Monitoring corn and soybean agroecosystems after establishing no-tillage practices in Québec, Canada
Bibliographic record
Abstract
The conversion to no-tillage (NT) may seem risky to some producers who rely on tillage to control weeds, some insect pests and disease-causing pathogens that can reduce crop yield. Weeds, arthropods, and disease incidence were monitored in silage corn (Zea mays L.) and soybean (Glycine max L. Merr.) agroecosystems with CT and newly established NT plots in Ste-Anne-de-Bellevue, Québec. During the first 2 establishment years, there were more annual grass and fewer annual broadleaf weeds in NT than in CT plots, but the surface area covered by weeds (broadleaf,grasses and perennials) was greater in CT than NT plots. Foliar arthropods were more numerous in soybean than corn plots in both years, but were unaffected by tillage. There were more ground-dwelling generalist predators, especially Coleoptera and Carabidae, in CT than NT plots, while spiders and harvestmen (Araneae and Opiliones) were dominant in NT plots. Crop damage from insect pests and diseases was below economic thresholds, but 5 yr of yield monitoring (2000–2004) revealed a trend of greater silage corn and soybean yields in CT than NT plots. The reduction in crop yield after establishing NT practices at this site was probably due to rooting constraints from inadequate seedbed preparation in the NT system, rather than from weed competition, insect damage or crop diseases. Key words: Arthropods, crop disease, silage corn, soybean, tillage, weed control
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".