Long-term biochar application influences phosphorus and associated iron and sulfur transformations in the rhizosphere
Bibliographic record
Abstract
Abstract The effects of long-term biochar application on soil phosphorus (P) flux across the root-soil interface and its availability in the rhizosphere of rice ( Oryza sativa L) remain unclear. We used diffusive gradients in thin films (DGT), laser ablation-inductively coupled plasma mass spectrometry, and planar optode sensor techniques to characterize, in-situ , the 2D heterogeneity and dynamics of rhizosphere soil P, iron (Fe), sulfur (S) and trace element fluxes, dissolved oxygen and pH in paddy soil, after 10 years of biochar application. Compared to the control (no biochar applied), biochar applied at 4.5, 22.5 and 45.0 Mg ha −1 yr −1 decreased rhizospheric P fluxes by 11.6%, 63.4% and 79.0%, respectively. This decrease under biochar treatments was attributed to changed redox status of Fe and S caused by the lower dissolved oxygen in rhizosphere soil and increased soil pH induced precipitating of soluble inorganic P into insoluble P forms, such as calcium-bound and residual P that are unavailable for crop uptake. Higher application rate of biochar resulted in lower As and Pb fluxes in rice rhizosphere and their availabilities for crop uptake. The in-situ observation results in rice rhizosphere at μm-scale after 10 years of biochar addition directly showed the complex effects of long-term biochar and rhizosphere heterogeneity on P transformation process. Graphical Abstract
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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) | 0.001 | 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".