Biochar-biofertilizer combinations enhance growth and nutrient uptake in silver maple grown in an urban soil
Bibliographic record
Abstract
Declining tree health status due to pollutant impacts and nutrient imbalance is widespread in urban forests; however, chemical fertilizer use is increasingly avoided to reduce eutrophication impacts. Biochar (pyrolyzed organic waste) has been advocated as an alternative soil amendment, but biochar alone generally reduces plant N availability. The combination of biochar and either organic forms of N or Plant Growth Promoting Microbes (PGPMs) as biofertilizers may address these challenges. We examined the effects of two wood biochar types with Bacillus velezensis and an inactivated yeast (IY) biofertilizer in a three-month factorial greenhouse experiment with Acer saccharinum L. (silver maple) saplings grown in a representative urban soil. All treatments combining biochars with biofertilizers significantly increased sapling growth, with up to a 91% increase in biomass relative to controls. Growth and physiological responses were closely related to nutrient uptake patterns, with nutrient vector analyses indicating that combined biochar and biofertilizer treatments effectively addressed nutrient limitations of both macronutrients (N, P, K, Mg, Ca), and micronutrients (B, Fe, Mn, Mo, Na, S, and Zn). Biochar-biofertilizer treatments also reduced foliar concentrations of Cu, suggesting potential to mitigate toxic metal impacts common in urban forestry. We conclude that selected combinations of biochar and biofertilizers have substantial promise to address common soil limitations to tree performance in urban settings.
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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".