Biostimulants Increase Soybean Productivity in the Absence and Presence of Water Deficit in Southern Brazil
Bibliographic record
Abstract
Biostimulants offer a potentially novel approach for the regulation/modification of physiological processes in plants to stimulate growth, to mitigate stress-induced limitations, and to increase yield. The objective of this work was to evaluate the influence of vegetable biostimulants in soybean crop subjected to different soil water conditions. The experiments were carried out in 2017/2018 and 2018/2019, in a completely randomized design (water deficit, combination of biostimulants, and application time). The combinations of biostimulants and time of application were: no combination (control); foliar application at stage V5; foliar application stages V5 and R1; seed treatment; seed treatment and V5 applications; and seed treatment, V5 and R1 applications. All the biostimulant combinations were moreover subject to either the presence or absence of water stress. Evaluations performed were maximum photochemical efficiency, pods per plant, seeds per pod, thousand grain mass, productivity, and incremental increases in performance of each biostimulant treatment. No differences were observed under water deficit in either season, and the use of biostimulants increased the thousand grain mass and final productivity. After two crop seasons with results in increasing yield, the application of biostimulants is recommended in three stages (TS + V5 + R1) for the best management of soybean crops.
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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) | 0.001 | 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".