An assessment of reduced herbicide and fertilizer inputs on cereal grain yield and weed growth
Bibliographic record
Abstract
Abstract Although crop production and weed growth could change if herbicides and fertilizer inputs were reduced, the short-term impact in an annual cropping system in the Northern Great Plains is not well understood. Data were collected from 14 sites in Saskatchewan, Canada, to investigate the influence of weed control method (cultural vs. herbicides) and N and P fertilizers on crop yield of fall rye, spring wheat, and barley, and the presence and number of weed species. Cultural weed control included 25% greater crop seeding rate, preseeding tillage closer to the time of seeding, and fertilizer N banding in closer proximity to the seed. Four weed species (wild oat, lambsquarters, wild buckwheat, and field penny cress) occurred more frequently in plots with cultural weed control compared with herbicide weed control for all cereal crops. However, straw and grain yields of all crops were unaffected by weed control method at all sites. The addition of fertilizer had a major impact on crop growth and some weed species. Green foxtail occurred more often in unfertilized compared with fertilized plots for all cereal crops. Straw and grain yields of all cereal crops were higher in fertilized compared with unfertilized plots. Crop yield response to fertilizer inputs was not consistent among sites for the three cereal crops. Producers making drastic reductions in fertilizer inputs may experience reductions in crop yields because of limited nutrient levels. However, the results indicate that herbicide inputs could be reduced or eliminated periodically with no short-term yield loss in cereal cropping 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 | 0.001 |
| 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".