ẢNH HƯỞNG DÀI HẠN CỦA PHÂN HỮU CƠ TRONG CẢI THIỆN ĐỘ PHÌ NHIÊU ĐẤT VÀ NĂNG SUẤT TRÁI CHÔM CHÔM (NEPHELIUM LAPPACEUM L.) TẠI CHỢ LÁCH - BẾN TRE
Bibliographic record
Abstract
Application of inorganic fertilizers with high dose and unbalances has led to the decline of soil fertility in-terms of physicochemical, biological properties of fruit orchards. The objective of this experiment was to determine the long-term effect of organic compost amendment in improving soil properties and fruit yield. The experiment was conducted in six crops of rambutan with three different composts including sugarcane filter mud, biogas sludge and vermi-compost. Compost was amended at a rate of 18 kg.plant-1 combined with a low dose of inorganic fertilizers in comparison with farmer?s practice. The results showed that application of three kinds of compost with low and balanced nutrients of inorganic fertilizers resulted in significant increase of soil pH, soil organic matter content, soil available nitrogen and phosphorus, exchangeable K and calcium, base saturation percentage, soil structure stability, bulk density, soil respiration in comparison to farmers? practice (p < 0.05). From the third crops after organic amendment, fruit yield was significantly increased, 60 ? 136% compared with those in farmers? practice. Fruit weight was higher by measuring the number of fruit per kg in treatments supplied with biogas sludge and sugarcane fillter mud. The results need to be recommended to farmers for using compost and reducing inorganic fertilizers to improve soil fertility and to enhance rambutan fruit yield and to increase income for farmers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".