Rice Husk and Its Biochar Have Contrasting Effects on Water-Soluble Organic Matter and the Microbial Community in a Bamboo Forest Soil
Bibliographic record
Abstract
Converting rice husk to biochar is one of the solutions to manage crop residues by transforming waste into a value-added material that has broad benefits to the environment when biochar is applied to the soil. This study investigated the impact of the application of rice husk and its biochar at different doses (i.e., 0, 10, and 30 t ha−1) on soil carbon stability, the property of water-soluble soil organic matter, and the abundance and diversity of microbial communities in a Lei bamboo (Phyllostachys praecox) forest soil 262 days after their application. The application of rice husk, especially at 30 t ha−1, increased dissolved organic carbon due to the high labile carbon (C) (e.g., cellulose, hemicellulose, polysaccharides) content in the rice husk. The biochar treatments stimulated the release of humic-like substances (e.g., (poly) phenols) into the soil solution, increased the aromatic C content by 412–557%, and increased the relative abundance of Chloroflexi, Planctomycetota, and Proteobacteria compared to the control. This study shows that biochar application, particularly at 30 t ha−1, enhanced the C stability by turning organic C into recalcitrant forms in the soil, demonstrating the merit of converting rice husk into biochar before its application to the soil.
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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".