Z oysiagrass (Zoysia 1 spp. Willd. 2) is an introduced, perennial, sod-forming species in the United States that is well adapted for use as a turf in the transitional and warm climatic regions and requires minimal maintenance inputs. Three 3 primary species of zoysiagrass [Zoysia japonica Steud. 4 , Zoysia matrella (L. 5

Genetic linkage maps of bermudagrass (Cynodon spp.) species using 118 triploid individuals derived from a cross of T89 [C. dactylon (2n = 4x = 36)] and T574 [C. transvaalensis (2n = 2x = 18)] were enriched with expressed sequence tags-derived simple sequence repeat (EST-SSR) markers. Primers were developed from 53 ESTs containing SSRs producing 75 segregating markers from which 28 could be mapped to the T89 and T574 genetic maps. With the addition of previously generated marker data, 26 T89 linkage groups and eight T574 linkage groups were formed using a log-of-odds (LOD) value of 4.0. The T89 and T574 linkage maps spanned 1055 cM and 311.1 cM and include 125 and 36 single-dose amplified fragments (SDAFs), respectively. Many of the SDAFs displayed disomic segregation and thus T89 may be a segmental allotetraploid or an allotetraploid. The additional EST-SSR markers add value to the maps by increasing marker density and provide markers that can be easily transferred to other bermudagrass populations. Furthermore, EST-SSRs can be immediately used to assess genetic diversity, identify non-mutated cultivars of bermudagrass, confirm pedigrees, and differentiate contaminants from cultivars derived from ‘Tifgreen’.

arm-season grasses are characterized by the C 4 photosynthetic pathway. This pathway occurs in 18 families of fl owering plants, and 61% of the species belong to the grass family. C 4 plants are usually found between 30° N and 30° S latitudes; however, some species extend beyond this range (Moser et al., 2004). The C 4 pathway gives the warm-season grasses an advantage for performing in hot and dry climates and is one reason why these groups of grasses are found mainly in the tropics and subtropics (Clayton and Renvoize, 1986). Developing warm-season grasses for turf is a relatively new concept that began about 60 yr ago. Most of these grasses were used in their native habitat or introduced for use as forages because they could survive under low fertility and in extreme environments (such as drought). Many stoloniferous grasses are somewhat "plastic" and can be changed by management conditions to perform well for diff erent uses (e.g., forage, turf, water conservation, etc.). Cultivar Development Many of the warm-season turfgrass have genetically controlled self-incompatibility systems (see below), which aid in making crosses. If these systems are not present, then marker genes controlling stem, fl ower, and anther color can often be eff ectively used to distinguish self-pollinated plants from crosses. Plant material for individual crosses should be identifi ed and managed properly before pollination. Crossing can be accomplished in most of these turfgrass species by placing

Recent advances in remote sensing technology, especially in the area of Unmanned Aerial Vehicles (UAV) and Unmanned Aerial Systems (UASs) provide opportunities for turfgrass breeders to collect more comprehensive data during early stages of selection as well as in advanced trials. The goal of this study was to assess the use of UAV-based aerial imagery on replicated turfgrass field trials. Both visual (RGB) images and multispectral images were acquired with a small UAV platform on field trials of bermudagrass (Cynodon spp.) and zoysiagrass (Zoysia spp.) with plot sizes of 1.8 by 1.8 m and 0.9 by 0.9 m, respectively. Color indices and vegetation indices were calculated from the data extracted from UAV-based RGB images and multispectral images, respectively. Ground truth measurements including visual turfgrass quality, percent green cover, and normalized difference vegetation index (NDVI) were taken immediately following each UAV flight. Results from the study showed that ground-based NDVI can be predicted using UAV-based NDVI (R2 = 0.90, RMSE = 0.03). Ground percent green cover can be predicted using both UAV-based NDVI (R2 = 0.86, RMSE = 8.29) and visible atmospherically resistant index (VARI, R2 = 0.87, RMSE = 7.77), warranting the use of the more affordable RGB camera to estimate ground percent green cover. Out of the top ten entries identified using ground measurements, 92% (12 out of 13 in bermudagrass) and 80% (9 out of 11 in zoysiagrass) overlapped with those using UAV-based imagery. These results suggest that UAV-based high-resolution imagery is a reliable and powerful tool for assessing turfgrass performance during variety trials.

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