Effect of tillage on soil mechanical parameters, fine root growth, and productivity of young <i>Pinus taeda</i> plantations
Bibliographic record
Abstract
The use of heavy machinery during forest establishment and/or harvest can contribute to limiting the productivity of Pinus taeda plantations, due to possible structural damage or soil compaction. However, the impact of tillage-induced changes in the soil mechanical properties on fine roots growth and shoots in P. taeda plantations remains unclear. Thus, we aimed to evaluate the effects of tillage on development and spatial distribution of P. taeda root systems in southern Brazil. Thus, we compared three tillage methods in a P. taeda plantation: no tillage, manual tillage, and mechanized tillage. Soil penetration resistance, area and diameter of fine roots, fine root length density across various soil layers (0–5, 5–10, 10–15, 15–20, and 20–40 cm), and height, diameter, and stem volume were assessed. Mechanized tillage improved the soil’s mechanical conditions, reducing compaction and favoring root growth (≈150%), increasing the absorption of water and nutrients and the productivity of P. taeda. In contrast, the no-tillage showed severe compaction, limiting root development. Manual tillage had less of an effect, especially in deep layers. Thus, mechanical tillage is essential to optimize growth and productivity, especially in compacted soils or areas undergoing forest reform.
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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 | 0.000 | 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) | 0.001 | 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".