Soil and plant cations as affected by application of wood ash, biochar, and papermill biosolids
Bibliographic record
Abstract
Abstract Recycling woody biomass for application to croplands is one option to divert materials from landfills and simultaneously improve degraded soil properties. Considering the diversity of materials that vary widely in characteristics, an understanding of the comparative effects of a single or combined application of these byproducts is missing with regards to soil C accumulation and availability of base and metallic cations. A field study was conducted in Québec City, QC, Canada, to assess the effects relative to untreated control of wood ash (10 and 20 Mg dry wt. ha−1), pine biochar (10 Mg dry wt. ha−1), papermill biosolids (12 Mg PB dry wt. ha−1), and a combination of wood ash and PB on soil C, pH, and cations in a circumneutral loamy soil. The site was cropped to a corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. All materials were applied before corn planting and the effects of treatment were followed over two growing seasons. Applying wood ash resulted in the statistically largest increases (p < 0.01) in soil pH, percentage base saturation, and Mehlich‐3 K, Ca, Mg, Zn, and Cd. Wood ash also increased K concentration in straw and total K accumulation for both plants, but its effect on plant metallic cations was limited. With a single application, PB only increased Mehlich‐3 Ca with no further effect when combined with wood ash, while pine biochar was limited to sequester soil C. Therefore, this study indicated that wood ash could benefit a corn–soybean rotation by enhancing soil quality and crop yield.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".