Long‐Term Biochar Application Improved Aggregate K Availability by Affecting Soil Organic Carbon Content and Composition
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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 (B 0 : 0 and B 1 : 2.625 t ha −1 year −1 ) and K fertilizer (K 0 : 0 and K 1 : 60 kg ha −1 year −1 ) on the variation in soil aggregate K and reveal the associated influencing factors. A total of four treatments (B 0 K 0 , B 0 K 1 , B 1 K 0 , and B 1 K 1 ) were included in this study. The soil analysis results obtained in 2021 showed that after 9 years' amendment, B 1 K 1 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. 13 C 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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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 it