Mineral–organic associations are enriched in both microbial metabolites and plant residues in a subtropical soil profile under no-tillage and legume cover cropping
Bibliographic record
Abstract
Context Knowledge of the impacts of no-tillage and cover cropping on carbon accumulation and stabilisation in highly weathered agricultural soils of subtropical regions is scant. We hypothesised that implementation of no-tillage coupled with high-quality legume residues in highly weathered agricultural soils would result in high carbon accumulation rates, mainly as microbe- and plant-derived materials in fine mineral–organic complexes. Aims and methods We sampled soil profiles down to 100 cm in a long-term field experiment and used density and particle size fractionation in combination with carbohydrate analyses to compare the effect of conventional tillage vs no-tillage, combined or not with legume cover cropping, and combined or not with mineral nitrogen fertilisation. Key results Both no-tillage and legume cover crops favoured the accumulation and enrichment in plant-derived carbohydrates in the surface soil layer, due to the accumulation of plant residues. The ratio of microbe- to plant-derived carbohydrates increased with soil depth indicating that the soil carbon (C) was more microbially processed than at the surface. Conservation management systems also increased soil C at depth and this was most visible in the clay fraction. The additional clay-size C accumulating at depth under conservation treatments was of both microbial and plant origin. Conclusions Our results support the hypothesis that mineral-associated C is composed of both plant and microbial residues and is positively influenced by conservation management practices. Implications Our results demonstrate that no-till and legume cover cropping are efficient practises to foster C accumulation and stabilisation in heavily weathered agricultural soil profiles in a subtropical climate.
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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 | 0.001 | 0.001 |
| 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".