Dynamics and Turnover of Soil Organic Matter as Affected by Tillage
Bibliographic record
Abstract
Greater knowledge of the dynamics of organic matter in different locations in the soil matrix can provide valuable information for implementing tillage practices that may favor C sequestration and improve soil quality. The objective of this study was to investigate the effect of tillage practices on the dynamics and turnover of organic matter located outside (free light fraction [FLF]) and inside (occluded light fraction [OLF]) aggregates, and in intimate association with soil minerals (heavy fraction [HF]). Composite soil samples from 11‐yr‐old corn ( Zea mays L.) plots under no‐till (NT) and conventional tillage (CT) practices, and from >60‐yr‐old tobacco ( Nicotiana tabacum L.)–rye ( Secale cereale L.) plots under CT were fractionated into these fractions, and changes in the amounts of total soil organic C (SOC), corn‐derived (C 4 ) C, and native (C 3 ) C and the turnover of native C as a consequence of tillage were estimated. Adoption of NT increased ( P < 0.05) standing SOC and C 4 −C stocks in whole soil and all its density fractions in the 0‐ to 5‐cm depth, but it did not alter these in the plow layer (0–20‐cm soil depth), indicating that NT primarily redistributed C within the profile without necessarily increasing SOC storage. Relative to CT, NT slowed the turnover of native C by 1.5 times and marginally ( P = 0.1) increased the sequestration of C 4 −C by 23% in the plow layer in OLF only; however, C 3 − and C 4 −C of OLF accounted for only small (6–10%) proportions of the soil's C 3 − and C 4 −C. Therefore, NT did not result in increased SOC sequestration in the plow layer.
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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) | 0.000 | 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 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".