Rhizosphere Engineering of Biocontrol Agents Enriches Soil Microbial Diversity and Effectively Controls Root-Knot Nematodes
Bibliographic record
Abstract
The root-knot nematode (RKN) causes significant yield loss in tomatoes. Understanding the interaction of biocontrol agents (BCAs)-nematicides-soil microbiomes and RKNs is essential for enhancing the efficacy of biocontrol agents and nematicides to curb RKN damage to crops. The present study aimed to evaluate the in vitro effectiveness of BACa and nematicide against RKN and to apply the amplicon sequencing to assess the interaction of Bacillus velezensis (VB7) and Trichoderma koningiopsis (TK) against RKNs. Metagenomic analysis revealed the relative abundance of three phyla such as Proteobacteria (42.16%), Firmicutes (19.57%), and Actinobacteria (17.69%) in tomato rhizospheres. Those tomato rhizospheres treated with the combined application of B. velezensis VB7 + T. koningiopsis TK and RKN had a greater frequency of diversity and richness than the control. RKN-infested tomato rhizosphere drenched with bacterial and fungal antagonists had the maximum diversity index of bacterial communities. A strong correlation with a maximum number of interconnection edges in the phyla Proteobacteria, Firmicutes, and Actinobacteria was evident in soils treated with both B. velezensis VB7 and T. koningiopsis TK challenged against RKN in infected soil. The present study determined a much greater diversity of bacterial taxa observed in tomato rhizosphere soils treated with B. velezensis VB7 and T. koningiopsis TK than in untreated soil. It is suggested that the increased diversity and abundance of bacterial communities might be responsible for increased nematicidal properties in tomato plants. Hence, the combined applications of B. velezensis VB7 and T. koningiopsis TK can enhance the nematicidal action to curb RKN infecting tomatoes.
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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 | 0.000 | 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".