Predicting stand attributes of loblolly pine in West Gulf Coastal Plain using gradient boosting and random forests

Abstract

Predicting future stand yield as a function of current stand conditions is important to forest managers. Two machine-learning techniques, gradient boosting (GB) and random forests (RF), were used to predict stand mean height of dominant and codominant trees (HT), trees per hectare (Tree·ha−1), and basal area per hectare (BA·ha−1) based on data sets collected from extensively and intensively managed loblolly pine (Pinus taeda L.) plantations in the West Gulf Coastal Plain region. Models were evaluated using coefficient of determination (R2) and bias by applying models to independent tests and validation data sets and then comparing to conventional statistical models (Coble-2017) currently being used in the region. For extensively managed plantations, the GB models had less bias than the RF models. For model precision (R2), the GB models were consistently better than the RF models, and the HT model was the best, followed by those of Tree·ha−1 and BA·ha−1. Even for BA·ha−1, the GB and RF models had R2 over 0.81. GB and RF models outperformed the Coble-2017 model; differences were not substantial for Tree·ha−1 but were significant for HT and BA·ha−1 (R2 = 0.96, 0.95, and 0.88 for HT and 0.84, 0.81, and 0.76 for BA). Important predictors identified by GB and RF and their contributions to the models were similar. For intensively managed plantations, GB and RF were similarly accurate in predicting HT and Tree·ha−1, but GB outperformed RF in predicting BA·ha−1 (R2 = 0.87 versus 0.75). We conclude that both GB and RF, although the former is preferred, can be effective in predicting future stand attributes. Forest managers can use the models presented here to predict quantitative information required for managing loblolly pine plantations in the region.