Emergence of Soybean and Canola Plants as Affected by Soil Compaction from a Seeder Press Roller
Bibliographic record
Abstract
Changes in soil properties under mechanical stress significantly influence the emergence and growth of crops, with different crops responding differently to these changes. To explore the impact of different soil compaction states on crop growth, field experiments were conducted in 2022 and 2023 in sandy loam soil in Manitoba, Canada. The crops (canola and soybean) were planted under three compaction levels created by a seeder’s press roller: no press roller (P0), one pass of the press roller (P1), and two passes of the press roller (P2). Soil mechanical properties and plant growth were measured for each treatment. The results indicated that soil shear strength increased significantly with each level of compaction from P0 to P1 and from P1 to P2, while soil surface resistance remained largely unaffected. Interestingly, soybean and canola responded differently to soil compaction. Soybeans showed no significant changes in emergence speed or final plant population across the three treatments. Conversely, canola exhibited over a 50% increase in emergence speed and more than a 100% increase in final plant population with either one (P1) or two passes (P2) of the press roller, compared to the no press roller (P0) treatment. These findings provide valuable guidance for agricultural producers and engineers in adjusting the down pressure of seeder press wheels when planting different crops.
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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 | 0.001 |
| 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".