Nitrogen Rate and Mowing Height Affect Seasonal Performance of Zoysiagrass Cultivars
Bibliographic record
Abstract
Core Ideas Nitrogen rate had a greater impact on turfgrass quality of zoysiagrass when the grass was actively growing, but the effect of mowing height was only significant during spring green‐up. Nitrogen rate of 171 kg ha−1 was suitable for consistent turf performance in zoysiagrass and the effect of increasing N rate from 171 to 268 kg per ha was minimal. Japanese lawngrass and manilagrass can be successfully maintained at 2.5 or 5.0 cm and 0.6 or 1.2 cm, respectively, for equivalent performance during the majority of the year; however, during spring green‐up, the lower mowing height may deliver better turf performance. As new zoysiagrass (Zoysia spp.) cultivars are released, field studies on N responses and mowing heights conducted over several years under different environments are needed to determine best management practices. This study was initiated to (i) characterize a general response (color, density, turf quality) to N fertilization rate, mowing height, and their interactions among zoysiagrass cultivars; and (ii) establish appropriate mowing height and N rate recommendations for each of the cultivars studied. Four Japanese lawngrass cultivars (Z. japonica Steud.) and four manilagrass cultivars (Z. matrella L. Merr.) were evaluated in Citra, FL, for 4 yr and in Raleigh, NC, for 2 yr under three N rates (73, 171, and 268 kg ha−1 yr−1) and two mowing heights (2.5 and 5.0 cm for Japanese lawngrass; 0.6 and 1.2 cm for manilagrass). Genetic differences were evident among the zoysiagrass cultivars. Nitrogen rate had a greater impact on most of the observed characteristics when the grass was actively growing, but the effect of mowing height was only significant during spring green‐up. The medium N rate was suitable for consistent turf performance throughout the year and the effect of increasing N rate from 171 kg ha−1 to 268 kg ha−1 was minimal. Japanese lawngrass and manilagrass can be successfully maintained at 2.5 or 5.0 cm and 0.6 or 1.2 cm, respectively, for equivalent performance during the majority of the year. However, during spring green‐up, the lower mowing height may deliver better turf performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".