Long‐Term Biochar Application Improved Aggregate K Availability by Affecting Soil Organic Carbon Content and Composition
Bibliographic record
Abstract
ABSTRACT Straw biochar is an effective amendment at improving soil aggregate structure and increasing soil carbon and potassium (K) content. However, little information is available on the relationship between soil organic carbon (SOC) and aggregate‐associated K distribution under long‐term biochar application conditions. To address this, a field trial established in 2013 was used to examine the impact of biochar (B0: 0 and B1: 2.625 t ha−1 year−1) and K fertilizer (K0: 0 and K1: 60 kg ha−1 year−1) on the variation in soil aggregate K and reveal the associated influencing factors. A total of four treatments (B0K0, B0K1, B1K0, and B1K1) were included in this study. The soil analysis results obtained in 2021 showed that after 9 years' amendment, B1K1 increased the aggregate exchangeable K (EK) and nonexchangeable K (NEK) pools by 27.40% and 39.55%, respectively, and the increment was primarily because biochar enhanced > 0.25 mm aggregate fractions and strengthened soil K+ adsorption capacity, which benefit from a synergistic increase in SOC and humic acid (HA) content by biochar. 13C NMR analysis showed that long‐term biochar applications altered the chemical composition of SOC, with an outcome of increased aromaticity and hydrophobicity but decreased the lipidation of SOC, indicating that the complexity of SOC molecular structure was enhanced and eventually contributed to strengthening the macroaggregates stability and soil K+ adsorption capacity. The correlation analysis revealed that soil aggregate EK and NEK contents were positively correlated with SOC and HA contents, which further proved that increase of SOC and soil HA is a significant mechanism for biochar ameliorate soil aggregate‐associated K 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".