Unlocking Mechanisms for Soil Organic Matter Accumulation: Carbon Use Efficiency and Microbial Necromass as the Keys
Bibliographic record
Abstract
ABSTRACT Soil microorganisms transform plant‐derived C (carbon) into particulate organic C (POC) and mineral‐associated C (MAOC) pools. While microbial carbon use efficiency (CUE) is widely recognized in current biogeochemical models as a key predictor of soil organic carbon (SOC) storage, large‐scale empirical evidence is limited. In this study, we proposed and experimentally tested two predictors of POC and MAOC pool formation: microbial necromass (using amino sugars as a proxy) and CUE (by 18 O‐H 2 O approach). Soil sampling (0–10 and 10–20 cm depth) was conducted along a climatic transect of 900 km on the Loess Plateau, including cropland, grassland, shrubland, and forest ecosystems, to ensure the homogeneous soil parent material. We found the highest POC and MAOC accumulation occurred in zones of MAT between 5°C and 10°C or MAP between 300 and 500 mm. Microbial necromass C was more positively related to POC than MAOC ( p < 0.05), suggesting that microbial residues may improve POC pool more strongly compared to MAOC pool. Random forest and linear regression analyses showed that POC increased with fungal necromass C, whereas bacterial necromass C drove MAOC. Microbial CUE was coupled with MAOC ( p < 0.05) but decoupled with POC and SOC ( p > 0.05). The POC have faster turnover rate due to the lack of clay protection, which may lead to the rapid turnover of microbial necromass and thus their decoupling from the CUE. In this sense, the SOC accumulation is driven by microbial necromass, whereas CUE explains MAOC dynamics. Our findings highlight the insufficiency of relying solely on microbial carbon use efficiency (CUE) to predict bulk SOC storage. Instead, we propose that CUE and microbial necromass should be used together to explain SOC dynamics, each influencing distinct C pools.
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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".