Arbuscular mycorrhizal networks—A climate-smart blueprint for agriculture
Bibliographic record
Abstract
The arbuscular mycorrhizal (AM) fungal symbiosis offers a transformative solution to mitigate agroecosystem challenges linked to the excessive use of synthetic chemicals. However, the role of AM-plant communication in response to anthropogenic activities and hyphal network functionality remains poorly understood. Here, we reposition AM fungal hyphosphere networks as a keystone ecological infrastructure for sustainable agroecosystems. Drawing on a synthesis of thousands of global experimental studies, we highlight the primary environmental functions of AM fungus-plant communication: enhancing agroecosystem resilience by buffering crops against diverse biotic and abiotic stressors through molecular signaling and physiological modulation, mediating energy transfer via small-RNA-mediated cross-kingdom interactions, facilitating hydraulic redistribution within the soil profile through hyphospheric networks, and optimizing root architecture via effective colonization for improved nutrient acquisition. Certain anthropogenic practices-such as soil disturbance, non-mycorrhizal crop monoculture, and fungicide application-can disrupt AM hyphal networks; however, these impacts can be minimized through improved farming practices, such as cropping diversification with legumes and AM fungus-compatible crops, AM-responsive plant genotypes, effective AM fungal inoculation, and microbial consortium amendments. Integrating insights into AM fungal mechanisms with anthropogenic practices and policy support is essential to scaling AM benefits across ecoregions. Harnessing AM fungal functionality can increase nutrient use efficiency, reduce reliance on chemical inputs, and enhance ecosystem productivity, offering a microbe-centered blueprint to support the United Nations' sustainability goals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".