Abstract
Context: Prediction of individual board MOE can lead to accurate optimisation of the value extracted from forest resources, and enhanced decision-making on the management and allocation of the resource to different processors, and improve the processors ability to optimise grade allocation.AimsThe objective of this study is to predict the MOE of individual sawn boards from the MOE measured from cores collected from standing trees.Methods: A five-parameter logistic (5PL) function and radial basis function interpolants are used to obtain a continuous distribution of MOE throughout a log. By developing a “virtual sawing” methodology, we predict the individual board MOE for sixty-eight trees consisting of locally developed F1 and F2 hybrid pines (Pinuscaribaea var. hondurensis × Pinuselliottii var. elliottii).Results: Moderate correlations for individual board predictions are observed, with R2 values ranging from 0.47 to 0.53. Good correlations between average predicted board MOE and average measured MOE are also observed, with R2 ≈ 0.83. A pseudo-three-dimensional approach, accounting for variation in height in the tree, affords marginally greater accuracy and predictive capability at the cost of increased data collection and processing. By using a single breast-height core, we can obtain a similar level of prediction of individual board MOE.Conclusion: We have presented a novel non-destructive evaluation approach to predict the MOE of individual boards sawn from trees. This approach can be adapted to other wood properties, and other wood products obtained from trees.