Compaction and Physical Attributes of the Soil After the Development of Cover Plants
Bibliographic record
Abstract
Compaction problems in heavily tilled soils have been commonly mitigated with the use of cover plants. Aiming to evaluate the effects of compaction on the physical properties of a plyntic Haplic-Alitic Cambisol soil after development of different cover crops, a complete randomized blocks design experiment, with 3 × 3 factorial arrangement and four replications, was conducted. Treatments consisted of cultivation of two legume species, crotalaria (Crotalaria juncea L.) and stylosanthes cv. Campo Grande (Estilosantes capitata + Estilosantes macrocephala) and a grass species, brachiaria (Urochloa brizhantha cv. Marandu), subjected to soil compaction: CM–Conventional soil management (tillage) without additional compaction; CMc4 and CMc8–conventional soil management with additional compaction using a 6 Mg tractor in four and eight wheel passes. Conventional management with additional compaction does not affect significantly the physical attributes at a soil depth of 0.10-0.20 m, and only the soil moisture does not differ according to the soil management, irrespective of the depth and kind of cover plant. Traffic levels in four passes result in an increased soil bulk density and macroporosity in the 0.0-0.05 m, and in soil resistance to penetration and total porosity in the layer up to 0.10 m. Cover crops are important in maintaining soil physical quality to reduce the negative effects of compacting forces, especially to stylosanthes cv. Campo Grande that provided greater soil protection in systems with or without addition of compaction, conditioning the lowest values of bulk density and soil resistance to penetration.
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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".