Dry Mass Increment, Foliar Nutrientes and Soybean Yield as Affected by Aminoacid Application
Bibliographic record
Abstract
Due to the importance of soybeans worldwide, there is a constant search for products or management systems that aim to increase the productivity of this crop. In this sense, some products that have amino acids in their composition have been used, however, there is still a lack of studies that aim to show the isolated effect of amino acids on growth parameters. Therefore, the present study aimed to evaluate the effect of the application of amino acids in the treatment of seeds and of the leaf in the soybean crop. Experiments were carried out in a greenhouse and in the field with the application of glutamate, phenylalanine, cysteine, glycine as a seed treatment (ST) and also as foliar application (FA) at the V4 growth stage. The dry mass accumulation of root, stem, leaves, total and yield of a soybean crop were evaluated. In addition, leaf element concentration in leaves was also evaluated. The use of phenylalanine in ST promoted the best results on stem mass, leaves, pods and total dry mass, with an increase of up to 152%, as compared to control. This same treatment led to higher productivity, with a 46% increase in relation to the control. In relation to foliar concentration of elements, the most effective application is the one that is carried out in both modes of application (ST and FA), mainly with glutamate and glycine. Therefore, the application of amino acids, especially in seed treatment promotes the greater accumulation of dry mass and productivity in soybean plants.
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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 | 0.001 |
| 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".