Winter-applied Glyphosate Effects on Spring Green-up of Zoysiagrasses and ‘Yukon’ Bermudagrass in a Transition Zone
Bibliographic record
Abstract
In transitional environments, turf managers and sod producers of warm-season grasses face the issue of winter annual weeds that can dominate dormant turf stands through the winter until late spring. The use of glyphosate to control weeds in dormant bermudagrass ( Cynodon dactylon ) has been well documented, but information is lacking about its effect on spring green-up of other warm-season grasses. A field study was conducted on two commercial sod farms in northern Italy (Expt. 1) to evaluate the effects of glyphosate applied on two different winter dates on weed control and spring green-up of ‘Zeon’ manilagrass ( Zoysia matrella ). A second study was carried out at the experimental agricultural farm of Padova University (Expt. 2) to assess the effects of a winter application of glyphosate on weed control and spring green-up of ‘Yukon’ bermudagrass and ‘Companion’ zoysiagrass ( Zoysia japonica ). Each experiment was conducted from Jan. to June 2011, and glyphosate was applied at 1.1 kg·ha −1 on 8 and 21 Feb. in Expt. 1 and on 8 Feb. in Expt. 2. Spring recovery was evaluated by periodical visual ratings of green turf cover and by collecting normalized difference vegetation indices (NDVIs). Weed injury was visually evaluated on all plots 7 weeks after the 8 Feb. glyphosate application. The visual ratings of green cover were strongly and positively correlated with NDVI measurements. Glyphosate applied in February as a single treatment effectively controlled winter weeds in ‘Zeon’ manilagrass (Expt. 1) and ‘Yukon’ bermudagrass (Expt. 2) without negatively affecting spring green-up. In contrast, spring green-up of ‘Companion’ zoysiagrass (Expt. 2) was delayed by the application of glyphosate.
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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) | 0.001 | 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".