资源环境科技发展态势分析平台

The objectives of this study were to (1) develop wood quality prediction models for a suite of commercially-relevant jack pine (Pines banksiana Lamb.) fibre attributes (wood density (W-d), microfibril angle (M-a), modulus of elasticity (M-e), fibre coarseness (C-o), tracheid wall thickness (W-t), tracheid radial (D-r) and tangential (D-t diameters and specific surface area (S-a)), (2) given (1), incorporate the parameterized equations within structural stand density management models (SSDMMs), and (3) given (2), exemplify their utility in silvicultural decision-making via the comparative assessment of attribute outcomes arising from operationally-relevant crop plans. Analytically, the equations were developed deploying Silviscan-determined attributes derived from transverse breast-height radial xylem sequences obtained from 61 trees sampled from 2 geographically-separated thinning experiments located in the central region of the Canadian Boreal Forest Region. Hierarchical mixed-effects regression modeling combined with cross-validation procedures were used to specify, parameterize and evaluate the attribute-specific prediction equations. Overall, the results revealed that the attribute trajectories were size-dependent and the resultant models were adequate in terms of their goodness-of-fit characteristics (e.g., I-2 values of 75, 71, 71, 66, 60, 55, 49 and 38% for C-o, M-e, W-t, S-a, W-d, M-a, D-r and D-t respectively), lack-of-fit indicators (e.g., temporally invariant patterns of absolute and relative errors devoid of evidence of systematic bias), and predictive performance (e.g., 95% probability that 95% of all future relative D-t, D-r, S-a, W-d, C-o, W-t, M-e and M-a errors would be within +/- 5, +/- 9, +/- 11, +/- 12, +/- 12, +/- 13, +/- 35 and +/- 43% of their true values, respectively). Incorporating the jack pine equations along with a similar suite of functions previously developed for black spruce (Picea mariana (Mill) B.S.P) into the SSDMM analytical framework, yielded a pair of enhanced stand-type-specific (natural-origin and planted stands) decision-support systems for each species. These systems enabled the estimation of attribute-specific developmental trajectories for the rotational tree population from which diameter-class and stand-level wood quality performance measures were derived. As exemplified, the development of size-dependent fibre attribute prediction models and their subsequent integration within SSDMMs provides forest managers with a decision-support platform for evaluating and comparing end-product-related consequences of selected crop plans.