Abstract
Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).
Highlights
Eucalyptus plantations are located all over the world [1]
This study aims at using artificial neural networks—without losing accuracy—to evaluate the reduction in the number of trees sampled, in terms of the commercial timber volume of Eucalyptus clones in the Brazilian Amazon and compared with the classical method of linear regression
The trees used to validate the datasets were those that did not participate in the artificial neural networks (ANN) training and regression fit; biases in the predictions were avoided
Summary
Eucalyptus plantations are located all over the world [1]. In Brazil, most tree species used to supply the forest industry are of the genus Eucalyptus; they have favorable characteristics for the industry and can adapt to different edaphic and climatic conditions [2,3]. In the Amazon region, forest plantations with Eucalyptus are expanding, especially in pasture areas [4], where volumetric timber stock should be constantly evaluated. Despite the importance of Eucalyptus in Brazil, there is a lack of technical and scientific information on cultures in the Amazon region [5,6]. This is the case for the availability of volumetric equations.
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