Microbial feedbacks on soil organic matter dynamics underlying the legacy effect of diversified cropping systems
Bibliographic record
Abstract
Crop rotations have well-known aboveground and belowground benefits. At regional to continental scales, the unifying mechanisms of how diversified rotations alter soil organic matter (SOM) dynamics have not been demonstrated. We assessed how increasing crop rotational diversity across a soil-climate gradient affected the integrated response of SOM chemistry, microbial community composition, and its enzymatic potential to degrade SOM. Agroecosystems with the same crop rotational diversity (all sampled during the corn phase) shared similarities in molecular SOM patterns with a strong microbial signature, pointing to common transformation processes. Differences in SOM chemistry between rotations were mainly characterized by shifts in microbial necromass markers and in lipids produced or transformed by microbes rather than by intact plant lipids. Microbial resource allocation to enzymes, which catalyze the decomposition of organic matter, differed between systems. Lower resource investment into recalcitrant C-degrading enzymes with increasing crop diversity indicates higher resource availability for the microbial community. Our multivariate analyses suggest that this could be regulated via relative changes in microbial functional groups – emergence of relatively more non-oxidase producing microorganisms like arbuscular mycorrhizal fungi rather than an absolute decrease in oxidase producing microbes. These uniform responses to increased crop rotational diversity over a wide geographical area point to enhanced stabilization of microbial-derived SOM and functional shifts in the microbial community as a common mechanism underlying the positive plant-soil feedback in diversified cropping systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".