Mid- and long-term effects of stock type on the growth and yield of spruce seedlings in a non-herbicide scenario
Bibliographic record
Abstract
<jats:p>Stock types used in reforestation projects can influence plantation success, as they determine the morphological attributes of the planted seedlings. They can also interact with silviculture treatments to influence early seedling survival and growth. As nurseries develop and produce new stock types in response to –and in interaction with– manager needs, research efforts must be pursued to validate early seedling performance and long-term growth and yields. In this context, we aimed to evaluate the main and interactive effects of mechanical site preparation and stock type on planted black (Picea mariana [Mill.] BSP) and white spruce (P. glauca [Moench.] Voss) seedling dimensions at 16-y, and estimate the long-term impact of stock type on the merchantable volume at rotation age for white spruce. We hence compared medium (200 cm3 root plug) and large (350 cm3 root plug) containerized seedlings, as well as large bare-root seedlings of both species, in a field experiment established in Quebec (Canada), where there is a ban on the use of chemical herbicides for vegetation management treatments. Our results confirm that there is a significant, although limited impact of stock type on the size of black and white spruce at the juvenile stage, when medium and large stock types are compared, but that these small differences have a negligible effect on the estimated merchantable volume produced at rotation age (60 years). Mechanical site preparation does not promote seedling growth on these rich sites with thin humus. Therefore, selection of a medium or larger stock type for reforestation projects and application of mechanical site preparation in ecosystems similar to the one studied here should be based on other considerations than growth and yield, such as seedling availability, production and planting costs, or operational constraints.</jats:p>
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".