Incorporated cover crop residue suppresses weed seed germination
Bibliographic record
Abstract
Abstract Cover crops have been shown to be important integrated weed management tools. In addition to directly competing with weeds, cover crops can provide weed suppressive effects following incorporation through release of allelopathic compounds and/or changes to nutrient availability. Incorporation of a cover crop mixture may provide a synergistic or antagonistic effect on weed suppression by further altering nutrient dynamics. To investigate this phenomenon, we evaluated the suppressive effects following incorporation of annual ryegrass, buckwheat, brown mustard, and phacelia sown with and without field pea on germination and growth of several pernicious weed species. Further, we used the additive partitioning model to determine if pea synergistically improved biomass production and weed suppression of cover crops. Our results demonstrate that following incorporation, cover crop residues suppress weed germination and weed biomass production. According to the additive partitioning model, the addition of pea had an antagonistic effect on buckwheat and brown mustard biomass production and decreased buckwheat weed suppression by 8%. In contrast, the addition of field pea greatly enhanced biomass production of phacelia at a reduced seeding rate suggesting a positive biodiversity effect. Limited evidence was found for changes to nutrient availability following cover crop incorporation, however, a dose‐dependent effect of cover crop residue on weed suppression suggests allelopathy and/or nutrient availability may have a role on weed seed germination success. Together, our results support the use of incorporated cover crop residues as an integrated weed management tool.
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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".