Doses and Times of Trinexapac-ethyl Application in Soybean Development and Yield
Bibliographic record
Abstract
The intense vegetative growth is one of the problems faced by soybean producers, causing the lodging, making difficult and damaging the harvest and, even, the efficiency of the application of agricultural defenses. The use of growth regulators is an important tool in the management of growth, in addition, it can favor the transport of photoassimilates for grain filling. Therefore, the objective was to evaluate the best dose and time of trinexapac-ethyl application in the development and yield of soybean varieties. The experiment was conducted in the Selvíria County, Brazil (20°20′53″ S, 51°24′02″ W). The experimental design was used in randomized blocks with treatments arranged in a factorial scheme 5 x 3 (doses x times), with four replications. The varieties used were the BRS Valiosa RR and the BMX Potência RR, the doses of the growth regulator were 0; 50; 100; 200 and 400 g ha-1 of trinexapac-ethyl (Moddus®) and the application times were at the phenological stages V7, V10 and R2. The varieties BRS Valiosa and BMX Potência, respectively, were obtained at the phenological stage V10, with application of 200 g ha-1 of trinexapac-ethyl, greater dry matter accumulation of leaves and pods. Nevertheless, the same did not act on the foliar area of the BRS Valiosa RR variety in the same way that it did not induce changes in the production components of the BMX Potência RR variety. Regardless of the application period (V7, V10 and R2) and the dose (up to 400 g ha-1), the application of trinexapac-ethyl was not a management capable of influencing the agronomic characteristics and the lodging of the plants in both varieties.
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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) | 0.002 | 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".