Evaluating the effect of biochar rate and combination with fertilizer on the dynamics of soil nitrogen supply in tea plantation
Bibliographic record
Abstract
Tea plantations commonly receive substantial quantities of nitrogen (N) fertilizer, with potential for considerable N loss to occur. This study assessed N retention in acidic tea plantation soil and examined how different biochar application rates and fertilizer combinations affect N dynamics, highlighting the importance of innovative technologies to monitor and enhance N supply management. This research adopted a modified 2-week aerobic incubation and ion-exchange membrane (IEM) techniques to evaluate the soil N supply in tea plantations following early-summer top-dressing as influenced by various biochar rates and fertilizer combinations. We quantified the amount of mineralized N in acidic tea plantation soils during the summer. Our results show that biochar enhances soil N supply not by increasing N mineralization directly but by improving soil mineral N retention. Notably, a threshold effect was identified at biochar application rates of 20–30 tonnes ha −1 . The window for maximizing the effectiveness of inorganic fertilizers applied during the summer months in tea plantations could only be 2–4 weeks. The use of biochar-based organic fertilizers can enhance this period by enhancing N retention and availability in the soil. Measuring N mineralization potential via aerobic incubations and N exposure using IEM technology effectively elucidated soil N dynamics during summer period.
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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".