Weed Suppression by Annual Legume Cover Crops in No‐Tillage Corn
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 it