Screening and Characterization of Rhizobacteria as PGPR for Enhancing Growth and Yield of Aceh Patchouli
Bibliographic record
Abstract
Patchouli (Pogostemon cablin Benth.) is a plant that yields essential oils with high economic value, contributing significantly to Indonesia's economy, particularly in the perfume, cosmetics, and aromatherapy industries.However, patchouli oil production in Aceh has declined due to suboptimal cultivation practices.The application of PGPR (Plant Growth-Promoting Rhizobacteria) has the potential to enhance production by functioning as a biofertilizer, biostimulant, and bioprotectant.This study aimed to explore, isolate, characterize morphologically and physiologically, and apply PGPR to patchouli growth, identifying potential strains through 16S rRNA gene sequencing.The study began with the exploration of PGPR in the rhizosphere of patchouli plants across six regions in Aceh.Isolation and characterization were conducted at the Seed Science and Technology Laboratory, Faculty of Agriculture, Syiah Kuala University.PGPR was applied using a Randomized Block Design (RBD) with two factors: 15 rhizobacterial isolates and three Aceh patchouli varieties (Tapak Tuan, Lhokseumawe, and Sidikalang).This study identified 14 potential isolates capable of producing indole-3-acetic acid (IAA), fixing nitrogen, solubilizing phosphate, and generating Hydrogen Cyanide (HCN), siderophores, and 1-Aminocyclopropane-1-carboxylic acid (ACC) deaminase.Among them, isolate PG 9/2 C exhibited the highest efficacy and was identified as Delftia tsuruhatensis BB1455.This isolate enhanced plant height by up to 53.91% (Sidikalang), increased the number of leaves and branches by 89.88% and 85.56% (Tapak Tuan), improved root volume by 85.47% (Tapak Tuan), and boosted wet and dry biomass by 79.75% and 323.68% (Sidikalang), respectively, indicating optimal biomass accumulation.
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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) | 0.001 | 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".