Ochroma pyramidale (Cav. ex. Lam.) Urb. (balsa-tree) is a commercially important tree species that ranges from Mexico to northern Brazil. Due to its low weight and mechanical endurance, the wood is particularly well-suited for wind turbine blades, sporting equipment, boats and aircrafts; as such, it is in high market demand and plays an important role in many regional economies. This tree species is also well-known to exhibit a high degree of variation in growth. Researchers interested in modeling the height–diameter relationship typically resort to using ordinary least squares (OLS) to fit linear models; however, this method is known to suffer from sensitivity to outliers. Given the latter, the application of these models may yield potentially biased tree height estimates. The use of robust regression with iteratively reweighted least squares (IRLS) has been proposed as an alternative to mitigate the influence of outliers. This study aims to improve the modeling of height–diameter relationships of tree species with high growth variation, by using robust regressions with IRLS for data-sets stratified by site-index and age-classes. We implement a split sample approach to assess the model performance using data from Ecuador’s continuous forest inventory (n = 32,279 trees). A sensitivity analysis of six outlier scenarios is also conducted using a subsample of the former (n = 26). Our results indicate that IRLS regression methods can give unbiased height predictions. At face value, the sensitivity analysis indicates that OLS performs better in terms of standard error of estimate. However, we found that OLS suffers from skewed residual distributions (i.e., unreliable estimations); conversely, IRLS seems to be less affected by this source of bias and the fitted parameters indicate lower standard errors. Overall, we recommend using robust regression methods with IRLS to produce consistent height predictions for O. pyramidale and other tree species showing high growth variation.

The objective of this work was to compare the height-diameter relationship, described by nonlinear biological models, in Eucalyptus benthamii in monoculture forestry and in three different integrated crop-livestock systems (ICLS): crop-forestry, livestock-forestry, and crop-livestock-forestry. The trees were evaluated during seven years after planting. Five nonlinear biological models were fitted to evaluate the height-diameter relationship, and Gompertz’s model was selected to describe the data, although all models described satisfactorily the height-diameter relationship of the trees in the ICLS. The analysis of the data showed that there is no similarity between monoculture forestry and the ICLS as to the height-diameter relationship. In addition, the height-diameter relationship in E. benthamii changes between the different ICLS. Particularly, two systems with cattle provide the same values of maximum growth rate, asymptote, and inflection point of diameter at breast height. Furthermore, with the integration of cattle into the tree component, the produced trees show lower asymptotic heights, with larger diameters when the average tree heights of the ICLS are equal.

Regularization methods were tested for selecting covariates in loblolly pine survival models. Stepwise method was the most suitable approach for selecting the covariates. Link functions can influence the covariates selection procedure. The survival models presented great ability to predict alive trees.