Effects of conservation tillage on soil organic carbon storage, fractions and stability in Black soil
Bibliographic record
Abstract
In Northeast China, conventional tillage practices involve removal of crop residue after harvest and prior to moldboard plowing; this has been shown to cause a decline of soil organic carbon (SOC) and degradation of Black soils (Mollisols). Conservation tillage, particularly no tillage (NT), has been suggested to be an effective practice to control soil erosion and increase the SOC content. Hence, we established an experiment (since 2001) to evaluate how a combination of different tillage and cropping systems could improve SOC in black soils. The total SOC storage, SOC fractions (physical and chemical), SOC stability were assessed to evaluate the effects of tillage and cropping system. Our results shows that: 1) different tillage and cropping system combinations had different effects on SOC storage; NT combined with continuous maize had the highest SOC storage among all treatments; 2) The effects of tillage on aggregate size and OC concentration mainly occurred in the surface layer (0–5 cm) while the effect of cropping system on aggregate size and OC concentration mainly occurred at deeper depths; 3) NT increased the recalcitrant carbon pool in surface layer showing the critical need for returning crop residues to maintain long-term SOC storage; 4) SOC mineralization (biological stability) appears to be related to the SOC proportion in the light fraction; 5) More than half of the increase in SOC storage due to NT existed as microbial necromass carbon storage under continuous maize which was higher than maize-soybean rotation. Our study shows that in black soils (Northeast China), NT and appropriate cropping systems can not only halt soil degradation caused by poor management but can induce substantial increases in SOC which is beneficial for SOC long-term sequestration.
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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) | 0.001 | 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".