Turf Quality and Freezing Tolerance of ‘Tifway’ Bermudagrass as Affected by Late‐Season Nitrogen and Trinexapac‐Ethyl
Bibliographic record
Abstract
Bermudagrass [ Cynodon dactylon (L.) Pers.] is the most widely used species for intensively managed turf sites in the southern United States and in the transition zone. However, the lack of cold tolerance in many cultivars can result in significant winter injury. There is a limited body of information in the literature regarding management of bermudagrass to enhance cold tolerance, especially as it relates to N nutrition and the use of plant growth regulators (PGRs). As such, a 2‐yr field study (1998–1999 and 1999–2000) was conducted to examine the effects of late season N fertilization and trinexapac‐ethyl (TE) applications on morphology, quality, and freezing tolerance of ‘Tifway’ bermudagrass. During both years, monthly N applications were terminated on either 15 July, 15 August, or 15 September, while applications of TE were made on 15 August; 15 August and 15 September; or 15 August, 15 September, and 15 October. Late season applications of N and TE enhanced the fall green color retention of bermudagrass and promoted early spring green‐up (SGU). Neither N nor TE had a consistent effect on growth and development of bermudagrass rhizomes or stolons, and neither treatment had a consistent effect on the freeze tolerance of rhizomes. However, a positive attribute of these treatments is a significant increase in the overall green period of bermudagrass (20–25 d), which can prolong the playability of high maintenance sports facilities. From these studies we have concluded that, contrary to what is commonly believed, late season applications of N did not affect the freeze tolerance of bermudagrass rhizomes.
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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".