Indigenous bacteria with antagonistic and plant-growth-promoting activities improve slow-filtration efficiency in soilless cultivation
Bibliographic record
Abstract
In tomato soilless culture, slow filtration allows one to control the development of diseases caused by pathogenic microorganisms. During the disinfecting process, microbial elimination is ensured by mechanical and biological factors. In this study, system efficacy was enhanced further to a biological activation of filter by inoculating the pozzolana grains contained in the filtering unit with 5 selected bacteria. Three strains identified as Pseudomonas putida and 2 as Bacillus cereus came from a filter whose high efficiency to eliminate pathogens has been proven over years. These 5 bacteria displayed either a plant growth promoting activity (P. putida strains) or antagonistic properties (B. cereus strains). Over the first months following their introduction in the filter, the bacterial colonisation of pozzolana grains was particularly high as compared to the one observed in the control filter. Conversely to Bacillus spp. populations, Pseudomonas spp. ones remained abundant throughout the whole cultural season. The biological activation of filter unit very significantly enhanced fungal elimination with respect to the one displayed by the control filter. Indeed, the 6-month period needed by the control filter to reach its best efficacy against Fusarium oxysporum was shortened for the bacteria-amended filter; in addition, a high efficacy filtration was got as soon as the first month. Fast colonization of pozzolana grains by selected bacteria and their subsequent interaction with F. oxysporum are likely responsible for filter efficiency. Our results suggest that Pseudomonas spp. act by competition for nutrients, and Bacillus spp. by antibiosis and (or) direct parasitism. Elimination of other fungal pathogens, i.e., Pythium spp., seems to differ from that of Fusarium since both filters demonstrated a high efficacy at the experiment start. Pythium spp. elimination appears to mainly rely on physical factors. It is worth noting that a certain percentage of the 5 pozzolana-inoculated bacteria failed to colonise the filter unit and were, thus, driven to the plants by the nutrient solution. Their contribution to the establishment of a beneficial microbial community in the rhizosphere is discussed.
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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 | 0.001 | 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".