Effects of vermicompost on soil physicochemical properties, kale (Brassica oleracea) crop growth and yield in Newfoundland podzolic soils
Bibliographic record
Abstract
Newfoundland and Labrador’s (NL) agricultural development faces challenges due to adverse weather, soil acidity, and low fertility. The pulp and paper industry generates large amounts of sludge or biosolids, which are often incinerated or disposed of in landfills without exploring organic waste recycling alternatives. Paper sludge (PS), a byproduct of Corner Brook Pulp and Paper Ltd., is a potential liming and nutrient source that offers a promising solution as a soil amendment due to its high pH and essential plant nutrients. Vermicomposting offers a more sustainable alternative compared to incineration, particularly because paper sludge has high water content, which lowers its calorific value and makes combustion inefficient. Additionally, its high nitrogen content, when combusted, contributes to nitrogen oxide emissions rather than being recycled as a nutrient, further reducing the sustainability of incineration. By converting PS into vermicompost and using it as an amendment, its use can further enhance soil properties by improving the physicochemical characteristics. The first study assessed the impact of different vermicompost-to-soil ratios (0:100, 15:85, 30:70, 45:55) on soil physicochemical properties, and the second study evaluated the agronomic effect of vermicompost made from PS on the growth and yield of kale (Brassica oleracea). A pot experiment with nine soil (Orthic Ferro-Humic Podzol), vermicompost, and urea combinations was conducted in a controlled environment for the second study. Except for the 0:100 mix (with 100% N), urea levels were set at 0 and 50% of the N required by kale. Findings from the first study (Physicochemical properties) revealed that vermicompost application significantly lowered soil bulk density by 17.8–58.1% and increased total carbon by 133%–470%, total nitrogen (N) by 102–302%, organic matter by 64–1107% and soil porosity by 15.5–41.8% compared to the control. Results of the second study (Growth chamber experiment) showed that vermicompost-enriched soils significantly enhanced kale growth, increasing shoot mass by 57–120 g per plant, height by 8–12 cm per plant, and leaf count by 5–9 leaves per plant. This study highlights PS-derived vermicompost as a solution to improve the fertility of NL’s podzolic soils, supporting crop growth while replacing a considerable amount of synthetic fertilizer, promoting organic waste recycling, and contributing to local food security.
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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) | 0.000 | 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 | 0.001 |
| 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".