DEM modelling of crop residue and soil dynamics as affected by the tillage direction of a disc harrow
Bibliographic record
Abstract
A comprehensive understanding of crop residue and soil dynamics under tillage is essential for improving the operational efficiency of tillage implements. In this study, a tandem disc harrow and its interaction with wheat-residue in a sandy loam soil were modelled using the discrete element method. The model was able to simulate two tillage directions: perpendicular and parallel to standing wheat-stubble rows (Perp-Direction and Para-Direction). The model was validated using field measured data. Field experiments showed that Perp-Direction produced significantly lower residue cover than Para-Direction (P < 0.05). Both tillage directions resulted in similar stubble forward displacements (mean: 354 mm). The DEM model predicted these variables with an overall relative error of 19.7 %. Simulations further revealed that stubble trajectories and soil cutting forces varied among individual discs due to the tandem configuration of the harrow. These variables were also influenced by operational parameters: disc angle, harrow travelling speed, and working depth. Reducing these operational parameters decreased soil surface roughness in both tillage directions. Conversely, increasing these operational parameters required higher total draft forces and disturbed a larger soil area. However, the soil cutting efficiency, a performance index combining both total draft force and soil disturbance area, declined from 95.9 to 37.3 m 3 MJ −1 under these conditions. Overall, these findings provide useful guidance for optimising tillage direction and operational parameters of tandem disc harrows to enhance field performance. • A DEM model of a tandem disc harrow was developed and validated with field data. • The model simulated soil and wheat stubble under two tillage directions (TD). • TD affected residue cover but minimally influenced stubble displacement. • Larger disc angle, working speed and depth reduced soil cutting efficiency.
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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 | 0.001 | 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".