Arbuscular mycorrhizal fungi and their role in plant disease control: A state-of-the-art
Bibliographic record
Abstract
Arbuscular mycorrhizal fungi (AMF) play a crucial role in plant health and growth by forming symbiotic relationships with most vascular plant species. Beyond their well-documented contributions to nutrient uptake and resilience to various stresses, AMF have been shown to protect plants from phytopathogen attacks, offering an ecological alternative to conventional pesticide-based approaches. This review summarizes recent advances in understanding the environmental and biological characteristics of AMF, with a focus on their multifunctional mechanisms for phytopathogen control. These mechanisms include competition with soil-borne pathogens, modulation of plant immune responses through induced systemic resistance (ISR), and shifts in the taxonomic and functional diversity and composition of the soil and root microbiomes. By stimulating plant defenses, producing antimicrobial metabolites, and optimizing root architecture, AMF play an important role in protecting plants against a wide range of fungal, bacterial, viral, and nematode phytopathogens. Furthermore, this review explores the role of AMF in improving soil health, a key factor in sustainable disease management, by influencing soil characteristics, nutrient cycling, and microbial activity. The integration of AMF into sustainable agricultural practices, such as no-till farming, organic farming, and biological control inoculants, is also discussed. However, challenges remain regarding their variable field efficacy and the costs associated with large-scale production and formulation of AMF-based products. Further research on OMICS technologies related to AMF is essential to harness their potential as bioagents. A comprehensive understanding of the relationships between plants, AMF, microbiomes, and phytopathogens is critical for advancing sustainable and ecological agriculture systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".