Agricultural management-driven soil inorganic carbon dynamics: Evidence from Chinese field experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it