The impact of bag-controlled release fertilizer on the site conditions and growth of Lei bamboo (Phyllostachys violascens) forests
Bibliographic record
Abstract
The use of fertilizers has become a growing problem worldwide. Various techniques have been developed to ensure that fertilizers are delivered in the right amounts and at the right time. We investigated the effects of bag-controlled release fertilizer (BCRF) on site conditions and growth of Lei bamboo, aiming to optimize fertilization strategies. Experiments compared broadcast fertilization (15 kg/S15; 30 kg/S30) with equivalent BCRF doses (15 kg/B15; 30 kg/B30) and no fertilizer (CK) across bamboo ages (1–3 years, T1–T3) and BCRF quantities (1–7 bags). Key findings revealed BCRF's superior efficiency, in 10–20 cm soil, B15 increased total nitrogen by 27.03 % than CK, and 27.10 % higher than the broadcast fertilizer treatment. Leaf total phosphorus under B15 was 28.13 % higher than S15 and 64.00 % above CK. BCRF-enhanced nutrient retention boosted bamboo shoot yields, with B15 outperforming S15 by 31.81 %. Age significantly influenced nutrient uptake, 3-year-old bamboo stands (T3) showed higher soil available nutrients than younger bamboo 5 months post-fertilization. The nutrient content in the leaves of 1-year-old bamboo was the highest, and the PI ABS value of the photosystem was also the highest, 59.52 % and 131.03 % higher than that of the 2- and 3-year-old bamboo, respectively. Optimal BCRF application involved 3 bags (600 g total), achieving soil-available potassium levels 28.70 % higher than single-bag treatments and 104.41 % above CK after 300 days, with comparable results to heavier 5–7 bags applications. While the 3 bags application maximized internode length and diameter at breast height, increasing to 5–7 bags caused significant reductions of 7.25 %–16.28 % from peak values. In conclusion, BCRF can reduce fertilizer use while ensuring the nutritional growth of Lei bamboo. Therefore, it can be applied as an efficient fertilization method in the management of Lei bamboo. • Appropriate fertilizers use boosts bamboo shoot yield. • Bag-controlled release fertilizers outperform broadcast fertilizers application. • Applying more fertilizer beyond the optimal amount produce no significant gain. • Bag-controlled release fertilizers reduce eutrophication and gaseous emission risks.
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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".