Doses and Application Seasons of Potassium in the Soybean-Corn Succession in Soil With Improved Fertility in the Southwest of Goiás
Bibliographic record
Abstract
Soils with improved fertility indicate opportunities for more rational use of fertilizers. The objective of this study was to evaluate the management of potassium fertilization in the succession of soybean-corn in soil with improved fertility, in the southwestern region of the state, Goiás. The experiment was set in 5×3 factorial scheme, arranged in randomized blocks with four repetitions. The treatments consisted of the combination of potassium doses (0, 40, 80, 120 and 160 kg ha-1 of K2O) and seasons of application (100% of the dose in pre-planting, 100% of the dose in coverage and in installments with 50% of the dose in pre-planting + 50% in coverage). The best performance of soybean, considering grain yield, was obtained with the parceled application of 80 kg ha-1 of K2O, with production of 3.6 Mg ha-1. The highest corn production was obtained with the anticipated application of 160 kg ha-1 of K2O in soybean. In the management of potassium fertilization in improved fertility soil in the soybean-corn succession, the parceled application of 120 kg ha-1 of K2O kept the available K reserve in the soil constant when compared to its initial content.
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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".