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Dominant tree selection for height estimation and its impact on site classification for African mahogany stands

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Taylor and Francis Online

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African mahogany plantations are increasing in Brazil, especially those planted with the species Khaya grandifoliola C. DC. Despite the interest in establishing plantations, there is a lack of studies in management practices for the genus. This makes it difficult to improve methodologies that optimise forest inventory activities, which are essential to support forest management decisions. Therefore, this study aimed to evaluate the effect of the number of trees selected to determine the mean dominant height, and to verify its impact on site classification of African mahogany stands. This will then optimise height sampling and ensure correct site index estimation. To determine the dominant height, 11 treatments were tested, with the selection of 1st to 11th thickest trees per plot, representing between 9 and 102 dominant trees per hectare. The effect of the treatments was evaluated using the values of the mean dominant height and metrics regarding site index classification (stability, stratification capacity and relationship with stand growth). Treatments with inclusion of more dominant trees resulted in a more stable site classification at the expense of a lower correlation with stand growth. Treatments using fewer dominant trees resulted in a greater allocation of plots in the upper site classes, showing that the number of trees used to calculate the dominant height influences the distribution of plots in different site classes. We concluded that Treatments 3 (27 trees ha−1) to 6 (55 trees ha−1) had the best performance, guaranteeing adequate site index estimation. The selection of at least 27 and a maximum of 55 dominant trees per hectare is recommended for African mahogany plantations with low planting densities.

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TEIXEIRA, M. O. et al. Dominant tree selection for height estimation and its impact on site classification for African mahogany stands. Southern Forests: a Journal of Forest Science, [S.l.], v. 85, n. 1, 2023.

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