Guiding soil carbon sequestration with a novel organic material returning index globally
Bibliographic record
Abstract
Straw returning (SWR) and manure returning (OMR) are vital for enhancing soil organic carbon (SOC) sequestration in croplands, supporting agricultural sustainability and climate change mitigation. We developed the Organic Material Returning Index (OMRI) using data from 347 global cropland sites to guide OMR and SWR selection. Our findings show OMR increases SOC by 35.60% in low-SOC soils compared to 13.92% for SWR, driven by initial SOC and soil pH, while SWR dexcels in low-clay soils (<18%) and warmer climates, governed by clay content and mean annual temperature. The OMRI (threshold k=0.2874) identifies 27.8% of croplands, notably in mid-to-high latitudes (e.g., Canada, Northeast China) and equatorial regions (e.g., West Africa), as suitable for cost-effective SWR, outperforming machine learning’s 2.38% prediction. Unlike opaque models, the OMRI offers a transparent, mechanistically grounded framework, enabling region-specific strategies to maximize SOC sequestration and inform sustainable agricultural policies worldwide.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".