Simulation of the micro-dynamics in soil–cornstalk–disc interactions using the discrete element method
Bibliographic record
Abstract
ABSTRACT Understanding the interactions between soil, crop residue, and tillage tools is essential for improving tillage quality in conservation agriculture. This study developed a discrete element model to simulate soil–cornstalk–disc interactions, replicating the cutting of corn stalks by a concave disc in sandy loam soil. A soil bin experiment was conducted using a notched concave disc to cut corn stalks, measuring draft forces, vertical forces, and corn residue cutting effectiveness. Experimental data were used to calibrate and validate the model. After validation, the model was applied to analyse the micro-dynamic behaviours of corn stalks and soil under varying soil bulk densities and disc types. Three key parameters calibrated for the corn stalk model were 2.0 × 10⁹ N m⁻¹ for particle normal stiffness, 8.0 × 10⁶ Pa for bond tensile strength, and 0.5 for particle friction coefficient. The model effectively predicted corn stalk cutting effectiveness, draft force, and vertical force, with an overall relative error of 16.4%. Micro-dynamic analysis showed that as soil bulk density decreased (within a range of 1.06 to 1.52 Mg m⁻³), corn stalk sinkage and soil supporting force increased, while corn stalk intrusion force decreased. Compared to the notched disc, the plain disc exhibited greater corn stalk sinkage and soil supporting force but lower corn stalk intrusion force. This research provides a validated model for simulating soil–cornstalk–disc interactions, enhancing the understanding of disc performance in cutting crop residue.
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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 | 0.001 |
| 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".