Prediction of Pulp Yields from Loblolly Pine Stand and Tree Characteristics, 2002 Fall Technical Conference

An artificial neural network model for predicting wood chip thickness distributions from stand and tree characteristics of loblolly pine and a single tree dry weight pulp yield model were incorporated into a growth and yield simulator. Single tree dry weight pulp yields were estimated from relationships between moisture content and specific gravity along tree boles for selected kappa numbers. Data were derived from 11,771 individual loblolly pine chip thickness measurements. Four stand ages, five dbh classes, and three stem positions were used to predict the cumulative proportion of chip weight per chip thickness class. Tables were constructed to give projected pulp yields for different levels of age, dbh, stand density, site index, and kappa numbers. The numerical procedures outlined can be used by procurement foresters and mill managers to evaluate forest stands for their potential pulp yields or to predict yields from various management strategies.