Crop and weed response to nutrient source, tillage and weed control method in a corn-soybean rotation
Bibliographic record
Abstract
The integration of various sustainable management techniques in cropping systems can potentially reduce agro-environmental problems, but may warrant a new approach to weed management, due to changes in weed communities. A 3-yr experiment was conducted to determine the effects and interactions of tillage (moldboard plow or chisel plow), weed control method (chemical or mechanical), and nutrient source (mineral fertilizers or liquid hog manure supplemented with mineral fertilizers) on crops and weeds in a corn (Zea mays L.)–soybean (Glycine max (L.) Merr.) rotation on two soils (a Sainte-Rosalie clay and a Duravin clay loam, Orthic Gleysols), at Saint-Hyacinthe, Québec. In most cases, weed density and biomass were more important with mechanical than with chemical control. Nutrient source did not affect weed density, but weed biomass was higher in the manure than the mineral treatment in 1997. Weed populations on both soils were greater with chisel plow tillage combined with mechanical weed control. The presence of crop residues in chisel plow systems may have hindered the efficacy of mechanical weed control operations, particularly when corn was the previous crop. Soybean populations, mid-season biomass and yields were reduced in chisel plow compared with moldboard plow tillage. Corn populations and mid-season biomass were not affected by any of the factors, whereas corn yields showed variable responses over the years. Efficacy of mechanical weed control would need to be enhanced or complemented with other weed control methods, in order to reduce the risks of weed infestations and lower crop yields, particularly in conservation tillage systems. Liquid hog manure would have little effect on weed populations, but would need to be adequately balanced in order to ensure proper crop nutrition and optimum yields. Key words: Chisel plow tillage, mechanical weed control, liquid hog manure, conservation tillage
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".