Effects of a fall rye cover crop on weeds and productivity of <i>Phaseolus</i> beans
Bibliographic record
Abstract
Fall-seeded rye (Secale cereale) is known to suppress weeds through physical and allelopathic properties. This study examined the effects of fall rye cover crops on weed and dry bean (Phaseolus vulgaris) productivity over four site–years in Manitoba. In addition to rye, we tested early versus late spring rye termination times as well as herbicide use in a factorial experiment with four replicates. In the absence of herbicides, rye reduced early-season broadleaf and grassy weed plant populations by 44%–72% and 43%–88%, respectively. Terminating rye at the four-leaf stage (∼1100 kg dry matter ha−1) provided the same level of weed suppression as termination at booting (∼3100 kg dry matter ha−1). Early rye termination increased bean plant populations (significant at three out of four sites), bean development (four out of four sites), bean biomass (two out of four sites), and bean yield (three out of four sites) compared with later termination. Lower bean yield with rye at one site–year was attributed to dry early season conditions, where rye reduced soil water content. While the rye cover crop provided multiple benefits to bean production, early termination resulted in the best agronomic outcome. Rye was beneficial to weed control even when herbicides were used.
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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".