Multi-trait selection for improved solid wood physical and flexural properties in white spruce
Bibliographic record
Abstract
Abstract Commercial production of high-quality lumber for Nordic conifers is negatively impacted by long rotation age and adverse negative correlations between growth and wood quality traits. A prospective solution to ensure sufficient fibre quality from future plantations is to identify key wood traits for desired applications and to consider them in tree breeding programs. In this study, we used the widespread and largely reforested white spruce (Picea glauca [Moench] Voss) in Canada to investigate the genetic control of wood flexural properties such as stiffness, i.e. modulus of elasticity (MOE), and strength, i.e. modulus of rupture (MOR). We also looked at their phenotypic and genetic correlations with other wood quality and growth traits to assess the efficiency of indirect methods of selection to improve wood flexural properties in the context of multi-trait selection in tree breeding programs. To achieve this, standardized solid wood samples, growth records and standing tree wood quality traits were collected from 289 trees belonging to 38 white spruce families from a polycross genetic trial established on two different sites in the province of Quebec, Canada. Flexural stiffness and strength, height, diameter at breast height (DBH) and wood density showed moderate to high heritability. Flexural stiffness was also positively correlated at the genetic level with flexural strength, average wood density and acoustic velocity as an indirect measure of dynamic MOE (${r}_{\mathrm{G}}$ = 0.99, ${r}_{\mathrm{G}}$ = 0.78 and ${r}_{\mathrm{G}}$= 0.78, respectively). When selecting the top 5 per cent of the trees, the expected genetic gains varied from 3.6 per cent for acoustic velocity to 16.5 per cent for MOE. Selection based on wood density and acoustic velocity would result in considerable genetic gains in flexural stiffness. Several multi-trait selection scenarios were tested to investigate the genetic gains obtained from selecting with different combinations of growth and wood quality traits. The results showed that indirect selection for wood flexural properties by means of acoustic velocity and wood density are efficient methods that can be combined in operational white spruce breeding programs to increase simultaneously genetic gains for growth and wood flexural properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".