Defoliation Regime Effects on Accumulated Season‐long Herbage Yield and Quality in Boreal Grassland
Bibliographic record
Abstract
Abstract Within boreal grasslands, little information exists on the effects of initial defoliation date, frequency, and intensity on accumulated herbage yield and quality. We investigated the effects of initial defoliation in May, June or July, at heights of 5, 10 or 15 cm, and repeated at 2‐, 4‐ or 6‐week intervals throughout the growing season. Harvested material was combined with year‐end residual biomass, and examined for herbage removed, crude protein (CP), crude protein yield (CPY) and neutral detergent fibre (NDF). Compared to single defoliated check plots, total, grass and forb dry matter (DM) yields were lower under repeated defoliation by 25, 38 and 17 %, respectively. The majority of total herbage produced was harvested in the spring clipping. Total, grass and forb DM yields peaked under moderate (10 cm) clipping. Total DM and grass biomass were maximized with long (6 week) recovery periods. In contrast, forb biomass was greatest with May defoliation followed by a 4‐week interval. While maximum grass CPY was found under 10‐cm defoliation, forb CPY peaked with early and moderate to intensive defoliation. These results indicate that season‐long herbage biomass, along with CPY, can be maximized in boreal grasslands through controlled defoliation.
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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".