Full‐Inversion Tillage and Organic Carbon Distribution in Soil Profiles: A Meta‐Analysis
Bibliographic record
Abstract
While the adoption of no‐till (NT) usually leads to the accumulation of soil organic C (SOC) in the surface soil layers, a number of studies have shown that this effect is sometimes partly or completely offset by greater SOC content near the bottom of the plow layer under full‐inversion tillage (FIT). Our purpose was to review the literature in which SOC profiles have been measured under paired NT and FIT situations. Only replicated and randomized studies directly comparing NT and FIT for >5 yr were considered. Profiles of SOC had to be measured to at least 30 cm. As expected, in most studies SOC content was significantly greater ( P < 0.05) under NT than FIT in the surface soil layers. At the 21‐ to 25‐cm soil depth, however, which corresponds to the mean plowing depth for the data set (23 cm), the average SOC content was significantly greater under FIT than NT. Moreover, under FIT, greater SOC content was observed just below the average depth of plowing (26–35 cm). On average, there was 4.9 Mg ha −1 more SOC under NT than FIT ( P = 0.03). Overall, this difference in favor of NT increased significantly but weakly with the duration of the experiment ( R 2 = 0.15, P = 0.05). The relative accumulation of SOC at depth under FIT could not be related to soil or climatic variables. Furthermore, the organic matter accumulating at depth under FIT appeared to be present in relatively stable form, but this hypothesis and the mechanisms involved require further investigation.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".