Physical sod suppression as an alternative to herbicide use in pasture renovation with clovers
Bibliographic record
Abstract
Using herbicides for sod suppression during pasture renovation by legume sod-seeding often results in the loss of potentially usable forage, weed encroachment, and inadequate legume:grass ratios. Physical sod suppression methods could alleviate some of the problems associated with suppression via herbicide. A study was conducted in Québec, Canada, to investigate, as an alternative to herbicide, sod suppression by sheep grazing or mowing before and after spring no-till seeding of red clover (Trifolium pratense L.) or white clover (T. repens L.). Sod-suppression treatments included six physical suppression methods: mowing or sheep grazing, to 5 or 10 cm, at seeding and when the grass sward reached 30 cm during the first 2 mo of clover establishment, or similarly managed mowing or sheep grazing to 5 cm with an additional defoliation the previous fall. Additional treatments included suppression by herbicide (glyphosate [N-(Phosphonomethyl) glycine] at 2.6 kg a.i. ha–1) and two controls: sod-seeding with no sod suppression and no seeding. Among the physical suppression treatments, grazing and mowing to 5 cm resulted in highest clover densities, similar to those achieved via herbicide suppression. Red and white clover had similar plant densities. Yield components and total forage yields varied with sites. Clover yields tended to be higher with herbicide than under physical suppression treatments. However, increasing the severity of physical suppression increased clover yields. Weed encroachment was observed only with herbicide sod suppression. Unlike suppression with herbicide, physical suppression did not decrease total forage yields in the renovation and post-renovation years when compared with controls. Forage quality was increased in the renovation year by both physical suppression methods and herbicide when compared with unrenovated controls; but the increase was greater with herbicide suppression. Only the most severe of the physical suppression methods sustained increased forage quality in the year after renovation. Timely mowing or grazing as methods for suppression of grass sod during renovation with legumes appear to have potential, but cannot yet be recommended as alternatives to herbicide. Key words: Clover, forage, grazing, pasture renovation, sod-seeding
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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) | 0.000 | 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 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".