Soil Physical Quality After 21 Years of Cultivation in a Brazilian Cerrado Latosol
Bibliographic record
Abstract
Long-term studies aiming soil quality evaluation under different soil management strategies are no common. Long-term evaluations provided more reliable contributions to decision-making and practices adoption. This study evaluated the soil physical quality of a Brazilian Cerrado Latosol after 21 years of three different soil management strategies: disc plowing (DP), no-tillage (NT), and disc harrowing+subsoiling (DHS). In comparison to the reference, a soil from a native Cerrado area, the removal of the original vegetation and the implementation of the three soil management strategies increased the soil bulk density (Bd) and reduced soil porosity, macroporosity, soil organic carbon (SOC) and the size of water-stable aggregates, but did not change the glomalin-related soil protein (GRSP) contents and clay flocculation. Similar effects were diagnosed on soil physical quality when is considered only the three different management strategies, especially on soil porosity, Bd, size of water-stable aggregates, SOC and GRSP contents. Strategies of DP and NT increased soil resistance to penetration in the superficial layers, while the annual use of DHS reduced this soil mechanical characteristic. The NT system did not provide increasing of soil organic carbon in comparison to other management practices evaluated. In conclusion, removing the native vegetation affected soil physical quality, but the Brazilian Cerrado soil is resilient to physical damage even when different intensive farming practices are implemented for more than two decades. The limitation of the NT system in improving the soil physical quality is related to climate conditions that determine the non-maintenance of straw on the soil surface.
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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 | 0.001 |
| 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 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".