Evaluating precision and accuracy of species-specific equations versus a generic equation in predicting commercial stem volume of managed forest species in the Amazon

ABSTRACT Aiming at improving accuracy and precision in the prediction of commercial stem volume, we compare species-specific and generic equations. Variables of 789 stems from 13 commercial tree species were measured, to know: diameter at breast height (D), commercial height (Hc), and volume. Two analyses were performed. First, global datasets (comprising all species) and species-specific datasets (one dataset per species) were used to fit volume models through bootstrap samples. Then, differences in regression coefficients, accuracy, and precision between both datasets were investigated. As a result, for all tested volume models, species-specific equations had less than 65% of their coefficients within the confidence interval of the generic equation coefficients, suggesting a potential inferential limitation when using a generic equation to predict the commercial volume of a single species. This coefficient frequency was notably lower (<5%) for two species. For the two-parameter Schumacher & Hall model, gains in accuracy when using a species-specific equation instead of a generic equation ranged from 0 to 61 times (average of ~8 times), gains in precision ranged from 0 to 30 times (average of ~6 times). The findings of this study emphasize the necessity of species-specific management, concluding that modeling stem volume with a species-specific approach can yield more precise and accurate predictions.