Rekal Remineralizer as an Alternative to Potassium Fertilization in Soybean Cultivation
Bibliographic record
Abstract
The agricultural sector seeks regional solutions to reduce environmental impact and to develop practices that consider the assumptions of the circular bioeconomy, in addition, to meeting the objectives set out in the 2030 agenda of the United Nations. In this context, Brazil leads research with soil remineralizers to contribute to regenerative agriculture and to have greater sovereignty in the use of natural inputs. This study aimed to evaluate the efficiency of the remineralizer of Rekal soils in soybean commercial areas in the State of Goiás, verifying the availability and mobility of potassium in soils compared to using soluble source (KCl). The methodology adopted was the application of doses of a Rekal remineralizer in four commercial areas in the municipalities of Santa Rita do Novo Destino/GO, Mimoso de Goiás/GO, and Niquelândia/GO, in rainfed and irrigated systems. The experimental design was the same for all experimental fields, randomized blocks made with four experimental treatments and four replications. The treatments were constituted of the farm standard using KCl (potassium chloride), as the only supplier of K2O demand, and three different doses of a Rekal remineralizer of soils. The parameters evaluated were the determination of the residual potassium content in the soil at three depths (0 to 10 cm, 0 to 20 cm, and 20 to 40 cm); determination of potassium leaf concentrations; determination of potassium concentrations in the grains; yield and weight of one thousand grains (PMG). The remineralizer of Rekal soils did not show significant differences, compared to the use of KCl (potassium chloride), in the variables of potassium content (soil, leaf, and grain) productivity and PMG in different productive environments in a commercial study of soybean cultivation in the Cerrado Goiano.
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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".