Plant response to mechanical resistance and air-filled porosity of soils under conventional and no-tillage system
Bibliographic record
Abstract
Roots may respond to restrictive soil physical conditions and send signals to shoots to control plant growth. Soil mechanical resistance and aeration can be managed to improve the soil physical conditions for plant growth by using different tillage systems. The objective of this study was to quantify the influence of no-tillage and conventional-tillage systems on plant response to soil mechanical resistance and aeration. The study was carried out on a farm, cultivated with corn, with a side-by-side comparison of no-tillage and conventional-tillage systems. Thirty-two paired sampling sites were located along two transects, located one in each treatment. Soil water content, bulk density, and plant growth were measured in each treatment. Based on the soil water and bulk density measurements, the air-filled porosity values were computed for each treatment. Soil water contents and bulk density values were converted to soil mechanical resistance by using the soil resistance curve. Plant growth varied positively with soil air-filled porosity, and negatively with soil mechanical resistance in both tillage systems. However, the decrease rates/increase rates were dependent on the tillage system. The no-tillage system somehow improved the soil physical conditions for the plants, especially when they were more restrictive, allowing them to attain greater values of growth.
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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 | 0.000 |
| 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".