Cover crop-driven shifts in soil microbial communities could modulate early tomato biomass via plant-soil feedbacks
Bibliographic record
Abstract
Sustainable agricultural practices such as cover crops (CCs) and residue retention are increasingly applied to counteract detrimental consequences on natural resources. Since agriculture affects soil properties partly via microbial communities, it is critical to understand how these respond to different management practices. Our study analyzed five CC treatments (oat, rye, radish, rye-radish mixture and no-CC) and two crop residue managements (retention/R+ or removal/R-) in an 8-year diverse horticultural crop rotation trial from ON, Canada. CC effects were small but stronger than those of residue management. Radish-based CCs tended to be the most beneficial for both microbial abundance and richness, yet detrimental for fungal evenness. CC species, in particular radish, also shaped fungal and, to a lesser extent, prokaryotic community composition. Crop residues modulated CC effects on bacterial abundance and fungal evenness (i.e., more sensitive in R- than R+), as well as microbial taxa. Several microbial structure features (e.g., composition, taxa within Actinobacteria, Firmicutes and Ascomycota), some affected by CCs, were correlated with early biomass production of the following tomato crop. Our study suggests that, whereas mid-term CC effects were small, they need to be better understood as they could be influencing cash crop productivity via plant-soil feedbacks.
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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 | 0.000 |
| 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) | 0.001 | 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".