<b>Modelo dinámico para estimar biomasa y carbono en bosques del norte de México</b><b></b><b></b>

Aboveground biomass is frequently used as an indicator of forest productivity and carbon storage capacity. The aim of this study was to develop a model for estimating biomass and carbon as a function of stand-level variables. We used data from permanent sample plots established in the forests of the ejido La Ciudad, Durango, where quadratic mean diameter, dominant height, volume, and basal area were measured. Seven growth functions were fitted for each explanatory variable, and their performance was evaluated using mean bias, root mean square error (RMSE, RMSE %), coefficient of determination (R2), and the Akaike Information Criterion (AIC). The model with the best statistical fit and biological behavior was then selected and expressed in dynamic form through the algebraic difference approach (ADA) to allow base-state–invariant projections. The Gompertz model that included basal area provided the best fit, yielding an R2 of 0.874 and a relative error of 15%. The resulting dynamic formulation enabled forward and backward projections of biomass based on changes in basal area and showed the ability to represent growth trajectories under different basal area levels. The results demonstrate the usefulness of the model as a tool for forest planning, evaluation of silvicultural scenarios, and carbon stock estimation for climate-mitigation purposes in natural forests.