Additive Positive Effects of Pollination on Bt and Non-Bt Soybean Cultivars by Honey Bee and Native Flower-Visiting
Bibliographic record
Abstract
In addition to the increase in the number of pods produced, a well-conducted pollination also contributes to an increase in the number of grains per pod, improves the quality of the grains and seeds, and renders the grain ripening more uniform, thus, increasing the production at harvest. The objective of the present study was to quantify the benefits of flower-visiting insects in soybean production. The experiment was conducted with two soybean cultivars, one Bt and one non-Bt. During the flowering period, 150 plants of each cultivar were randomly selected, yielding 25 replicates (three plants per replicate) with free access to flower visitors, and another 25 replicates (also with three plants per replicate) that flower visitors could not access. During the flowering period, 549 specimens of flower-visiting insects were found in both cultivars, divided into eight orders, 30 families, and 92 species. The most abundant species were Apis mellifera (Linnaeus) (Hymenoptera: Apidae), Musca sp.1 (Linnaeus) (Diptera: Muscidae), and Lagria villosa (Fabricius) (Coleoptera: Tenebrionidae). In the treatment with flower visitors, grain weight increased by 84.22% in Bt cultivar and by 202.52% in non-Bt cultivar, compared with the area without the presence of flower visitors. The increase in the number of pods in Bt and non-Bt cultivars was 45.72% and 101.25% respectively, in the area open to flower visitors. The high increase in grain yield and number of pods observed in the area with free access to pollinator insects emphasizes the high importance of the pollination service performed by flower visitors to the soybean crop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".