Disentangling the Association of Corn Root Mycobiome With Plant Productivity and the Importance of Soil Physicochemical Balance in Shaping Their Relationship
Bibliographic record
Abstract
Soil bacteria and fungi are integral parts of healthy ecosystem functioning in production agriculture. The effects of fungal abundance and diversity on crop productivity is poorly understood. We sampled 10 corn farms at the V10 growth stage across southwestern Ontario, Canada, using aerial infrared imaging to identify zones of low and high productive corn plants. Roots and soils were sampled from low and high yield zones and soil physical and chemical properties were measured in conjunction with assessment of the root mycobiome communities using Illumina MiSeq sequencing of 4 rRNA amplicons. Higher crop yields were associated with sites having greater fungal phylogenetic diversity and Fisher's α diversity. Indicator species associated with high and low yield sites within a farm could be identified but there were no shared fungal indicators of productivity differences across farms. Communities largely varied across locations despite crop genetics, demonstrating a major influence of soil texture and chemistry in shaping the mycobiome in a site-specific manner. Across all 4 primers, roots from high-yielding sites shared 35 major OTUs including Penicillium spp., Trichoderma, Chalara fungorum , and Gibellulopsis . Low-yielding sites shared 31 OTUs including Fusarium spp., Pythium, Setophoma terrestris , and Neonectria . Soil physical and chemical parameters that contributed to broad scale differences in yield and mycobiome diversity included: %clay, %sand, %phosphorus saturation, cation exchange capacity, aluminum, pH, iron, potassium, %moisture, organic matter, and chlorine. The results show the importance of physicochemical balance in shaping the relationship between root mycobiome and plant productivity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".