Extreme Grain-Based Cropping Systems: When Herbicide-Free Weed Management Meets Conservation Tillage in Northern Climates
Bibliographic record
Abstract
The challenges associated with the adoption of conservation tillage and/or low-input cropping systems, whether organic or herbicide-free, across Canada are shaped by scale, environment, and local practices. A study in eastern Canada captured the challenges of introducing low-input cropping systems in mature (20+ yr) tillage treatments in a barley/red clover/corn/soybean rotation. Each mature tillage system came with its own weed problems, but this did not affect crops such as barley and red clover, which produced similar yields across high and low input systems. However, some form of primary tillage was needed to achieve adequate weed control and yield in organic (ORG) and herbicide-free (HF) systems. The HF and ORG systems with no-till actually failed to produce a corn crop but produced soybean yields that were half or less than that for other treatments. The successful combination of conservation tillage practices and low-input systems in eastern Canada would thus appear to be crop-specific, being easier to achieve in competitive cereal crops. In western Canadian organic agriculture, tillage is practiced with low-disturbance chisel plows instead of inversion plows. However, green manure crops (summer cover crops) are often terminated with tandem discs. Both roller crimpers and mowing can successfully kill annual green manure crops such as field pea and rye, and usually result in reduced weed growth following termination. However, the lack of tillage can result in lower crop yields in wheat following green manure terminated by roller crimping compared to tillage. Challenges for no-till organic practices include perennial weed control and soil fertility. Overall, flexible crop production programs such as the former Manitoba Pesticide Free Production program and the “Agriculture raisonnéeTM” program in Québec are more likely to promote sustained environmental, economic, and social prosperity than rigid adherence to organic or no-till practices.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".