Application of Spruce Bark Biochar Minimizes Nitrogen and Carbon Leaching from an Eastern Newfoundland Podzolic Soil
Bibliographic record
Abstract
Biochar has broad applications in agriculture, where its incorporation into soils is recognized as an effective strategy for improving soil quality, enhancing remediation, sequestering carbon, and mitigating climate change. Although the application of nitrogen fertilizers can enhance nitrogen leaching, integrating biochar may improve nutrient retention and reduce associated losses. However, the effects of biochar on nitrogen and carbon leaching in specific soil types remain unclear. This study investigated the impact of spruce bark biochar (SB550) on the leaching of total nitrogen (TN), nitrate (NO3−), ammonium (NH4+), and dissolved organic carbon (DOC) in agricultural soils of eastern Newfoundland. A greenhouse experiment was conducted with Festulolium forage grown in a soil–biochar mixture at five biochar rates (0, 2, 5, 8, and 10% v/v), with and without nitrogen fertilizer (0 and 60 kg N ha−1). The results showed that SB550 biochar significantly reduced nutrient and carbon losses (p < 0.001). At the 10% biochar rate, leaching of NO3−, NH4+, TN, and DOC decreased by 48.6%, 80.4%, 60.0%, and 74.3%, respectively, compared with the control. These findings confirm that the addition of biochar is an effective amendment for minimizing nitrogen and DOC leaching, offering a promising strategy for sustainable nutrient management and environmental protection in this soil type.
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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 | 0.000 | 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) | 0.000 | 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".