Deciphering the mechanisms through which arbuscular mycorrhizal symbiosis reduces nitrogen losses in agroecosystems
Bibliographic record
Abstract
Nitrogen (N) cycling within terrestrial ecosystem is largely controlled by networks of prokaryotic microbial communities that mediate the conversion, immobilization, and turnover of various forms of N present in soil. Recently, the role of arbuscular mycorrhizal (AM) symbiosis in N cycling within terrestrial ecosystems has gained considerable attention. However, a comprehensive assessment of how AM symbiosis can contribute to reducing N loss within the agricultural ecosystems remains incomplete. In this review, we examine the direct and indirect mechanisms by which arbuscular mycorrhizal fungi (AMF) can help mitigate N loss from agricultural ecosystems. Direct mechanisms include the interception and immobilization of organic and inorganic N within fungal and plant biomass. Indirect mechanisms involve the contributions of AMF to plant nutrition and diversity, soil organic matter mineralization, soil structure formation, plant–soil–water relations, microbial biomass N immobilization. Both the direct and indirect mechanisms ultimately influence the composition and functioning of N-cycling communities. This review reinforces the relevance of ecologically based approaches not only for addressing agricultural N losses but also for advancing sustainable development goals, especially the target to halve N waste from all sources by 2030.
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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 | 0.000 |
| 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 | 0.001 |
| 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".