Multi-volume modeling of Eucalyptus trees using regression and artificial neural networks.

The stem volume of commercial trees is an important variable that assists in decision making and economic analysis in forest management. Wood from forest plantations can be used for several purposes, which makes estimating multi-volumes for the same tree a necessary task. Defining its exploitation and use potential, such as the total and merchantable volumes (up to a minimum diameter of interest), with or without bark, is a possible work. The goal of this study was to use different strategies to model multi-volumes of the stem of eucalyptus trees. The data came from rigorous scaling of 460 felled trees stems from four eucalyptus clones in high forest and coppice regimes. The diameters were measured at different heights, with the volume of the sections obtained by the Smalian method. Data were randomly separated into fit and validation data. The single multi-volume model, volume-specific models, and the training of artificial neural networks (ANNs) were fitted. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error, mean bias error, as well as graphical analysis of observed and estimated values and distribution of residuals. Additionally, the t-test (α = 0.05) was performed between the volume obtained in the rigorous scaling and estimated by each strategy with the validation data. Results showed that the strategies used to model different tree stem volumes are efficient. The actual and estimated volumes showed no differences. The multi-volume model had the most considerable advantage in volume estimation practicality, while the volume-specific models were more efficient in the accuracy of estimates. Given the conditions of this study, the ANNs are more suitable than the regression models in the estimation of multi-volumes of eucalyptus trees, revealing greater accuracy and practicality.

Influence of fertilizer and hydrogel on physical-chemical attributes of substrate for seedling production / Influência de adubação e hidrogel nos atributos físico-químicos de substrato para produção de mudas florestais

The incorporation of the hydrogel into the substrate used in the forest nursery promotes improvements in its physical-chemical attributes. However, several factors influence the hydrogel efficiency, such as the increase of salts, promoted by fertilizer of the substrate. Therefore, this study aimed to evaluate the effects of the incorporation of fertilizer levels and hydrogel doses on the physical-chemical attributes of a substrate to produce forest seedlings. Different levels of basic fertilizer commonly used in the nursery (100%, 50% and 0%) and different doses of the hydrogel (0, 1, 2 and 3 g L-1) were incorporated into the substrate Tropstrato Florestal®, consisting of 12 treatments in a 3 x 4 factorial scheme. A sample of the substrate in each treatment was sent for analysis of its physical and chemical attributes. To evaluate the effect of the treatments on the physical-chemical attributes of the substrate, was used the multivariate principal components analyses. The levels of fertilizer and hydrogel doses were correlated with the attributes of the substrates obtained in each treatment. The incorporation of different fertilizer levels and hydrogel doses to the substrate modified their chemical and physical attributes. Fertilizers have influenced mainly the chemical attributes of the substrate, whereas hydrogels have influenced the physical attributes, mainly those related to water retention capacity and availability.

A incorporação do hidrogel ao substrato utilizado no viveiro florestal promove melhorias em seus atributos físico-químicos. Porém vários fatores influenciam na eficiência do hidrogel, como o aumento de sais, promovido pela adubação do substrato. Portanto, este estudo teve como objetivo avaliar os efeitos da incorporação de níveis de adubação e doses de hidrogel sobre os atributos físico-químicos de um substrato para a produção de mudas florestais. Foram incorporados ao substrato Tropstrato Florestal® diferentes níveis da adubação de base comumente utilizada no viveiro (100%, 50% e 0%) e diferentes doses do hidrogel (0, 1, 2 e 3 g L-1), consistindo em 12 tratamentos em esquema fatorial 3 x 4. Uma amostra do substrato em cada tratamento foi encaminhada para análise de seus atributos físicos e químicos. Para avaliar o efeito dos tratamentos sobre os atributos físico-químicos do substrato foi utilizada a análise multivariada de componentes principais. Os níveis de adubação e doses de hidrogel foram correlacionados com os atributos dos substratos obtidos em cada tratamento. A incorporação de diferentes níveis de adubação e doses de hidrogel ao substrato modificaram seus atributos químicos e físicos. A adubação influenciou principalmente os atributos químicos do substrato, enquanto que o hidrogel os atributos físicos, principalmente os relacionados à capacidade de retenção e disponibilidade de água.