Growth promotion of greenhouse tomatoes with Pseudomonas sp. and Bacillus sp. biofilms and planktonic cells
Bibliographic record
Abstract
The effects on plants of commercial applications of biofertilizer and biocontrol agents containing planktonic rhizobacteria cells are inconsistent mainly due to an unsuccessful competition with existing endogenous microbial communities. The present study investigated rhizobacteria inoculants in the form of biofilms, which are dense colonies of single or multi-species microbial cells, adherent to either biotic or abiotic surfaces, and encased in a self-produced matrix composed of extracellular polymeric substances. Bacillus sp., Pseudomonas sp., or a mixed inoculum of both bacteria at the planktonic or biofilm stage of growth were applied to greenhouse-grown tomato ( Solanum lycopersicum L. cv Trust) plants fertilized with a half strength Hoagland solution or a modified solution lacking phosphate or iron. Based on multivariate analysis of variance, availability of nutrients, bacteria, and stages of bacterial growth significantly affected plant variables known to be contributing factors to greenhouse tomato yield. Results suggest that Bacillus sp. and Pseudomonas sp. biofilms are more effective inoculants than planktonic bacteria cells. Pseudomonas sp. applied as a biofilm increased the height and root dry weight of tomato plants, but it inhibited the positive effects of inoculation on tomato leaf number and tomato root length. Path analysis identified positive causal paths from ( Pseudomonas ) biofilm and planktonic cells, to the tomato plant growth variables. Presence of Bacillus sp. alone or in a mixture inhibited the positive effects of inoculation on tomato plant leaf area and root dry weight. Nevertheless, the effect of Bacillus sp. on root length was relatively positive. Results suggest that the use of innovative rhizobacteria biofilm technologies could prove advantageous as biofertilizer agents, and alleviate the dependence of greenhouse tomato producers on non-biological agro-chemicals.
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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) | 0.001 | 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".