Weed Suppression by Annual Legume Cover Crops in No‐Tillage Corn
Bibliographic record
Abstract
Cover crops often reduce density and biomass of annual weeds in no‐till cropping systems. However, cover crops that over‐winter also have the potential to reduce crop yield. Currently, there is an interest in annual medics (Medicago spp.) and other annual legumes that winter‐kill for use as cover crops in midwestern grain cropping systems. A 2‐yr study was conducted at East Lansing and the Kellogg Biological Station, Michigan, to investigate the influence of annual legume cover crops on weed populations. Two annual medic species [burr medic (M. polymorpha cv. Santiago) and barrel medic (M. truncatula Gaertn. cv. Mogul)], berseem clover (Trifolium alexandrinum L. cv. Bigbee), and medium red clover (Trifolium pratense L.) were no‐till seeded as cover crops into winter wheat (Triticum aestivum L.) stubble in a winter wheat/corn (Zea mays L.) rotation system. Density of winter annual weeds were between 41 and 78% lower following most cover crops when compared with no cover control in 2 out of 4 site years, while dry weight was between 26 and 80% lower in all 4 site years. Impact of cover crops on the density of summer annual weeds was infrequent; however, weed dry weights were reduced by 70% in 1995 following burr medic and barrel medic. Dry weight of perennial weeds before corn planting were 35 to 75% lower following annual legumes compared with the control, while weed density was not affected. This study indicated a potential for annual legumes to reduce weed density and growth in no‐till corn grain systems.
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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".