Soil Physical Attributes in Long-Term Soil Management Systems (Tillage and No-till)
Bibliographic record
Abstract
The period of implantation of the no-tillage system (NTS) is a fundamental factor to the dimension of the changes that occur to the soil's physical, chemical and biological attributes. Thus, the objective of this study was to evaluate the soil changes to the physical attributes and correlate the results to the soil organic matter in areas of different long-term soil management. The study was set as a completely randomised design, in a 4 × 4 factorial scheme, with four management systems [5 years NTS (NTS5); 17 years NTS (NTS17); conventional tillage system for 20 years (CTS20); native area (NA)], and four soil depths (0-0.05, 0.05-0.1, 0.1-0.2, 0.2-0.4 m), with five repetitions. Soil mechanical resistance to root penetration (RP), bulk density (SD), volumetric moisture (VM), macro (Ma), microporosity (Mi) and total porosity (TP), and the aggregation parameters were evaluated. The CTS20, NTS5 and NTS17 presented superior SD in the most superficial soil layers, which was not yet causing resistance to root development. The SD was the only physical attribute that correlated significantly with all the other soil attributes evaluated, indicating the importance of such attribute to evaluate soil quality to crops. The soil physical attributes found in the Cerrado native area followed the sequence of similarities: no-tillage system with 17 years (most similar), with five years and the conventional tillage system (less similar). The changes caused by the anthropic activity in the soil's physical attributes are more pronounced and perceptible in soil depths up to 0.2 m.
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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 | 0.001 | 0.001 |
| 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".