Effects of tillage practices on soil organic carbon in non-saline and saline‒alkaline croplands
Bibliographic record
Abstract
Understanding how different tillage practices affect soil organic carbon (SOC) and maize yield under contrasting soil conditions is critical for optimizing soil fertility and productivity. Field experiments were conducted across two soil types to assess tillage impacts on carbon fractions and maize yield. The effects of subsoiling (ST) and rotary tillage (RT) on SOC, dissolved organic carbon (DOC) and maize yield in non-saline (N-) and saline‒alkaline (S-) croplands were studied. The results revealed that in non-saline cropland, compared with N-ST in the 0–10 cm, the SOC content under N-RT at the filling and maturity stages increased ( p < 0.01). The DOC content under N-ST was 65.62% greater than that under N-RT in the 0–10 cm at the maturity stage ( p < 0.01), and compared with RT, subsoiling resulted in a greater increase in the DOC content. In the 0–40 cm, compared with RT, subsoiling was more effective at increasing the DOC/SOC ratio. In the saline‒alkaline cropland, the SOC in the 20–30 cm was 58.32% less under the S-ST than under the S-RT at the filling stage ( p < 0.05). Compared with RT, subsoiling increased the SOC content in the 0–40 cm. Subsoiling had a stronger effect on the DOC content than RT did. The DOC/SOC ratio under S-RT was greater than that under S-ST in the 10–40 cm at the maturity stage. The DOC/SOC ratio and SOC content were significantly negatively correlated. Compared with S-RT, S-ST increased the maize yield ( p < 0.05). Therefore, in non-saline cropland, RT promoted surface SOC accumulation, whereas ST enhanced DOC. In saline‒alkaline soils, ST was more effective at improving deep SOC and maize yield. These results suggest that subsoiling may be a more effective strategy for improving carbon sequestration and crop yield in saline–alkaline croplands.
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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 | 0.001 |
| 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".