Using soil-based and physiographic variables to improve stand growth equations in Uruguayan forest plantations
Information provided by traditional growth models is an essential input in decision making processes for managing planted forests. They are generally fitted using inventory data guaranteeing robustness and simplicity. The introduction of explanatory factors affecting tree development in age-based sigmoidal growth and yield equations attempts not only to improve the quality of predictions, but also to add useful information underpinning forest management decisions. This study aimed to assess the use of the following soil-based and physiographic predictors: potentially available soil water (PASW), elevation (Elev), aspect (α) and slope (β) in a system of empirical stand equations comprising: dominant height (hdom), basal area (G), maximum diameter at breast height (dmax), and standard deviation of diameters (SDd). Augmented models were compared with the base models through precision and bias of estimations for two contrasting species: Pinus taeda (L.), and Eucalyptus grandis (Hill ex. Maiden), planted commercially in Uruguay. Soil-based and physiographic information significantly improved predictions of all the state variables fitted for E. grandis, but just hdom and G for P. taeda. Only PASW was consistently significant for the augmented models in P. taeda and E. grandis, while the contribution of other predictors varied between species. From a physiological point of view, predictors on the augmented models showed consistency. Models with such augmentation produced decrease of errors between 3 to 10.5%, however decreases in the prediction errors calculated with the independent dataset were lower. Results from this study contributed to add information to the decision-making process of plantations’ management.