Residue retention promotes soil carbon accumulation in minimum tillage systems: Implications for conservation tillage
Bibliographic record
Abstract
Abstract Crop residue retention and minimum tillage (including no-tillage, NT, and reduced tillage, RT) are common conservation tillage practices that have been extensively practised for improving soil health and reducing the negative environmental impact caused by intensive farming. However, the complex effect of conservation tillage practices on soil organic carbon (SOC) storage has not been systematically analyzed, and particularly, the synergistic effect of crop residue retention and minimum tillage on SOC storage remains nonexistent. We conducted a global meta-analysis using a dataset consisting of 823 pairs of data points from 164 studies. We analyzed the effect of crop residue retention and minimum tillage on SOC storage and how the above effects were influenced by various soil/environmental (soil sampling depth, soil texture, and climate) and management conditions (cropping intensity and treatment duration). We found that either residue retention or minimum tillage alone increased SOC stock, while the former increased SOC more. The NT and RT increased SOC stock by 10 and 6%, respectively, in comparison to conventional tillage (CT). The NT plus residue retention (NTS) and RT plus residue retention (RTS) resulted in 20 and 26% more increase in SOC than NT and RT, respectively. Compared with CT, NTS and RTS further increased SOC stock by 29 and 27%, respectively. The above effects were greater in the topsoil than in the subsoil. Availability of initial soil nutrient played a greater role in affecting SOC stock than climatic conditions and management practices. Both residue retention and NT increased SOC rapidly in the first 6 years regardless of soil texture or climate condition, followed by a period of slower sequestration phase before reaching a slow steady rate. Double cropping generally increased SOC stock across all conservation tillage practices as compared to single or multiple cropping. Therefore, we conclude that minimum tillage coupled with residue retention in a double cropping system is the most beneficial management system for increasing cropland SOC storage, which can inform sustainable soil management practices aimed at increasing global C 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".