Crop diversity enriches arbuscular mycorrhizal fungal communities in an intensive agricultural landscape
Bibliographic record
Abstract
Arbuscular mycorrhizal fungi (AMF) are keystone symbionts of agricultural soils but agricultural intensification has negatively impacted AMF communities. Increasing crop diversity could ameliorate some of these impacts by positively affecting AMF. However, the underlying relationship between plant diversity and AMF community composition has not been fully resolved. We examined how greater crop diversity affected AMF across farms in an intensive agricultural landscape, defined by high nutrient input, low crop diversity and high tillage frequency. We assessed AMF communities across 31 field sites that were either monocultures or polycultures (growing > 20 different crop types) in three ways: richness, diversity and composition. We also determined root colonization across these sites. We found that polycultures drive the available AMF community into richer and more diverse communities while soil properties structure AMF community composition. AMF root colonization did not vary by farm management (monocultures vs polycultures), but did vary by crop host. We demonstrate that crop diversity enriches AMF communities, counteracting the negative effects of agricultural intensification on AMF, providing the potential to increase agroecosystem functioning and sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".