Stand-level diameter distribution yield model for black spruce plantations

• Published in: Forest ecology and management Vol. 209; no. 3; pp. 181 - 192
• Main Authors: Newton, P.F., Lei, Y., Zhang, S.Y.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 02.05.2005; Elsevier
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; diameter; equations; forest plantations; forest trees; forest yields; Fundamental and applied biological sciences. Psychology; mathematical models; model validation; Ontario; Optimal site occupancy; Picea mariana; Product value; Productivity; Seemingly unrelated regression; simulation models; stand density; Structural yield prediction; Synecology; Terrestrial ecosystems; Three-parameter Weibull PDF; Ontario; Seemingly unrelated regression; Productivity; Three-parameter Weibull PDF; Product value; Optimal site occupancy; Structural yield prediction; Forests; Stand density; Gymnospermae; Development; Coniferales; Spermatophyta; Picea mariana
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2005.01.020

The objectives of this study were to develop and demonstrate a stand-level diameter distribution yield model and associated algorithm for black spruce ( Picea mariana (Mill.) B.S.P) plantations. Employing a parameter prediction approach within the context of a stand density management diagram (SDMD), model development consisted of four sequential steps: (1) obtaining maximum likelihood estimates for the location, scale and shape parameters of the Weibull probability density function (PDF) for 296 empirical diameter frequency distributions; (2) developing and evaluating parameter prediction equations in which the parameter estimates of the Weibull PDF were expressed as functions of stand-level variables employing stepwise regression and seemingly unrelated regression techniques; (3) explicitly incorporating the parameter prediction equations into the SDMD modelling framework; and (4) developing an associated PC-based algorithm and demonstrating its utility in density management decision-making. The results indicated that the parameter prediction equations described 74.4, 87.1 and 66.8% of the variation in location, scale and shape parameter estimates, respectively. Incorporating the parameter prediction equations into the structure of the SDMD enabled the prediction of the temporal dynamics of the diameter frequency distribution by density management regime, site quality and region. An algorithmic version of the model is provided as a decision-support aid in which forest managers are able to simultaneously contrast multiple density management regimes in terms productivity, product value and optimal site occupancy.