Modelling and measurements of soil dynamic attributes resulting from machine-induced soil compaction
Bibliographic record
Abstract
Machine-induced soil compaction is a form of soil degradation that dictates the effectiveness of numerous on-field agricultural operations and establishment of crops. Understanding the characteristic behaviour of soil compaction resulting from machinery is a strenuous task to accomplish due to the nonhomogeneous nature of agricultural soil. The aim of this study was to investigate soil-wheel and soil-plant interactions for varying soil conditions, through modelling and measurements. The combined effects of field traffic and different tillage systems on plant growth was also investigated. Soil-wheel (press-wheel of seeder) and soil-tire (tire of tractor) interaction were simulated using the discrete element methods (DEM). Experiments were performed using sandy loam soil under field or laboratory conditions for validating the DEM models. With low overall relative mean errors, the soil-wheel model adequately predicted the sinkage, rolling resistance and average peak soil stresses for various soil moisture content levels. The soil-tire interaction model predicted the maximum soil pressure for one and two passes of tire accurately but it was least accurate for three to eight passes. The soil-tire interaction model also adequately predicted soil sinkage and rolling resistance for the same number of tire passes in the experiment. In terms of soil-seedling interaction, linear regression models developed from experimental data predicted seedling emergence forces adequately under various soil compaction levels. The predicted seedling emergence forces had an increasing linear trend with the increase in soil compaction level. And lastly, field traffic negatively affected the soil physical properties, tilled using disc and spring tine implements, thereby resulting in a reduction of plant (canola) population density. The results acquired from this study can aid in selection of machine parameters, managing field operations to minimize machine-induced soil compaction and improve crop development and growth.
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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".