Management of Soil Acidity and Its Relations With Soybean Productivity in Brazilian Savanna
Bibliographic record
Abstract
The soils of Brazilian Savanna, naturally, present acidity problems, making correction practices fundamental to ensure production. Even with so many years since the introduction of agriculture, some soil correction practices are still misused. Thus, the objective was to evaluate soybean yield and chemical changes in a Red Oxisol in the Brazilian Savanna with the use of limestone, associated or not with gypsum, applied superficially and incorporated by harrow and moldboard plow. The experiment was conducted under field conditions, in Rio Verde-GO, cultivating soybeans in the 2015/2016 and 2016/1017 harvests. The experimental design was in randomized blocks, with four replications, with treatments arranged in a 3 × 3 factorial scheme. The first factor refers to the application of superficial limestone and incorporated by harrow and moldboard plow. The second factor was the presence or absence of limestone and/or gypsum (0 + 0; 0.875 + 0 and 0.875 + 1.75 t ha-1). Plant height, number of pods per plant, productivity, pH, Al, CTC, Ca, Mg and V were evaluated, in soil depths of 0-0.2 and 0.2-0.4 m. In the 2016/2017 harvest, the characteristics of pH, CTC, V and contents of Ca, Mg and Al were influenced by the methods of application of limestone and by its use, associated or not with gypsum. There was an increase in pH on the soil surface with the use of limestone and on the subsurface with the use of limestone and gypsum. The moldboard plow provided increases in the contents of Ca and Mg in the soil in comparison to surface application. The moldboard plow incresed in soybean yield, in the second crop, with and without association of gypsum with limestone.
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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 | 0.000 |
| 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".