Nutrient Cycling by Cover Species and Yield of Soybean Grains in a Clayey Oxisol Under No-Tillage System
Bibliographic record
Abstract
The objective of this work was to evaluate the impact of changes in soil management and crop rotation on a clayey Oxisol under no-tillage system in nutrient cycling and soybean yield. The experiment was conducted in the 2014/2015 harvest, at the Agronomic Institute of Parana (IAPAR), at the Experimental Station of Santa Tereza do Oeste, in a clayey Oxisol, in a completely randomized design with four replicates. The management systems evaluated were: no-tillage system (control), no-tillage system with scarification (NTSS) and no-tillage system with gypsum application (NTSG), and six treatments involving crop rotation with species reclaimers of structure: pearl millet, dwarf pigeon pea, sunn hemp, pigeon pea, rattlebox and velvet bean. The attributes/chemical characteristics of the soil were evaluated: phosphorus (P), calcium (Ca2+), magnesium (Mg2+), potassium (K+), organic carbon (C), soil acidity (pH), aluminum (Al3+), potential acidity (H++Al3+), base sum (BS), cation exchange capacity (CEC), base saturation (V), aluminum saturation (Al*) in the layers 0-0.05, 0.05-0.10, 0.10-0.20 and 0.20-0.40 m, after the application of treatments and cultivation of soil cover species. Statistical analysis was performed by analysis of variance (ANOVA) and the means of the treatments compared by the Tukey test at 5 % of significance. The pigeon pea provided higher phosphorus cycling (63.67 mg dm-3), velvet bean, larger magnesium cycling (4.25 cmolc dm-3) and higher values of organic carbon (27.67 g dm-3) in the layer of 0.05-0.10 m. The yield of grains, number of plants per meter and mass of 100 soybean grains did not present significant differences among the evaluated treatments.
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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) | 0.000 | 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".