Rates of Agricultural Gypsum in Soil Under No-tillage System With Surface Lime in the Southern of Brazil
Bibliographic record
Abstract
At the no-tillage system, the surface liming is a good environmental practice, which aims to maintain the physical structure and stocks of carbon in the soil. However, the acidity amelioration is restricted to the surface layer and the use of the gypsum can be an alternative to improve the chemical conditions in subsurface without the soil revolving. The purpose of this experiment was to evaluate the effect of different rates of gypsum, estimated by different methods, in acid soils with application of superficial limestone since the beginning of the implementation of the no-tillage system. The experiment was conduct at a commercial cropping field located at the municipally of Muitos Capões, RS, Southern Brazil in a Red Oxisol. The experiment was conducted in randomized completely blocks design (RCBD), with six treatments and four replications. The treatments consisted of the superficial application of gypsum in the rates: 0, 3100, 6014, 7875, 9750 and 12400 kg ha-1. At this area, soybean and corn were cultivated and have their productivity evaluated. After 4 and 16 months of the experiment, Ca, Mg and K levels were evaluated at different depths (0-5, 5-10, 10-20, 20-30 and 30-40 cm). The results showed an increase in Ca contents to the depth of 10 cm at 4 months after application and at all depths evaluated at 16 months after their application. The application of gypsum decreases the Mg contents to the depth of 20 cm and of potassium at all the depths after 16 months of their application. The effects on soil chemical properties with increasing rate of applied gypsum resulted in higher maize grain yield. So, agricultural gypsum applied in the soil with surface liming is efficient in improving soil chemical conditions in deeper layers in a 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 | 0.001 |
| 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 | 0.001 | 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".