Defoliation Levels Supported in Soybean Crop With No Harm on Productivity in the Municipality of Parauapebas
Bibliographic record
Abstract
The reduction in the leaf area is one of the causes in the fall in soybean (Glycine max) productivity as it depends on the production of photoassimilates generated by the leaves, so any factor that interferes in its leaf area may affect the production. The attack of defoliating insects is among such factors. They cause a marked drop in grain yield due to its direct action, therefore, reducing the leaf area, consequently reducing the photosynthetic rate of the plant. The agronomic characteristics of the cultivars may interfere on the level of tolerance of the plant to this type of stress. The objective of this study was to evaluate the influence of defoliation levels on the vegetative and reproductive stages on the development and yield of grains in soybean cultivars. The experimental design was in randomized blocks, in a 2×11×2 factorial scheme, with four replicates. Factors consisted of defoliation stage (vegetative and reproductive), treatment levels (T1-control plant and ten treatments of artificial defoliation) and soybean cultivars (BRS 9090 RR and BRS 8890 RR). The following variables were evaluated: grain yield, dry mass of the pod, leaf dry mass, stem and root dry mass, plant height, stem diameter, number of leaves per plant, length and width of roots. It was observed that the defoliation had a negative effect on the productivity components of the cultivars, with the highest decrease in the reproductive stage, except for the treatment R5, 100% defoliation at the R5 stage, which was also reduced. In relation to the cultivars, the BRS 8890 RR was 27% better in grain yield in relation to BRS 9090 RR.
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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".