Influence of Rhizosphere-Isolated Indigenous Bacteria on Growth and Yield of Soybean (Glycine max L.) Devon 2 Varieties in Mugarsari Land
Bibliographic record
Abstract
Indigenous bacteria thriving in natural environments can serve as an alternative to biofertilizers in promoting plant growth.This study aimed to isolate and quantify the abundance of bacteria from the rhizospheres of calopo (Calopogonium mucunoides), reeds (Imperata cylindrica), and kirinyuh (Eupatorium odoratum) in Mugarsari land.Furthermore, the effects of applying isolated nitrogen-fixing bacteria, phosphatesolubilizing bacteria, and organic matter-decomposing bacteria on the growth and yield of soybean (Glycine max L.) Devon 2 varieties were investigated.A randomized block design with five treatments and five replications was employed.Results revealed a diverse range and abundance of bacteria isolated from calopo plants, reeds, and kirinyuh in the Mugarsari land rhizosphere.Bacterial inoculation significantly influenced the number of leaves, total chlorophyll content, the total number of effective root nodules, and the shoot/root ratio in soybean plants.However, plant height, leaf area, root length, the total number of ineffective root nodules, wet weight of plant biomass, number of pods per plant, number of seeds per plant, weight of seeds per plant, and weight of 100 dried seeds were not significantly affected.This study highlights the potential of indigenous bacteria as an eco-friendly alternative in enhancing soybean growth and yield in Mugarsari land.
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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".