This work shows how modern machine learning techniques can be used to solve current problems faced by the forestry industry. More specifically, the focus is on comparing the predictive performance of several algorithms on estimating the dry weight, in tons, of chip residues. The dataset contains samples obtained during 22 months from 220 trucks coming from 17 different farms located within the area spanned by the Biobio and Maule regions, Chile. Once the trucks arrived, samples were collected and dried to compute the dry tons carried by each truck, which was set as the dependent variable. Using open-source software implementations of state-of-the-art algorithms it was possible to determine, for our data, that even though the non-parametric models Gradient Boosting Machines (GBM) and Neural Networks (NNET) outperformed the linear regression (LM) model, they are not statistically superior to the LASSO regression (GLMNET), an improved version of the LM model. Additionally, it was observed that seasonality affects the ratio of green tons to dry tons a truck can deliver to a power plant during the year. Finally, the continuous variables green tons, elevation, east and north (longitude-latitude) also contribute to explaining the dependent variable.