Turf Quality and Species Dynamics in Bermudagrass and Kentucky Bluegrass Mixtures
Bibliographic record
Abstract
Core Ideas In transitional environments, mixtures of bermudagrass and Kentucky bluegrass can provide green cover year‐round. In mixtures, cultivars of bermudagrass characterized by slow green‐up favor the survival of Kentucky bluegrass. The choice of Kentucky bluegrass cultivars has limited practical impact on the performance of mixtures with bermudagrass. Climatic changes and the need to reduce water consumption for irrigation have led to expanded use of warm‐season turf species in transitional zones. Turf managers are often hesitant to use warm‐season species because they undergo dormancy for a long period during the winter. Although this issue might be addressed by mixing cool‐ with warm‐season species, there is a lack of information on the performance and dynamics of species succession in such turfgrass mixtures. A 2‐yr investigation was conducted in Legnaro, Italy, and Fayetteville, AR, to test the turf quality and species succession in mixtures of various cultivars of bermudagrass (BG) [ Cynodon dactylon (L.) Pers.] with Kentucky bluegrass (KBG) ( Poa pratensis L.). Bermudagrass cultivars, Yukon and Veracruz, were seeded in June 2011 at 5 g m −2 and KBG cultivars Brooklawn, Mystere, and Nublue Plus were overseeded in September 2011 at 30 g m −2 . Across both studies, the frequency of BG in the mixture was generally higher for Yukon and ranged from 40 to 95%. However, the mixtures with Veracruz had superior turf quality in Legnaro from October 2012 to March 2013. The species succession was influenced by BG cultivars, whereas KBG cultivar had little effect on the rate of plant composition change. On the basis of these results, the choice of BG cultivar appears critical for establishing functional KBG and BG mixtures in transitional zones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".