Land use effects on soil carbon retention through glomalin-mediated aggregation
Bibliographic record
Abstract
• Grassland increased the content of macroaggregates, SOC and GRSP. • TG and EEG both promoting the formation and stability of macroaggregates. • TG contributed more to SOC in macroaggregates, while EEG in microaggregates. The glomalin-related soil protein (GRSP), produced by arbuscular mycorrhizal fungi, plays a critical role in soil organic carbon (SOC) storage and stabilization across terrestrial ecosystems. We examined the effects of four contrasting land-use types (bare land, cropland, grassland, and woodland) originating from the same soil matrix on GRSP dynamics, soil aggregation, and SOC accumulation. We found that grassland and woodland ecosystems exhibited significantly higher contents of macroaggregates (>0.25 mm), SOC, total nitrogen (TN), total GRSP (TG), easily extractable GRSP (EEG) and aggregate stability compared with cropland and bare land. Across all land uses, the EEG/SOC and TG/SOC ratios in Mi and SC were higher than other aggregates sizes. Correlation analyses showed that TG was significantly and positively correlated with SOC in bulk soil and macroaggregates (>2 mm), while EEG exhibited significant positive correlations with SOC in smaller macroaggregates and microaggregates. Furthermore, GRSP was strongly and positively correlated with aggregate stability, and the content of TG and the TG/SOC ratio were mainly influenced by microbial biomass carbon (MBC) and SOC. Our findings highlight the contrasting correlations of TG and EEG to SOC storage across aggregate size classes, with TG playing a more prominent role in bulk soil and large macroaggregates, and EEG more significantly associated with SOC accumulation in small macroaggregates and microaggregates. This study advances our understanding of the mechanisms by which GRSP facilitates SOC sequestration and soil aggregate formation under different land-use regimes, informing sustainable land management strategies for climate change mitigation.
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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) | 0.000 | 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 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".