Abstract
A new model for predicting the total tree height for harvested stems from cut-to-length (CTL) harvester data was constructed for Pinus radiata (D.Don) following a conceptual analysis of relative stem profiles, comparisons of candidate models forms and extensive selections of predictor variables. Stem profiles of more than 3000 trees in a taper data set were each processed 6 times through simulated log cutting to generate the data required for this purpose. The CTL simulations not only mimicked but also covered the full range of cutting patterns of nearly 0.45 × 106 stems harvested during both thinning and harvesting operations. The single-equation model was estimated through the multiple-equation generalized method of moments estimator to obtain efficient and consistent parameter estimates in the presence of error correlation and heteroscedasticity that were inherent to the systematic structure of the data. The predictive performances of our new model in its linear and nonlinear form were evaluated through a leave-one-tree-out cross validation process and compared against that of the only such existing model. The evaluations and comparisons were made through benchmarking statistics both globally over the entire data space and locally within specific subdivisions of the data space. These statistics indicated that the nonlinear form of our model was the best and its linear form ranked second. The prediction accuracy of our nonlinear model improved when the total log length represented more than 20% of the total tree height. The poorer performance of the existing model was partly attributed to the high degree of multicollinearity among its predictor variables, which led to highly variable and unstable parameter estimates. Our new model will facilitate and widen the utilization of harvester data far beyond the current limited use for monitoring and reporting log productions in P. radiata plantations. It will also facilitate the estimation of bark thickness and help make harvester data a potential source of taper data to reduce the intensity and cost of the conventional destructive taper sampling in the field. Although developed for P. radiata, the mathematical form of our new model will be applicable to other tree species for which CTL harvester data are routinely captured during thinning and harvesting operations.
