Wood property genetic parameter estimation from first-generation Douglas-fir progeny tests

Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) is the most important commercial timber species in the United States Pacific Northwest (US PNW). Owing to its significance, Douglas-fir has been the subject of long-term tree improvement. First-generation and second-generation progeny tests are available for wood property evaluation, but aside from specific gravity (from increment cores) and stiffness (usually determined on standing trees using acoustics), the estimation of genetic parameters has been limited. There is interest in evaluating trees for wood stiffness, but the cost of evaluation is generally a barrier. Near infrared hyperspectral imaging (NIR-HSI) may provide a rapid technique for the estimation of a variety of wood properties, providing wood property data is available for building predictive models. In this study, SilviScan was used to assess tracheid properties (wall thickness, coarseness, specific surface and radial and tangential diameter), air-dry density, microfibril angle (MFA) and stiffness for 40 calibration samples, 20 each from two progeny tests aged ten and twelve years, respectively (500 samples in all, one test site from each of two independent first-generation breeding programs). Wood properties were measured on sections of increment cores representing the five growth rings adjacent to the bark. Based on the NIR-HSI and SilviScan data from the 40 calibration samples, models were built to predict wood properties of all samples. These data were used to estimate heritabilities and trait-to-trait genetic correlations. Results from this preliminary study are encouraging and the technique can be explored on larger, multi-site, datasets.

Automated summary

Douglas-fir is the most important commercial timber species in the US PNW and has been the subject of long-term tree improvement. This study utilized NIR-HSI and SilviScan to assess wood properties of 40 samples from two progeny tests, and built predictive models to estimate wood properties and genetic parameters for all samples.