentration, methanogen abundance, total microbial concentration, a parameter effecting the inhibition of methanogen growth rate by the action of bromoform, and the maximum specific utilization rate of hydrogen. WithoutA. taxiformis, feed composition parameters, the hydrolysis rate constant of cell wall carbohydrates, and a parameter affecting the yield factors during sugar utilization were found to be most significant. For possible methane reduction withoutA. taxiformis, we propose an adjustment in feed composition parameters that reduces predicted methane by 25.6%.

Dynamic sensitivity analysis of a mathematical model describing the effect of the macroalgaeAsparagopsis taxiformison rumen fermentation and methane production underin vitrocontinuous conditions

“…This last IP showed also an influence on variation for high AT treatment in our work. Therefore, Merk et al (2023) also identified a shift in the key factors driving CH 4 production variation in presence of AT.…”

“…This last IP showed also an influence on 𝑞 CH 4 ,g,out variation for high AT treatment in our work. Therefore, Merk et al (2023) also identified a shift in the key factors driving CH4 production variation in presence of AT. For other references, the comparison of SA results is only valid for the control as other models did not consider AT treatments.…”

“…When no dose of AT was considered, Merk et al (2023) also highlighted the impact of fibers degradation component on CH 4 production variation via the initial neutral detergent fiber concentration and k hyd,ndf . This is the initial condition of neutral detergent fiber that was largely associated with the highest contribution (= 43%) to CH 4 production variation.…”

“…In presence of a dose of AT (included at a 5% inclusion rate) , the high impact of IPs associated with bromoform concentration and the factor 𝐼 br on CH4 production variation was also highlighted in Merk et al (2023). The initial bromoform concentration and 𝑝 1 showed the highest contributions (= 46%) to CH4 production variation.…”

“…In both studies, uniform distributions were set for exploring parameter variability. Recently, Merk et al (2023) adapted and calibrated the model of Muñoz-Tamayo et al (2021) to represent experimental data from the in vitro RUSITEC study of Roque et al (2019), which aimed at evaluating the effect of the macroalgae Asparagosis taxiformis (AT) on CH 4 production and rumen microbiota. Authors used a Sobol based approach for identifying key parameters associated to microbial pathways driving CH 4 production with and without the presence of AT.…”