Slow-Release Fertilizer Improves the Growth, Quality, and Nutrient Utilization of Wintering Chinese Chives (Allium tuberosum Rottler ex Spreng.)
Bibliographic record
Abstract
Excessive application of fertilizers leads to the loss of a high amount of nutrients and low fertilizer utilization, which severely restricts crop productivity. Establishing better fertilizer usage practices can mitigate the adverse effects of excessive fertilizer use in agricultural practices. This study determined the effects of slow-release fertilizers on the growth; quality; root and nitrate reductase activity; accumulation; distribution of nitrogen (N), phosphorus (P), and potassium (K) in roots, stems, and leaves; and NPK utilization of winter Chinese chives (Allium tuberosum Rottler ex Spreng.) in multi-layer covered plastic greenhouses. Treatments were conventional fertilization (CF, NPK: 1369.5 kg ha−1), conventional fertilization with slow-release fertilizer (SRF, NPK: 1369.5 kg ha−1), reduced fertilization with slow-release fertilizers (SRFR, NPK: 942.0 kg ha−1), and no fertilizer arranged in a completely randomized design with three replicates. The SRFR treatment increased Chinese chives yield and economic profitability by 37% and 47%, respectively, compared to the CF treatment. Similarly, nitrate reductase activity, root activity, soluble sugar, soluble protein, and flavonoid contents in the Chinese chives were increased by 40%, 12%, 16%, 6%, and 18%, respectively, in SRFR than CF. In addition to these, we observed a significant reduction in the surplus of N (42%) and P (58%) in soil under SRFR compared to CF. Nutrient uptake and nutrient use efficiency were also greater in SRFR than in CF. The results indicate that the adoption of SRFR can be an efficient approach to enhance quality and productivity of Chinese chives compared to CF under a multi-layer covered plastic greenhouse system.
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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) | 0.001 | 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".