Biocontrol of Phytopathogens Using Plant Growth Promoting Rhizobacteria: Bibliometric Analysis and Systematic Review
Bibliographic record
Abstract
Biocontrol has emerged as an effective strategy for managing plant pathogens and pests. The use of plant growth-promoting rhizobacteria (PGPR) as biocontrol agents offers a sustainable alternative, enhancing plant morphology, biochemistry, physiology, and secondary metabolism. This study conducts a bibliometric analysis and systematic review of PGPR-based biocontrol research from 2019 to 2023, using the Web of Science (WoS) database. A total of 2823 publications were identified, with a significant increase in scientific output since 2019. Original research articles dominated the field, with India, China, the USA, and Pakistan leading in publication volume. Key contributors included Babalola (North-West University, South Africa), Kloepper (Auburn University, USA), and Shen (Nanjing Agricultural University, China), each with at least 25 publications. Co-authorship analysis revealed four major research networks centered in India, China, Brazil, and Canada. Bacillus and Pseudomonas were the most studied PGPR genera, recognized for their roles as bioinoculants, bioremediators, and biostimulants, mitigating the negative impacts of synthetic fertilizers and pesticides. This analysis underscores the growing global focus on PGPR-based biocontrol and its potential for sustainable agriculture. Strengthening international collaboration and accelerating applied research on PGPR formulations will be critical for optimizing their efficacy and scalability in real-world agricultural systems.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.103 | 0.093 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".