Container-grown Shrubs Respond Differently to Controlled-release Fertilizer Rates in a Temperate Climate
Bibliographic record
Abstract
To determine the response of container-grown shrubs to controlled-release fertilizer (CRF) rate when grown in a temperate climate, Polyon® 19–04–10 + Minors, an 8–9 month CRF, was incorporated into growing substrates for ‘Gro-Low’ fragrant sumac (Rhus aromatica Aiton), ‘Goldmound’ spirea (Spiraea × bumalda Burv.) and ‘Bloomerang’® purple lilac (Syringa × ‘Penda’) transplants. Also, a 15–06–11 + Micros, a 10–12 month CRF, was incorporated into growing substrates for ‘Green Mound’ boxwood (Buxus × ‘Green Mound’), ‘Runyan’ yew (Taxus × media) and ‘Emerald’ white-cedar (arborvitae) (Thuja occidentalis L.) transplants, at six rates (0.15, 0.45, 0.75, 1.05, 1.35 and 1.65 kg·m−3 N; 0.25, 0.76, 1.26, 1.77, 2.28 and 2.78 lb·yd−3 N). We observed greater growth index, leaf area, and shoot dry weight at high vs. low CRF rates for the majority of species. Nutrient deficiency symptoms such as light green leaves were observed at low CRF rates for some species, including fragrant sumac, lilac and white-cedar. Optimal species-specific CRF application rates were 1.05 kg·m−3 N (1.77 lb·yd−3 N) for lilac and yew and 0.45 kg·m−3 N (0.76 lb·yd−3 N) for boxwood and white-cedar, while the optimal CRF ranges were 0.75 to 1.35 kg·m−3 N (1.26 to 2.28 lb·yd−3 N) for fragrant sumac and 0.75 to 1.05 kg·m−3 N (1.26 to 1.77 lb·yd−3 N) for spirea. Adjusting CRF application rates based on plant response may provide nursery growers with an efficient tool for managing nursery crop growth and production timing in the temperate climate.
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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 | 0.001 |
| 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".