Influence of Inoculant Application Methods on the Physiological Quality of Common Bean Seeds
Bibliographic record
Abstract
Common bean (Phaseolus vulgaris L.) is one of Brazil’s main crops, however, its productivity remains low. Biological nitrogen fixation (BNF) is a sustainable alternative to mineral nitrogen fertilization, reducing costs and environmental impacts. This study aimed to evaluate the physiological quality of BRS Estilo bean seeds under different inoculation strategies with Rhizobium tropici. The experiment was conducted during the 2022/2023 growing season in Anápolis-GO, using six treatments: seed inoculation, furrow inoculation, topdressing reinoculation at the V4 stage, their combinations, as well as a mineral nitrogen fertilization treatment and a control without nitrogen. The harvested seeds were subjected to germination, vigor, accelerated aging, seedling length, and dry mass tests. The results indicated that furrow inoculation combined with topdressing reinoculation at the V4 stage produced higher-quality seeds, with germination and vigor comparable to those of mineral nitrogen fertilization. Conversely, single seed inoculation was insufficient to ensure high quality seeds. It was concluded that furrow inoculation followed by topdressing reinoculation can partially or fully replace mineral nitrogen fertilization, improving nitrogen use efficiency in common bean and contributing to a more sustainable production system.
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 | 0.001 |
| 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".