Effects on soil carbon storage from municipal biosolids application to agricultural fields
Bibliographic record
Abstract
This study investigated the influence of biosolid applications on soil carbon storage and evaluated nutrient management strategies affecting soil carbon dynamics. The research assessed alterations in soil pH, soil carbon stock, and soil nitrogen content within short-term and long-term biosolids-amended soils in Bible Hill, Nova Scotia, Canada, extending to a depth of 0–60 cm. The findings indicated an increase in soil pH with alkaline treatment biosolids (ATB) applications across both study sites, with a legacy effect on soil pH noted in the long-term biosolids-amended soil following a single ATB application over 13 years. Both sites demonstrated significant increases in soil total carbon (STC) and soil organic carbon (SOC) within the 0–30 cm soil depth after biosolid application, and soil inorganic carbon (SIC) accounted for approximately 5–10% of STC, specifically in the surface soil layer (0–15 cm). In the long-term study site, annual 14, 28 and 42 Mg ATB ha−1 treatments resulted in a substantial rise in soil carbon stock (59.5, 60.1 and 68.0 Mg C ha−1), marking a 25% increase compared to control soil. The SOC content in biosolids-amended soil showed a declining trend with increasing soil depth at both study sites. Notably, the carbon stock in the short-term site was observed in composted biosolids (COMP) > ATB > liquid mesophilic anaerobically digested biosolids (LMAD) from the 0–60 cm soil depth. Approximately 79–80% of the variation in SOC response at both sites was concentrated within the top 30 cm soil. Soil total nitrogen (STN) showed no significant differences at the short-term site, and STN in biosolids-amended soil decreased with increasing soil depth at the long-term site. Biosolids-induced C retention coefficients (BCR) for ATB remained consistent at both sites, ranging from −13% to 31.4% with a mean of 11.12%. BCR values for COMP ranged from 1.9% to 34.4% with a mean of 18.73%, while those for LMAD exhibited variability, spanning from −6.2% to 106.3% with a mean of 53.9%.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".