Conventional versus genomic selection for white spruce improvement: a comparison of costs and benefits of plantations on Quebec public lands
Bibliographic record
Abstract
Abstract Intensive plantation forestry is a potent strategy for forest managers to increase wood production on a smaller forest land acreage, especially with the use of genetically improved reforestation stock. The main drawback with conventional conifer improvement is the several decades it takes before stock deployment, which is particularly acute in the context of climate change and evolving wood markets. Use of genomic selection allows to drastically shorten breeding cycles, resulting in more flexibility and potentially increasing benefits. This study compares the financial performance of five white spruce (Picea glauca) breeding and deployment scenarios, from conventional breeding to genomic selection in conjunction with top-grafting or the use of somatic embryogenesis, in the context of plantations established by the Quebec government on public lands with banned herbicide use. We estimated the land expectation value (LEV) for the five scenarios applied to eight site productivity indices, and considered costs and revenues from breeding, plantation establishment, silviculture, and harvest. LEVs at 4% discount rate were positive for all scenarios on all site indices, and varied from $197 to $2015 ha−1assuming mechanical brushing of the plantations. The scenarios integrating genomic selection resulted in the highest LEVs, which increased with site index. We also conducted sensitivity analyses with 3% and 5% discount rates, with a range of costs and benefits, and with herbicide control of competing vegetation. These results should help orientate public investment decisions regarding the integration of genomic selection at the operational level in tree breeding and reforestation programs on public lands.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".