Salinity Tolerance and Recovery Attributes of Warm-season Turfgrass Cultivars
Bibliographic record
Abstract
As population growth places greater pressures on potable water supplies, nonpotable recycled irrigation water is becoming widely used on turfgrass areas including golf courses, sports fields, parks, and lawns. Nonpotable recycled waters often have elevated salinity levels, and therefore turfgrasses must, increasingly, have good salinity tolerance to persist in these environments. This greenhouse study evaluated 10 commonly used cultivars representing warm-season turfgrass species of bermudagrass ( Cynodon spp . ), zoysiagrass ( Zoysia spp . ), st. augustinegrass [ Stenotaphrum secundatum (Walt.) Kuntze], and seashore paspalum ( Paspalum vaginatum Swartz) for their comparative salinity tolerance at electrical conductivity (EC) levels of 2.5 (control), 15, 30, and 45 dS·m –1 . Salinity treatments were imposed on the grasses for 10 weeks via subirrigation, followed by a 4-week freshwater recovery period. Attributes, including turf quality, the normalized difference vegetation index (NDVI), canopy firing, and shoot biomass reductions were evaluated before and after salinity stress, as well as after the 4-week freshwater recovery period. Results showed considerable differences in salinity tolerance among the cultivars and species used, with the greatest tolerance to elevated salinity noted within seashore paspalum cultivars and Celebration ® bermudagrass. In comparison with growth in 2.5-dS·m –1 control conditions, increased shoot growth and turf quality were noted for many bermudagrass and seashore paspalum cultivars at 15 dS·m –1 . However, st. augustinegrass and some zoysiagrass cultivars responded to elevated salinity with decreased growth and turf quality. No cultivars that had been exposed to 30- or 45-dS·m –1 salinity recovered to acceptable levels, although bermudagrass and seashore paspalum recovered to acceptable levels after exposure to 15-dS·m –1 salinity. More severe salinity stress was noted during year 2, which coincided with greater greenhouse temperatures relative to year 1.
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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) | 0.001 | 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".