231-6 Relating Spectral Reflectance Measurement to Turfgrass Response.



Tuesday, October 18, 2011: 2:15 PM
Henry Gonzalez Convention Center, Room 008B, River Level

Alexander Kowalewski1, Brian Schwartz2, Dana Sullivan3 and Yale Leiden3, (1)Abraham Baldwin Agricultural College, Tifton, GA
(2)University of Georgia, Tifton, GA
(3)TurfScout, LLC, Greensboro, NC

Commercially available, ground-based spectral sensors have recently provided professionals in turfgrass science and industry with the ability to collect quantitative data, which is then used to make objective turfgrass color and quality ratings.  However, questions regarding the practical application of spectral response in turfgrass management have arisen.  For instance, how do cultural practices impact spectral response, secondly, how does spectral response relate to visual ratings, and finally, why does spectral response vary between some varieties?  A randomized complete strip block design with three replications was initiated May 2009 at the University of Georgia, Tifton Campus on a Tifton sandy loam soil.  Factors include bermudgrass (Cynodon dactylon x C. transvalensis) hybrids (whole-plot), mowing height (strip-plot) and plant growth regulator applications (strip-plot).  Bermudagrass cultivars included ‘Tifway' and three experimental hybrids with various morphological differences; color, density and leaf texture.  Mowing heights were 1.3 cm and 3.8 cm, and plant growth regulator applications include trinexapac-ethyl applied at a rate of 0.09 L a.i. ha-1 per month during the active growing season in comparison to a control treatment.  Spectral reflectance bands centered on 550 nm (blue), 650 nm (red) and 730 nm (near-infrared) were measured using an ACS470 CropCircle and used to derive NDVI and RVI values.  Visual ratings included color, quality and density, while digital images were collected to determine percent green cover and dark green color index (DGCI).  Other variables included chlorophyll content, leaf width and length, seed head count, clipping yield and leaf water content.  All data were collected July 14-15, 2011 and again July 21-22, 2011.  These variables were then analyzed to determine how mowing height and plant growth regulator applications impact reflectance variables, identify correlations between spectral reflectance variables and visual ratings, and finally determine how variations in cultivar morphology and physiology impact reflectance.

See more from this Division: C05 Turfgrass Science
See more from this Session: Genetics, Tolerance to Stresses, and Evaluations of Turfgrasses