Use of Logistic Regression Model for Prediction of Non-Timber Forest Products

The use of non-timber is a valuable alternative for the conservation of tropical forests. Jucara (Euterpe edulis Mart.) is considered one of the main alternatives in the Atlantic Forest for the production of acai pulp. However, there are few studies that aim to evaluate their production. The present study aimed to construct a probabilistic model to predict the production of Euterpe edulis bunches, using dendrometric variables and competition index. Twenty plots of 10 × 50 m were sampled in an area with said specie, showing the arboreal entities with diameter at breast height > 4.8 cm, and recording the Euterpe edulis phenomena. The main variables influencing the production of bunches were assessed using logistic regression model. The logistic regression showed the variables diameter breast height (DBH) and total height (h) as significant to explain the variation between productive and non-productive entities. The competition index tested was not significant (p-value = 0.221). The model of prediction of curl production in Jucara can be written as: Zi = -6.878594 + 0.2522454 × DBH + 0.1951574 × h. The use of a logistic regression model showed potential for prediction of non-timber forest products.