Agricultural management-driven soil inorganic carbon dynamics: Evidence from Chinese field experiments
Bibliographic record
Abstract
Soil inorganic carbon (SIC) is a crucial component of soil carbon pool, impacting climate change and ecosystem functions. SIC is affected by drastic changes in agricultural practices, while its response remains uncertain. We synthesized 54 field studies in China to assess the impact of agricultural practices on soil carbon stock, focusing on SIC and its responses to environmental factors. Overall, agricultural practices significantly reduced SIC stock (3.37 %) while increasing soil organic carbon (SOC) stock (15.41 %) and total carbon stock (6.80 %). Carbon pool changes could be categorized as follows: synergistic increases in SIC and SOC; trade-offs between SOC increases and SIC decreases; and individual effects on either SOC or SIC. SIC varied significantly across practices and regions, driven by climate, field management, and soil properties. Mineral fertilizer and straw return caused SIC losses, particularly under low-temperatures (MAT < 10 ℃), high-rainfall (MAP > 400 mm), and after 30 years. Severe SIC losses were observed in Northeast and East China. Combining organic and mineral fertilizers optimized the balance between SIC and crop yield, especially in arid regions. Key factors affecting SIC stock included soil depth, nitrogen addition, and experimental duration. Furthermore, our meta -analysis revealed that the distinct responses of SIC and SOC to agricultural practices underscored the necessity of integrated management strategies that effectively balanced SOC sequestration with SIC conservation. This study enhances understanding of SIC cycle and provides scientific evidence for sustainable agricultural practices.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".