The AM2 model was able to accurately predict gas production in anaerobic digestion, with methane production increasing from 0.015 to 0.018 /day as organic loading rates (OLRs) increased. However, the model faced limitations in predicting volatile fatty acid (VFA) dynamics, especially at high OLRs, due to the presence of excess organic matter.In a 37-day experiment of maize silage digestion in a 50-liter anaerobic reactor, it was shown that the AM2 model accurately estimates biogas production, with feeding intervals of fifteen minutes and a pause during weekends.The extended AM2 model was calibrated to ADM1 for grass silage simulation in  2015b. The ADM1 simulation was unsteady initially, with inconsistent biogas flow and alkalinity output profiles, which were stabilized by increasing the disintegration process parameter, , to 0.266 based on a literature review. The profiles demonstrated stability, and an identical initial parameter was suggested for .The organic loading rate (OLR) and hydraulic retention time (HRT) were set at 3.58 kg ODM   and 33.09 days, respectively. The extended AM2 model successfully simulated biogas and methane flow rate profiles, indicating better performance than ADM1 for grass silage digestion simulation.Cattle manure digestion is simulated using an extended AM2 model calibrated to ADM1, based on literature parameters. Manure composition analysis determines the influent composition of organic fractions. Manure is valuable for agriculture, enhancing soil structure and nutrient availability, with some nutrients persisting despite cow digestion. Anaerobic digestion of manure can be affected by ammonia concentrations, with equilibrium digestion achievable by maintaining elevated rates, as detailed in the literature.In AM2's extended version, a sensitivity analysis of 24 parameters found that  , , ,  , , , , ,  , , ,  ,  , ,    and  related to substrate degradation and CO2 yield, had the greatest impact on model output.The hydrolysis process and organic matter parameters also demonstrated high sensitivity.Sensitivity analysis data can improve model accuracy by removing parameters with low sensitivity.AM2 model extensions were made based on sensitivity analysis and AM2-ADM1 model comparisons, enhancing its applicability and accuracy. These modifications allowed AM2 to account for ADM1's combined factors, improving simulation results. While biogas generation and key variables showed agreement with ADM1 trends, AM2 responded more slowly to feedstock addition. Important findings include the consideration of inorganic nitrogen incorporated into organic matter, addressing a limitation in the original model.