Corn Residue Management Strategies to Improve Soybean Yield in Northern Climates
Bibliographic record
Abstract
Many soybean [Glycine max (Merr.)] growers in northern climates are reverting back to some tillage based on perceptions that increasing corn residues interfere with no‐till (NT) soybean performance. Field trials were established in southern Ontario, Canada, to investigate the impact of corn residues on soybean among seven tillage strategies (NT, stalk chop, vertical tillage (VT) twice in the fall, fall and spring VT, fall disc plus spring cultivate, fall disc plus fall cultivate, and fall plowed plus spring cultivate), three corn residue removal treatments (none, intermediate, and nearly complete), and two planters (row‐unit and drill). Overall, soybean yields were not different between NT and plowed systems, despite delayed development, and cooler/wetter seedbeds where corn residue was not removed. Shallow tillage after corn harvest did not increase yields from NT alone. Removal of corn residue did not increase soybean yields when averaged across tillage systems. However, NT yield was lowered by 0.36 Mg ha−1 when corn stalks were chopped in the fall, but only in the drill‐planted treatments. This result was significant since many farmers have purchased corn combine heads that chop stalks in an attempt to manage residue. Soybean planted with a row‐unit planter yielded 0.13 Mg ha−1 higher compared to a drill when averaged across tillage‐residue treatments; differences between planters were higher when contending with high amounts of corn residue or an uneven soil surface at planting. Our results show that shallow tillage and/or physically removing corn residue did not improve soybean yield compared to NT alone.
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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 | 0.000 |
| 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".