Remote Sensing Technology for Early Detection of Root Decline in Putting Green Turfgrass
Matthew Tucker: Mississippi State University
<div>Root decline is associated with ultradwarf bermudagrass (UDB) greens during the summer months in the southeastern United States. Abiotic factors coupled with a complex of fungal root pathogens from the genera <em>Gaeumannomyces</em>, <em>Magnaporthiopsis</em>, and the novel, <em>Candidacolonium cynodontis</em> contribute to this syndrome. Remote sensing technologies (RST) are commonly used in precision agriculture; however, management of amenity turfgrasses have not kept pace. The goal of this study is to demonstrate the application of RST for use in monitoring UDB for plant health and the distribution of root decline pathogens within putting greens. A fishnet grid system using ArcGIS was established for sampling (n = 254) putting greens. Aerification cores were collected within 2.4 m<sup>2</sup> of each centroid. Composite root samples were analyzed for root health using WinRhizo Pro and fungal identification was based on multiplex qPCR assays. Normalized Difference Vegetation Index (NDVI) using a drone and visual turfgrass quality ratings were conducted when UDB was actively growing. To date, <em>Magnaporthiopsis</em>, <em>C. cynodontis</em>, and <em>Gaeumannomyces</em> spp. were detected in association with UDB roots at 55, 15, and 10%, respectively. NDVI ratings (scale=0–1; 1=highest reflectance) in those quadrats were ≤ 0.375 and root health averaged < 10%. Results of this research may serve as a model to integrate RST into turfgrass management for early disease detection and reduced fungicide inputs.</div>
View Presentation |
