Pre-emergence mechanical weed control in field pea (<i>Pisum sativum</i> L.)
Bibliographic record
Abstract
Producers who wish to adopt organic or pesticide-free crop production are seeking alternatives to herbicides. A field experiment was conducted at Scott, SK, from 1999 to 2001 to determine the combination of seeding date (early-May, mid-May, and late-May) and weed management system (shallow-seeded untreated, shallow-seeded with pre-emergence tillage, shallow-seeded herbicide, deep-seeded untreated, deep-seeded with pre-emergence tillage, and deep-seeded herbicide) that would optimize yield of field pea grown without the use of herbicides. The shallow and deep seeded pre-emergence tillage treatments were a 2.5-cm seeding depth followed by two sequential harrow passes and a 7.5-cm seeding depth followed by two sequential rod-weeder passes, respectively. None of the weed management systems had a significant effect on field pea density. The herbicide treatments were the only treatments that had a significant effect on weed density as the application timing was more synchronous with weed emergence than were the tillage treatments. The pre-emergence tillage treatments reduced weed biomass when seeding was delayed until mid-May following weed emergence. Deep seeding followed by pre-emergence rod-weeding was more efficacious in reducing weed fresh weight than shallow seeding followed by pre-emergence harrowing; however, they both provided similar yield responses at the mid-May seeding date. Delayed seeding until late May resulted in a 30 to 35% reduction in field pea yield compared with early or mid-May seeding, respectively. Early-seeded field peas followed by an in-crop herbicide application resulted in the highest field pea yields with the best seeding date by pre-emergence tillage combination achieving 81% of these yields. A strategy for field pea producers who choose not to use herbicides would be to delay seeding until some weeds emerge and seed at a 7.5-cm depth followed by two sequential rod-weeding passes prior to crop emergence. Key words: Field pea, seeding date, seed depth, pre-emergence tillage
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".