Enhanced Stabilization of Soil Organic Carbon by Growing Leguminous Green Manure on the Loess Plateau of China
Bibliographic record
Abstract
Evaluating the impacts of growing leguminous green manure (LGM) on soil organic carbon (SOC) stabilization is crucial to evaluate the sustainability of this management practice. To clarify this, we measured organic carbon (OC) fractions and used protected C to estimate the stability of SOC. The field study was a split‐plot design with four main treatments: summer fallow–winter wheat ( Triticum aestivum L.) (FW) as control and growing LGM to replace Huai bean ( Glycine soja Sieb . et Zucc)–winter wheat (HW), soybean [ Glycine max (L.) Merr.]–winter wheat, and mung bean [ Vigna radiata (L.) Wilczek]–winter wheat. The subtreatments were four synthetic N rates applied before sowing winter wheat. Physical fractionation was used to isolate different OC fractions. Among them, the intra‐microaggregate fine particulate OC and mineral‐associated OC are protected C. The mean weight diameter at the 0‐ to 10‐cm soil of HW was significantly increased compared with FW. The SOC content of the bulk soil for the LGM treatments was increased by 0.93 to 1.18 g kg −1 and 0.33 to 1.04 g kg −1 at depths of 0 to 10 cm and 10 to 20 cm, respectively, compared with FW. The protected C accounted for 69 to 86% of the total SOC increase. Moreover, only the protected C was significantly and positively correlated with the SOC increase. In conclusion, growing LGM to replace summer fallow can increase the quantity and stability of SOC by increasing the content of protected C, suggesting that the proposed management practice could promote sustainable agriculture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".