The objective of this study was to evaluate the effect of lidar point density, plot size, and stand management conditions on modeling dasometric variables with the area-based approach (ABA). The study was divided into three phases. In the first phase, two stand conditions were evaluated using two plot sizes (300 and 500 m2) and a lidar point cloud density of ~ 400 points m⁻2. Metrics were selected using LASSO regularization. In the second phase, the effect of lidar point density was analyzed by systematically reducing the initial point density from 80 to 1%. In the third phase, model fitting was conducted using Seemingly Unrelated Regression (SUR) method. The results highlight the importance of plot size, with better results obtained for the larger plots (500 m2). Although lidar cloud density was not identified as a critical factor, the application of segmented models allowed the identification of inflection points suggesting that a lidar point density between 20 and 40 points m⁻2 is sufficient to obtain estimates with lower RMSE. Finally, the SUR method demonstrated its potential for estimating composite variables such as volume since it depends on other variables for its estimation, emphasizing the need to continue exploring its application in modeling with lidar information for forestry applications.