Effect of Fertilizer Rate on Plant Growth and Leachate Nutrient Content during Production of Sedum-vegetated Green Roof Modules
Bibliographic record
Abstract
With the increasing popularity of green roofs, efficient green roof plant production is required to adequately supply the industry. Applying fertilizer at an appropriate rate can provide sufficient plant nutrition for efficient plant growth without excess nutrient leaching into the environment. This study compared rates of controlled-release fertilizer (CRF) applied to green roof modules at the plant production stage to determine an optimum CRF rate for encouraging plant growth and vegetative coverage while minimizing the amount and concentration of leached nutrients. After sedum cuttings were rooted in green roof modules on 29 Aug. 2011, CRF was applied at 5, 10, 15, 20, 25, 30, and 35 g·m −2 nitrogen (N) and modules were compared with an unfertilized control. Plant growth, vegetative coverage, and overall appearance requirements were met after fertilization at 20 g·m −2 N. Modules fertilized at less than 20 g·m −2 N did not reach the target proportion coverage during the study. When fertilized at 20 g·m −2 N, green roof modules reached the target proportion coverage after 240 days of growth. Differences in leachate volumes were observed among treatments 35 days after fertilization and fertilization at 20 g·m −2 N minimized leaching of most nutrients. Therefore, with the green roof module system used in this study, an application of 20 g·m −2 N for green roof module or sedum cutting production is an optimum CRF rate for plant growth and vegetative coverage while minimizing negative environmental impacts.
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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".