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 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 | 0.000 | 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) | 0.000 | 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".