Evaluation of Agronomic Efficiency with Regional Source of Natural Potassium in the Brazilian Midwest
Bibliographic record
Abstract
Brazil plays an important role in global food production, but faces challenges due to its dependence on imported fertilizers. To reduce this vulnerability of the agricultural sector, the use of natural sources such as agrominerals, also known as rock dust, is gaining ground. The objective of this study was to characterize and evaluate a new source of natural potassium, extracted from a deposit located in the Brazilian Midwest, through geological characterization and agronomic evaluation through yield tests, soil and foliar potassium content. The tests were conducted in a greenhouse with seven treatments, two soil types and four replications, with millet, soybeans and beans in succession to evaluate the residual effect of the product. The agromineral was classified as a nepheline syenite saprolite with an average K2O content of 11.6%. The effects of the agromineral were promising, especially in the medium term. In the case of beans, at the standard dose of 60 K2O, yields in clay soils were 3.6 Mg ha-1 higher than in the 60 KCl treatment, which obtained only 2.3 Mg ha-1, probably due to absorption or leaching losses, since the effect evaluated was residual in nature. For all the crops evaluated, the agromineral showed an increase in potassium levels in both the soil and the leaves compared to the control group, indicating that it is a potential alternative to gradually reduce the use of traditional chemical fertilizers. Field trials are recommended to validate these benefits, taking into account more realistic environmental variations.
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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".