Improving Soil Fertility and Forage Production Using Spruce Bark Biochar in an Eastern Newfoundland Podzolic Soil
Bibliographic record
Abstract
Biochar has been widely used in agriculture to improve soil quality, support soil remediation, enhance carbon sequestration, and mitigate climate change. Podzolic soils, such as those in Newfoundland, are typically acidic, low in organic matter, and poor in nutrients, which can limit their agricultural productivity. Applying biochar alongside nitrogen fertilization presents a promising strategy to enhance soil fertility, nutrient uptake, and forage productivity. This study evaluated the effects of spruce bark biochar (SB550) and nitrogen fertilization on soil properties, nutrient uptake, and Festulolium forage growth under greenhouse conditions in podzolic soils of Newfoundland, Canada. Five biochar rates (0%, 2%, 5%, 8%, and 10% by soil volume) were combined with two nitrogen levels (0 and 60 kg N ha−1). Soil analyses included pH, soil organic matter (SOM), cation exchange capacity (CEC), and nutrient availability (Ca, Mg, K, P, S, Zn, Mn, and B). In contrast, forage nutrient uptake, biomass production, and quality were assessed. Results showed that biochar significantly increased soil pH, SOM, CEC, and nutrient availability for key elements such as Ca, Mg, and K, while reducing potentially harmful elements such as Na and Mn. The Festulolium nutrient uptake and biomass improved, with dry matter and root biomass increasing by up to 32%. The combined application of biochar and nitrogen further amplified these benefits. This study highlights the potential of biochar as a sustainable soil amendment for improving soil properties and forage productivity in podzolic soils. The findings suggest that biochar, particularly with nitrogen, can significantly enhance soil fertility and agricultural productivity, making it a viable strategy for sustainable forage production in Newfoundland.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".