Effect of rice-husk biochar on selected soil properties in tropical Alfisols
Bibliographic record
Abstract
Despite the large number of studies on biochar and soil properties, few studies have investigated the effects of biochar in contrasting soils. A study was conducted including four rice-husk biochar rates (0%, 0.1%, 0.5% and 1%) to understand the effects on selected soil properties of two Alfisols (sand and sandy loam) in Sri Lanka. Significant changes in soil properties including increases in pH, cation exchange capacity (CEC), organic carbon, water retention at field capacity and saturated hydraulic conductivity, and reduction in bulk density, were observed at higher rates of biochar (0.5% and 1%). Mean-weight-diameter increased only at 1% biochar application rate in sandy soil, whereas it significantly increased across all the rates in sandy loam soil over the control. Electrical conductivity showed no significant increase in either soil, indicating no threat of salinity. Biochar showed a potential for ameliorating acidity, especially in slightly acidic sandy soil. Soil aggregation and water flow improved markedly in sandy loam soil over sandy soil. Further, CEC and water retention of sandy soil had pronounced effects compared with sandy loam soil. Our study highlights the importance of soil type in determining the value of rice-husk biochar as a soil amendment to improve soil aggregation, water retention and flow and CEC.
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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".