Effects of Co-Application of Biochar and Nitrogen Fertilizer on Soil Properties and Microbial Communities in Tea Plantation
Bibliographic record
Abstract
Soil acidification reduces the abundance and activity of beneficial microorganisms, impairs tea plant growth, and ultimately leads to a decline in tea quality. Maintaining healthy soil is critical for sustainable tea agriculture. However, the interactive effect of biochar and nitrogen fertilizer on the microbial community structure and function in acidic tea plantation soils remains unclear. This study was designed to explore whether the co-application of biochar and fertilizer could enhance soil properties and maintain microbial health in tea plantations. Three treatments were set up through a controlled pot experiment: no fertilizer or biochar application (B0N0), fertilizer without biochar (B0N1), and biochar with fertilizer (B1N1). High-throughput sequencing technology was used to investigate the characteristics of soil microbial communities in tea plantations. Biochar amendment increased soil pH by 0.8 units, organic matter and total nitrogen by 13.5% and 21.4%, and reduced NH4+-N and NO3−-N leaching by 10.8% and 12.9%, respectively. It also modulated microbial community structure, enhanced the abundance of nitrogen-cycling genes (e.g., narB, nirK, nosZ), and influenced nitrogen availability through adsorption. Nitrate was identified as the main factor shaping microbial communities under fertilization. These results highlight the potential of biochar as a sustainable amendment to improve soil health and nitrogen retention in tea cultivation systems. Further field studies are warranted to validate its efficacy in enhancing tea productivity and reducing environmental nitrogen losses under real-world conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".