Soil Carbon and Nitrogen Stocks and Their Influencing Factors in Different-Aged Stands of Sand-Fixing Caragana korshinskii in the Mu Us Desert of Northwest China
Bibliographic record
Abstract
Establishing artificial sand-fixing shrubs is a key measure to curb dune flow and drive changes in the soil stocks and cycling of carbon and nitrogen. But our understanding of these dynamics across years of sand-fixing afforestation and the factors influencing them remains inadequate, making it hard to accurately assess its capacity to sequester carbon. To fill that knowledge gap, this study investigated soil organic carbon (SOC) and soil total nitrogen (STN) stocks in Mu Us Desert under artificial sand-fixing shrub stands of different ages (10, 30, 50, and 70 years old) vis-à-vis a mobile sand dune, to determine whether Caragana korshinskii afforestation improved stock characteristics and whether SOC and STN stocks were correlated during the restoration processes. The results showed that the pattern observed is consistent with an increase over time in the stocks of both SOC and STN. At 10, 30, 50, and 70 years, these stocks were found to be 1.8, 2.3, 3.2, and 5.5 times higher for SOC, and 1.3, 1.6, 2.1, and 2.7 times higher for STN, respectively, than those of the control (mobile sand) dune. Stocks of SOC and STN mainly increased significantly in the 0–10 cm soil layer. The SOC stock was correlated positively with the STN stock as well as the C:N ratio. The slope of the regression for the C:N ratio against stand age was positive, increasing slightly faster with afforestation age. Additionally, our findings suggest that during the establishment of artificial stands of shrubs, the size of the STN stock did not expand as fast as the SOC stock, resulting in an asynchronous N supply and demand that likely limits the accumulation of soil organic matter. This research provides important evidence for the sustainable development of desertified ecosystems.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".