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Brazil is a major producer in the timber sector, mainly with the use of wood from species of the genus Eucalyptus, with 26.1% of planted forests located in Minas Gerais. Researchers and manufacturers have been searching for techniques with the objective of making full use of these forests, with a primary focus on greater growth. A modeling of growth curves is an alternative for the estimation of oral production and
an important aid tool for the researcher's decision making. Growth curves are commonly studied by nonlinear regression models, which have important assumptions that if not met should be added to the model. The present work aims to select among nonlinear Logistic, Gompertz and von Bertalany regression models the most suitable to describe the growth in wood volume of Eucalyptus urophylla x Eucalyptus grandis hybrids in three Forest Site categories, including whether assumption deviations are required. Methods were executed by the Gauss-Newton iterative method implemented in nls() and it gnls() functions of the R software. Determination coecient, Akaike information criterion (AICc) and Residual Standard Deviation (RSD) were used as selection evaluators of the best model. The results demonstrate that for all site categories, the Gompertz model with addition of autoregressive parameters AR (1) is the most appropriate to describe the growth in wood volume of Eucalyptus urophylla x Eucalyptus grandis hybrids. The addition of the rst-order autoregressive parameter does not aect the quality of t, but it is the correct procedure. Site I, which presents the largest trees according to pre-dened variations, recorded 308 m3/ha of wood volume, followed by 286 m3/ha and 263 m3/ha for Sites II and III, respectively. The time for Site III to reach the maximum point of volume growth is between the fourth and sixth year, while the other sites are more precocious, reaching this point between the second and third year.
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