Economic gain of genetically-selected coastal Douglas-fir: Timber, log and carbon value at varying planting densities
Bibliographic record
Abstract
Substantial investments in tree breeding for coastal Douglas-fir in British Columbia are projected to lead to significant volume gain at rotation age. Recent research shows growth gains are accumulating as expected, but it is less clear to what degree and when these volume gains translate into economic gains. We use discounted cash flow analysis techniques to quantify economic gains and determine optimal rotation ages expected from planting three levels of genetic gain in tree volume (a 0 % control, +10 % and + 18 %) at four initial densities (625, 1189, 1890 and 3906 stems/ha). Valuations were estimated for a variety of economic conditions for timber volume and log grades, with and without carbon pricing. These analyses rely on a growth and yield model simulating data from a 21-year coastal Douglas-fir realized gain trial, installed on five sites differing in productivity. Simulations show that planting selectively-bred coastal Douglas-fir trees reliably led to significant economic gains relative to unselected control stands, across initial planting densities, sites and varied economic scenarios. Highest financial returns are projected for genetically-selected seedlings at the most productive sites. Lower initial planting densities were associated with higher economic gains but also reduced important wood quality metrics that were not captured by the financial analyses, suggesting that operational planting densities (1189–1890 stems/ha) could offer a suitable compromise. Incorporating carbon prices led to larger economic returns and longer rotations. Altogether, these simulations suggest that a reliably higher return on investment can be achieved by deploying selectively-bred planting stock.
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 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 | 0.001 | 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".