Soybean Yield, Soil Porosity and Soil Penetration Resistance under Mechanical Scarification in No-Tillage System
Bibliographic record
Abstract
Technological development has triggered a steady increase in Brazilian agricultural production, but also brought problems due to the excessive land use. The lack of care with proper management practices has led to soil physical degradation, mainly the formation of impermeable layers, which can lead to a reverse effect, a reduction in crop yield. It can be potentiated in silage production and with the lack of cover crops. To minimize the negative impacts of soil compaction, scarification is recommended, but its effectiveness has been questioned in no-tillage system. Thus, an experimental field was implemented in Brazil in 2015-2016 season, to evaluate the mechanical scarification on soybean production in succession to silage and grain corn intercropped with Brachiaria (Urochloa ruziziensis) as well as the physical properties of the soil. The experimental layout was a complete randomized block design with four replications. The plots were composed of second crop maize (autumn) intercropped with brachiaria, in two systems: silage and dry grains. The subplots were composed of three management system: no-tillage, reduced tillage cultivation with Terrus scarifier and Fox scarifier. Corn harvesting systems as well the scarifiers use did not affect soybean production and its yield components. The use of scarifiers reduced soil coverage, plant population, and soil penetration resistance. The data suggest that there was no persistence in the benefits presented by scarification. Soybean was able to break through the compacted layers, even above the critical level, corroborating with the hypothesis that the use of scarifiers does not bring benefits in no-tillage system.
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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".