Influence of Three Nitrogen Fertilization Schedules on Bermudagrass and Seashore Paspalum: I. Spring Green‐up and Fall Color Retention
Bibliographic record
Abstract
ABSTRACT A primary concern in managing warm‐season turfgrasses within the transition zone is the lengthy dormant period, during which these swards lack green color. The objectives of this study were to determine the effects of three N fertilization schedules on spring green‐up and fall color retention of bermudagrass [Cynodon dactylon (L.) Pers.] and seashore paspalum (Paspalum vaginatum Sw.). A field trial was performed at the agricultural experimental farm of Padova University (northeastern Italy). Bermudagrass cultivars Princess‐77, Riviera, SWI 1014, and Yukon and seashore paspalum ‘Sea Spray’ were compared under three N fertilization schedules: (i) 6.7 g N m−2 on 15 May, 15 June, and 15 August, (ii) 5 g N m−2 on 15 May, 15 June, 15 August, and 15 October, and (iii) 4 g N m−2 on 15 May, 15 June, 15 August, 15 September, and 15 October. Spring green‐up was estimated weekly as a percent green turfgrass coverage from 15 March to 15 June of 2010 and 2011. Fall color retention was visually rated from September to November of 2010 and 2011. Sea Spray seashore paspalum had later spring green‐up and better fall color than the bermudagrass cultivars, which differed widely in terms of spring green‐up and fall color retention. Fall‐applied N enhanced green‐up of all the grasses tested and extended fall color retention of bermudagrass cultivars. This study revealed that protracting applications of N fertilizer until late season may improve quality performance of warm‐season grasses without increasing annual N applied.
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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 | 0.001 |
| 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".