Long‐term biochar application promoted soil aggregate‐associated potassium availability and maize potassium uptake
Bibliographic record
Abstract
Abstract Biochar is an effective ameliorator for soil quality improvement and nutrient reuse from biomass; however, the effect of biochar application on soil potassium (K) availability, plant K uptake, and the underlying mechanisms have not been well‐elucidated. To address this, the variation in the soil K forms, soil aggregate stability, and aggregate‐associated K concentration, as well as maize K uptake, were investigated in a field experiment after 9 years of biochar amendment. The treatments included no biochar and NPK fertilizer (CK); NPK fertilizer treatment (F); biochar applied annually at the rate of 2.625 t ha −1 (C 1 ), and biochar applied annually at rate of 2.625 t ha −1 with NPK fertilizers (C 1 F); one‐time biochar applied with NPK fertilizers, with biochar rate of 31.5 (C 2 F) and 47.25 t ha −1 (C 3 F). The results showed that after 9 years of field application, biochar inhibited the downward K migration to the deeper layer, thus increasing water‐soluble potassium (WSK), exchangeable potassium (EK), non‐exchangeable potassium (NEK), and total potassium (TK) in 0–20 cm soil, with C 1 F exhibiting better performance than C 2 F and C 3 F. Biochar also increased aggregate‐associated EK, NEK, and TK pools, mainly due to an increase in the macroaggregate proportion (>0.25 mm). Biochar amendment promoted maize K uptake by an average of 35.69%, the path analysis indicated that the positive effect was an outcome of the synergetic effect of the increase in surface soil WSK content and promoted macroaggregate EK pools, which was primarily attributed to biochar improved soil properties, including soil organic carbon, pH, total nitrogen, total phosphorus, and cation exchange capacity. These factors explained 76% of the variance in maize K uptake. In conclusion, biochar is an effective ameliorator for improving soil K content and availability.
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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 | 0.001 |
| 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".