Assessing soil mechanical resistance at different depths and compactation mapping at effective root zone
Bibliographic record
Abstract
Soil compaction, a result of mechanical resistance to penetration, has a direct impact on yield potential by limiting root access to water and nutrients. Factors such as inadequate crop rotation, intensive mechanization, and trampling by animals contribute to compaction. Mitigation strategies include crop rotation, control of machinery traffic, the use of cover crops, and the use of mechanical techniques. Geostatistical methods in pedostatistics evaluate the spatial variability of soil properties. The aim of this study was to determine the penetration resistance in five soil layers (10, 20, 30, 40, and 50 cm), identify critical compaction regions, and quantify the economic impact of compaction management in a 230 ha farm in Alegrete, Brazil. A digital penetrometer was used to measure resistance and semivariograms were calculated using classical and robust estimators for interpolation. The evaluation of the economic impact took into account the variable cost differences between the total area and the area required for compaction. The analysis showed a gradual increase in compaction from the surface to the subsoil, with a highly compacted zone occurring at 20–30 cm, signaling the need for monitoring and intervention. The dependency analysis showed a well-defined structure. The results show that geostatistical tools can be used in the assessment of soil penetration resistance, especially in layers from 10 to 20 cm. An efficient identification and quantification of compacted zones within the cultivation area was achieved. This approach proves to be economically viable, especially in extensive farming, suggesting a wide application in agricultural compaction management.
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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 | 0.001 | 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".