Compost and biosolids increase long-term soil organic carbon stocks
Bibliographic record
Abstract
Biobased residues derived from organic urban waste materials can be processed to produce soil amendments that enhance soil fertility and carbon sequestration. However, the extent of carbon sequestration by biobased residues depends on the interaction between their physicochemical properties, climate, and agroecosystem management practices. Our objective was to predict how different biobased residues (compost, anaerobic digestate, or biosolids), compared to nitrogen fertilizer, affect soil organic carbon stocks under continuous cropping and crop rotation in Ontario, Canada, using the Century model. The Century model was calibrated and validated with data, from a three-year field study located in Elora, Ontario, Canada, that was used to predict long-term changes in soil organic carbon. Our results showed that after 150 years, soil amended with compost and biosolids increased soil organic carbon stocks significantly ( p < 0.05) compared to anaerobic digestate and nitrogen fertilizer. Soil organic carbon stocks were 1%–27% greater with crop rotation compared to continuous cropping. Model performance indicated a strong correlation between measured and simulated soil organic carbon stocks ( R2 = 0.26–0.82; RMSD = 432–727 g m−2). Our findings suggested that compost had the greatest soil carbon sequestration potential of the tested soil amendments, and this difference was due to the quantity and quality of carbon input.
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 | 0.000 | 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) | 0.001 | 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".