Tree breeding and silviculture: Douglas-fir volume gains with minimal wood quality loss under variable planting densities
Bibliographic record
Abstract
Validating performance of genetically-selected trees under realistic planting scenarios is essential for confidence in tree breeding programs. Quantifying the relative impact of genetic selection and initial planting density on tree size and quality can further guide operational forest practices. We evaluate volume gains, survival, stem quality and wood quality traits on 20-year old trees representing three levels of genetic selection that were grown under four initial planting densities. Working in a realized gain trial for coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) on five replicated sites, we ask: (1) Do predicted stand productivity levels materialize as expected under different planting densities? (2) Are single-tree plot designs, simulating progeny trials, capable of producing reliable results relative to large-block designs, simulating realistic planting scenarios? (3) If trees selected for volume gain show declines in wood and stem quality relative to wild-stand controls, can this be effectively managed by altering stocking densities? Because young progeny trials are used to estimate genetic gain in tree volume at a rotation age of 60, we use a growth and yield model calibrated to wild-stand controls to assess whether genetically-selected families meet projections at age 20. On average, observed stand volumes exceeded projections on four out of five sites, and in three out of four initial planting densities. At the level of site by planting density, the moderate genetic-gain population (mid-gain) exceeded projections 13 out of 20 times while the top genetic-gain population (top-crosses) exceeded projections 11 out of 20 times. This fits expectations for breeding values, which are designed to reflect general performance averaged across all environments. Using a different validation approach, large-block designs showed better performance relative to simulated progeny trial designs. Very high planting densities (1890+ stems/ha) may minimize wood quality losses of genetically-selected planting stock but effects are relatively minor, while good performance among all traits was observed at operational planting densities (~1189 stems/ha). Wood density and microfibril angle proxy measures in top-crosses showed relatively minor and non-significant losses (−1.1 to −4.0%) compared to major and significant gains in volume per hectare at age 20 (29.0%) when averaging values at 1189 and 1890 stems/ha. Altogether, the genetic selection systems produce reliable results across a range of site qualities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".