Diversification of corn-based cropping systems with wheat increases plant growth-promotion potential of soil microbial communities
Bibliographic record
Abstract
Diversifying corn-based crop rotations by including wheat can enhance soil health and increase corn yield resilience, but long-term effects on soil microbial communities and microbial plant growth-promoting (PGP) functions are unclear. Moreover, responses of soil microbiota to rotational changes are influenced by other interacting agricultural practices. We analyzed soil samples from a 25-year field trial in southern Ontario, Canada, comparing four corn-based crop rotations in combination with conventional and reduced tillage practices and presence and absence of nitrogen fertilization. Microbial community composition and PGP potential were assessed via amplicon sequencing of 16S rRNA genes and ITS regions and qPCR quantification of PGP functional genes. Tillage had strongest impacts on microbial composition, followed by crop rotation, then fertilization. Reducing tillage exerted stronger effects on prokaryotic than fungal composition, whereas increasing rotational diversity had stronger effects on fungal than prokaryotic composition. A strong rotation × tillage interaction indicated that diversification effects were more pronounced under reduced tillage, promoting distinct microbial taxa across tillage systems. Diversifying rotations with wheat enriched potential PGP genera such as Marquandomyces , Clonostachys , and Edaphobaculum, and increased gene abundances related to phosphate mineralization, nitrification, sulfur oxidation, and plant stress tolerance. Functional shifts were most evident under conventional tillage, suggesting enhanced microbial capacity for nutrient availability and stress mitigation in intensive systems. Overall, our findings demonstrate that crop rotation-induced shifts in soil microbiota are strongly shaped by tillage, and integrating wheat into corn-based rotations can enhance microbial plant growth-promotion potential, particularly under conventional tillage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".