74-1 Bridging the Gap Between Remote Sensing and Turfgrass Management.

See more from this Division: C05 Turfgrass Science
See more from this Session: Turf and Pest Management
Monday, November 1, 2010: 1:15 PM
Long Beach Convention Center, Room 102B, First Floor
Share |

Dana Sullivan1, David Spak2, Richard Rees3, Brian Schwartz4 and Yale Leiden1, (1)TurfScout, LLC, Greensboro, NC
(2)Bayer Environmental Science, Research Triangle Park, NC
(3)Bayer Crop Science AG, Research Triangle Park, NC
(4)University of Georgia, Tifton, GA
For more than 40 years, remote sensing has been evaluated as a tool for managing or monitoring plant health.  Research has undeniably linked remotely sensed plant reflectance with variability in crop yield, nutrient requirements and water stress.    However, a majority of research has been conducted on agricultural commodities and many end-users were not equipped to handle spectral data processing and geographical analysis.  More recently, remote sensing in turfgrass management has begun to show promise as a tool to manage stress.  Yet, the methodology to deliver remotely sensed information in a rapid, useable format has not previously been available.  In response to this, a new tool designed to facilitate the use of ground-based reflectance data has been proposed:  TurfScout (TurfScout, LLC, Tifton GA).  The web-based system has recently (2009) been tested at two research facilities:  1) Bayer Environmental Science, Clayton, NC and 2) University of Georgia, Tifton Campus.  In both locations reflectance data were collected using a CropCircle (Holland Scientific, Lincoln NE), which measures light reflectance in the near-infrared and red and is equipped with a modulated light source to minimize the impact of ambient sun conditions.  Along with reflectance, a decimeter resolution global positioning unit recorded position.  All spectral data were processed using TurfScout.  The TurfScout online processing system was compared to traditional data handling for accuracy, sensitivity, forecasting stress, and time/labor investment.  Preliminary data assessments indicated remotely sensed data processing resulted in lower coefficients of variation within treatments and increased sensitivity to treatment effects.  At the Tifton location, remotely sensed data detected sensitivity to drought nearly one week prior to the manifestation of visual symptoms in a variety trial and increased overall productivity (time/labor) nearly eight-fold.
See more from this Division: C05 Turfgrass Science
See more from this Session: Turf and Pest Management