Optimizing Hardwood Reforestation in Old Fields: The Effects of Treeshelters and Environmental Factors on Tree Seedling Growth and Physiology
Bibliographic record
Abstract
Abstract Strong competition for water is largely recognized as the main factor explaining the resistance of herbaceous old fields to tree invasion. Therefore, site preparation as well as chemical and mechanical vegetation control are recommended when establishing hardwood tree plantations on such sites, but those methods are sometimes socially or ecologically inappropriate. The main objective of this study was to analyze whether treeshelters could improve early tree growth in herbaceous old fields, when mulching alone is used to partially control herbaceous competition. Our results indicate that treeshelters can facilitate tree growth in those conditions but that this was not caused by an improvement of tree water relations. Rather, it appeared to be related to an optimization of light levels inside the shelter, where light intensity was low enough to lead to a photosynthetic system less costly to maintain due to a greater specific leaf area but high enough to have no adverse effects on photosynthetic rates. Although treeshelters increased tree growth when surrounding herbaceous vegetation was low (either height or standing biomass), allowing high light levels, they reduced growth when surrounding vegetation was high and blocked a substantial quantity of light. Therefore, environmental factors such as light availability need to be considered to optimize the success of hardwood plantations when treeshelters are used in recently abandoned agricultural fields.
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 | 0.000 |
| 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".