Regulating Environmental Factors of Nutrients Release from Wheat Straw Biochar for Sustainable Agriculture
Bibliographic record
Abstract
Abstract One mechanism by which biochar application enhances soil nutrient availability is through direct nutrients release from biochar. However, factors controlling the release processes are poorly understood. In this study, the effects of pH, biochar to water ratio, temperature, ionic strength, and equilibration time on the release of PO , NO , NH , K+, Na+, Ca2+, and Mg2+ from biochar were evaluated in simulated experiments. The release of PO , K+, Ca2+, and Mg2+ was significantly affected by extraction pH, suggesting that their release from biochar was pH dependent or an H+‐consuming process. Correlation analysis indicated that PO and Ca2+, PO and Mg2+, and Ca2+ and Mg2+ were co‐solubilized with increasing soil acidity. To a lesser extent, the recovery of the nutrients was also affected by the ratio of biochar to water: more nutrients were soluble with more water supply. In contrast, the release of Na was not affected by pH while the concentration increased with decreasing biochar to water ratio. Meanwhile, other factors (temperature, ionic strength, and equilibration time) had less effect on nutrient release from biochar. Under the influence of pH, the patterns of NO and PO release from biochar were different: extractable NO concentration was not affected by the pH but more PO was released in strongly acidic conditions. Our data suggested that P was mainly retained in inorganic forms while N was in organic forms in biochar. We conclude that environmental factors have marked influences on nutrients release from biochar.
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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".