Comparison of soil and corn residue cutting performance of different discs used for vertical tillage
Bibliographic record
Abstract
High amount of corn (Zea mays L.) residue left in the field interferes with seeding operations, which hinders the viability of conservation agriculture. Vertical tillage is a promising practice in dealing with heavy crop residue, but its effectiveness largely depends on the design and use of tillage machines. In this study, three vertical tillage discs with different shapes, namely notched, plain, and rippled, were tested in a soil bin at two different working depths, shallow (63.5 mm) and deep (127 mm). Corn residues were spread on top of the soil as surface residue. soil cutting forces, soil displacement, and residue mixing with soil, as well as residue cutting were measured. The results showed that the working depth had a stronger effect on the performance of discs as compared to the disc type. No difference in residue cutting was found between the treatments. The deep working depth resulted in 5.1% higher residue mixing, 53.4% greater soil cutting forces, and 34.9% larger soil displacements, as compared to the shallow depth. The rippled disc resulted in the largest soil displacements with the greatest demand in soil cutting forces. Overall, the rippled disc was the most aggressive among the three discs with regard to the performance indicators measured. The results suggested that varying working depth would be an effective approach in changing the soil dynamics and residue cutting performance of the discs for vertical tillage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".