The rhizosphere microbiome can sustainably protect field-grown tomato crops against soil-borne pathogens and plant parasitic nematodes
Bibliographic record
Abstract
Roots of tomato plants (Solanum lycopersicum) are vulnerable to soil-borne pathogenic fungi, bacteria and nematodes. Current control methods for these biotic stressors have limitations, necessitating the need for new eco-friendly alternatives. The rhizosphere microbiome is an effective natural barrier to invasion by soil-borne pathogens, and there is scope to harness this inherent capacity to improve the management of pathogens. This review examined molecular analyses of the taxonomic composition, abundance and function of the rhizosphere microbiome in healthy and diseased field-grown tomato plants for evidence of the role of the microbiome in disease suppression. The role of biological products in manipulating the rhizosphere microbiome to suppress soil-borne pathogens in field-grown tomato crops was also analysed. We discuss likely mechanisms underpinning microbiome-mediated tolerance to biotic stress in tomato crops and highlight research gaps to be considered in future investigations. Identifying functionally beneficial rhizosphere microbiota in healthy tomato crops may provide new insights into understanding plant–pathogen interactions and allow new strategies for exploring disease control.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".