Soil Physical Quality in Sugarcane Field Under Cover Crop and Different Soil Tillage Systems
Bibliographic record
Abstract
Currently, the management practices employed in Brazilian sugarcane plantations have contribute to soil physical degradation and, few studies considering the effect of cover crop associated with conservationist soil tillage systems to control or even reverse this process. Therefore, with the aim to assess the impact of cover crop and tillage systems on the least limiting water range (LLWR) and the S index in two soils of different textures used for sugarcane production, a fieldwork was carried out in two sugarcane plantations in the state of São Paulo, Brazil. The experimental design is a split-plot with four repetitions. The main factor consisted of soil cover vegetation: cover crop and fallow, and the second factor, the tillage system: minimum tillage and conventional tillage. The data of this study demonstrated that clayey and medium-textured soil are sensitive to the management systems used. The use of cover crop promoted an increase of LLWR (average incremental rate of 105% for clayey and 100% for medium-textured soil) and S index (average incremental rate of 16% for clayey and 10% for medium-textured soil). The maintenance of soil under fallow represented restrictive conditions for the growth/development of the plants due to the degradation of the soil structure. In addition, conservation management systems, such as minimum tillage, resulted in better soil physical quality when associated with cover crop. Finally, the clayey and medium-textured soil, show good S index during the first cycle of sugarcane cultivation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".