Tillage and cropping effects on soil organic carbon: Biodegradation and storage in density and size fractions
Bibliographic record
Abstract
Abstract Improvements in management practices can prevent the decline of soil organic carbon (SOC) storage caused by conventional tillage practice in Northeast China. Density and size fractionation can track the transformation of plant residue into SOC and its location in the soil matrix. We used a long‐term field study in China to evaluate these changes as a result of improved management involving tillage and cropping systems. Experimental treatments included no‐till (NT) and mouldboard ploughing (MP) under monoculture maize ( Zea mays L.) (MM) and maize‐soybean ( Glycine max Merr.) rotation (MS); these were compared to the traditional management involving conventional tillage (CT) under MM. An incubation study was conducted to evaluate mineralization and the biodegradability of SOC. The soils were also physically fractionated by density (light fraction, LF) and size (sand, silt and clay). With improved management, the SOC storage in the clay size fraction showed the largest increase across all fractions. This increase was greater for MS than MM. The NTMS treatment resulted in a decline in silt‐OC storage compared to CTMM. The SOC mineralization (mg CO 2 ‐C g −1 soil) was affected by tillage and driven by LF‐OC and was observed in the order: NTMM (2.06) > MPMM (1.72) ≈ NTMS (1.71) > CTMM (1.52) ≈ MPMS (1.41). Both cropping and depth affected the biodegradability of SOC. Considering the plough layer (0–20 cm), treatments under MM had a larger proportion of biodegradable SOC than those under MS. We conclude that the significant differences in SOC storage in physical fractions and SOC biodegradation were caused by differences in soil management. Highlights Clay size fraction showed the largest increase in SOC storage when residue was returned. Silt size fraction lost SOC in no‐till maize‐soybean compared with traditional farming practice. Potential SOC mineralization depended on quantity of SOC in the LF. Biodegradability of the SOC was driven by cropping system not tillage.
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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".