This study develops forestry growth and yield models for two economically important tree species in Manitoba, black spruce [Picea mariana (Mill.) BSP] and jack pine [Pinus banksiana Lamb]. The growth and yield models developed include regression-based individual tree height growth and site index, tree diameter (basal area) growth, tree bole taper, and individual tree mortality models. These regression-based models were developed empirically, using stem analysis, growth and mortality data from 80 permanent sample plots located within the commercially important boreal forests of Manitoba. Model development involved the exploration, comparison and testing of numerous potential regression models and predictor variables. Statistical issues commonly encountered in forest growth and yield modeling, particularly data autocorrelation and variable multicollinearity, were addressed using nonlinear least squares (NLS), generalized nonlinear least squares (GNLS), and nonlinear mixed-effects model regression (NLMM) approaches. Height growth and site index of black spruce and jack pine was modelled using a three-parameter generalized logistic function. NLMM regression was used since the data were spatially autocorrelated. The inclusion of prior measures from individual trees produced more accurate predictions. In the tree diameter (basal area) growth models, tree size variables were significant predictors for black spruce and managed jack pine stands. Site index (a measure of site productivity) was positively correlated, and basal area of trees larger than the target tree (a relative measure of competition) negatively correlated, with diameter increment. Thiessen polygon area, a spatial measure of competition, was a significant predictor for natural jack pine and upland black spruce stands. Tree bole taper was modeled by NLMM approach using a five-parameter equation based on dimensional analysis, with breast height diameter, total height and relative height as predictor variables. The inclusion of a single prior measure from each tree improved model prediction. Black spruce and jack pine mortality was modeled using logistic regression. The black spruce models predicted high survivorship for larger, fast-growing trees in less crowded stands. In the jack pine model, highest survivorship was predicted for larger, less locally crowded trees.

This study develops forestry growth and yield models for two economically important tree species in Manitoba, black spruce [Picea mariana (Mill.) BSP] and jack pine [Pinus banksiana Lamb]. The growth and yield models developed include regression-based individual tree height growth and site index, tree diameter (basal area) growth, tree bole taper, and individual tree mortality models. These regression-based models were developed empirically, using stem analysis, growth and mortality data from 80 permanent sample plots located within the commercially important boreal forests of Manitoba. Model development involved the exploration, comparison and testing of numerous potential regression models and predictor variables. Statistical issues commonly encountered in forest growth and yield modeling, particularly data autocorrelation and variable multicollinearity, were addressed using nonlinear least squares (NLS), generalized nonlinear least squares (GNLS), and nonlinear mixed-effects model regression (NLMM) approaches. Height growth and site index of black spruce and jack pine was modelled using a three-parameter generalized logistic function. NLMM regression was used since the data were spatially autocorrelated. The inclusion of prior measures from individual trees produced more accurate predictions. In the tree diameter (basal area) growth models, tree size variables were significant predictors for black spruce and managed jack pine stands. Site index (a measure of site productivity) was positively correlated, and basal area of trees larger than the target tree (a relative measure of competition) negatively correlated, with diameter increment. Thiessen polygon area, a spatial measure of competition, was a significant predictor for natural jack pine and upland black spruce stands. Tree bole taper was modeled by NLMM approach using a five-parameter equation based on dimensional analysis, with breast height diameter, total height and relative height as predictor variables. The inclusion of a single prior measure from each tree improved model prediction. Black spruce and jack pine mortality was modeled using logistic regression. The black spruce models predicted high survivorship for larger, fast-growing trees in less crowded stands. In the jack pine model, highest survivorship was predicted for larger, less locally crowded trees.