Mathematical models have been developed to better understand rumen fermentation and methane (CH4) production. The influence of input parameters (IP) of these models have been often assessed by analysis static sensitivity analyses (SA). We hypothesized that dynamic SA can be useful to inform on optimal experiments aimed at quantifying the mechanisms that drive CH4 production. We used as case study a dynamic model representing rumen fermentation under in vitro continuous conditions. It describes the effect of Asparagopsis taxiformis (AT), a potent CH4 inhibitor. We computed the Shapley effects (Owen, 2014) over time of 15 IP of the model to quantify their contribution to CH4 (mol/h) variation. Two AT treatments were compared: a low treatment (LT: 0.25% DM of AT) and a high treatment (HT: 0.50% DM of AT). The IP studied were hydrolysis rate parameters (khyd, h-1) of 3 polymer components in the diet, kinetic rate parameters (km, mol substrate/(mol biomass h) and KS, mol/L) of 3 soluble substrates in the rumen, and parameters describing AT effects on fermentation (p). The hydrogen utilizers microbial group with IP KS,H2 contributed to more than 50% of CH4 variation over time for control (0% DM of AT) and LT. For HT, IP p2, which models the direct effect of AT on methanogens, showed a high influence (> 30%) over 2 time periods, highlighting a shift of the pathways impacting CH4 variation over time. Further work is ongoing to exploit dynamic SA for designing optimal strategies using AT for CH4 mitigation.