Leguminous green manure intercropping changes the soil microbial community and increases soil nutrients and key quality components of tea leaves
Bibliographic record
Abstract
Intercropping, a green and sustainable planting pattern, has demonstrated positive effects on plant growth and the soil environment. However, there is currently little research on the influence of intercropping leguminous plants and using them as green manure on the soil environment and tea quality. During the profuse flowering period of Chinese milkvetch, the contents of tea amino acids and soluble sugar in intercropping tea plants with soybean increased by 6.89 and 54.58%. Moreover, there was 27.42% increase in soil ammonium nitrogen and 21.63% increase in available nitrogen. When Chinese milkvetch was returned to soil for 1 month during its profuse flowering period, the soybean and Chinese milkvetch as green manure enhanced tea amino acids and soluble sugar by 9.11 and 33.96%, and soil ammonium nitrogen, nitrate nitrogen and available nitrogen increased by 25.04, 77.84, and 48.90%. Intercropping systems also have positive effects on tea quality components, soil fertility, and soil microbial communities during the profuse flowering period of soybeans and when soybeans with this period were returned to the field for 1 month. Furthermore, the soil fertility index was significantly increased, especially in the intercropping system of tea-soybean-Chinese milkvetch. The soil bacterial community complexity and fungal community interactions were significantly increased. Soil pH, nitrate nitrogen, and available phosphorus were found to be crucial influencing factors on soil microbial communities, specifically bacterial communities. These results highlight the significance of optimizing intercropping systems to improve the soil environment and tea quality components. They also provide a theoretical foundation for promoting the sustainable development of tea plantations.
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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) | 0.001 | 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".