Year-to-year variation in organic fertilization effects on soil carbon stabilization and microbial networks
Bibliographic record
Abstract
Objective Soil organic carbon (SOC) stabilization is a key process linking soil fertility and climate change mitigation; however, its microbial regulatory mechanisms under organic fertilization remain unclear. This study aimed to elucidate how different rates and combinations of organic fertilization regulate SOC fractions, enzyme activities, and microbial communities in newly reclaimed farmland. Methods A two-year field experiment (2024–2025) was conducted under a maize–oilseed rape rotation system in Sichuan, China. Treatments included conventional fertilization, varying rates of organic fertilization, and combined organic–inorganic fertilization. SOC fractions, enzyme activities, and microbial community composition were analyzed, and structural equation modeling was applied to explore the microbial and enzymatic pathways driving SOC stabilization. Results Organic fertilization enhanced both labile and stable carbon pools. Readily oxidizable carbon (ROC) and dissolved organic carbon (DOC) increased under moderate inputs, while mineral-associated organic carbon (MAOC) accumulated under higher inputs. Enzyme activities mirrored these changes, with laccase activity enhanced under high organic inputs and cellobiohydrolase suppressed by sole organics but restored under combined fertilization. Microbial analysis showed enrichment of Proteobacteria , increased diversity, and year-specific shifts in Actinobacteriota . Moderate labile carbon inputs promoted stable carbon formation in the first year, whereas excessive inputs in the second year reduced microbial efficiency, increased network complexity, and weakened stabilization. Conclusion Moderate organic fertilization improved rapeseed yield and carbon sequestration efficiency by promoting stable carbon accumulation, oxidative enzyme activity, and functional microbial diversity. These findings reveal the microbial and enzymatic mechanisms underlying SOC stabilization in newly reclaimed farmland and provide practical guidance for balanced fertilization strategies to enhance carbon storage while sustaining crop productivity.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".