Alternative Sources of Potassium for Soybean Crops
Bibliographic record
Abstract
Over the last three decades, soybean production in the Brazilian agricultural sector has experienced significant yield growth. The soil must have adequate fertility to provide nutrients for good agricultural yield. This study aimed to evaluate the effect of different sources and doses of potassium on the soybean grain yield and its components. The experiment was conducted in the 2022/2023 crop season at the experimental field of the Goiás State University in Ipameri, GO. The experimental design used was randomized blocks arranged in a 3 x 5 factorial scheme, with three potassium sources potassium chloride (KCl: 58% K2O), Phonolite (Ph1:8% K2O and 25% Si), and Hydrothermalized Phonolite (HPh2:12% K2O and 25% Si) and five doses (0, 50, 100, 150, and 200 kg ha-1), with four replications. The management used the no-till system on sorghum straw planting the NEO 750 IPRO soybean cultivar. According to the results, there was an influence of the interaction between the factors only on the number of pods per plant. The potassium doses influenced the first pod insertion height, hectoliter mass, and grain yield. Based on the results, potassium fertilization using the alternative sources, Ph1 and HPh2, reached satisfactory grain yield levels compared to KCl, with the maximum grain yield value, regardless of the source, occurring at a dose of 138.18 kg ha-1 of potassium.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".