Grafting and Inoculation of Grafted Plants With Bacillus amyloliquefaciens Enhances Biotic and Abiotic Stress Tolerance in Organic Tomato
Bibliographic record
Abstract
Biotic and abiotic stress factors pose significant challenges to organic tomato production in Alabama and the southeastern United States. High temperature, drought, soil, and foliar-borne diseases have rendered it exceedingly difficult for organic tomato growers in Alabama to produce profitable crops. Our objective was to evaluate plant response to imposed biotic (Verticillium dahliae) and abiotic (drought) stress in the popular tomato variety ‘Roma’ through grafting unto the resistant rootstock ‘Maxifort’, and the incorporation of the plant growth-promoting rhizobacterium (PGPR) B. amyloliquefaciens, to mitigate challenges posed by these stress factors. The research was conducted in a greenhouse in north Alabama where both soilborne pathogens (V. dahliae) and drought stress treatments were applied. The experimental design was a split plot with 4 main plots (Grafting, PGPR, Grafting + PGPR, and Control) and 3 subplots (drought, pathogen, and control) treatments with 4 replications. B. amyloliquefaciens was applied in the rhizosphere of grafted and non-grafted plants at 1 × 108 CFU/ml/plant. Pathogen-treated plants were also inoculated with V. dahliae at 1 × 105 propagules/ml. The study revealed that grafting, and grafting + B. amyloliquefaciens caused a significant increase in stem girth, plant biomass, early flowering, and fruiting of tomatoes compared to the non-grafted and control treatments. Integrating grafting and PGPR could be beneficial in enhancing plant resilience and performance under biotic and abiotic stress conditions.
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) | 0.000 | 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 | 0.001 |
| 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".