Using Digital Image Analysis to Assess Tall Fescue Traffic Tolerance during Spring, Summer, and Autumn.

Recreational and sports turf playing surface quality can be improved with the establishment of traffic stress tolerant turfgrasses. Digital image analysis (DIA) has been used by researchers to evaluate turfgrass traffic tolerance. The objective of this field trial was to assess tall fescue (Schedonorous arundinaceus [Schreb.] Dumort.) traffic stress tolerance using DIA during spring, summer, and autumn. Three replications of 120 entries (including the 2012 NTEP Tall Fescue test) were seeded in September 2012 on a loam in North Brunswick, NJ. During 2016, sixteen traffic passes were applied each season as a strip across entries using a combination of the Rutgers Wear Simulator and the Cady Traffic Simulator during 13 Apr. to 31 May; 1 July to 17 Aug.; and 13 Sep. to 31 Oct. (1 pass wk^-1 with each machine during each 8 wk traffic period). Digital images were captured after each seasonal traffic period on no-traffic and traffic plots and DIA was used to determine green cover (%). Data were analyzed as a 2 (no traffic and traffic) x 120 (entries) factorial strip-plot design. Traffic reduced green cover of all entries compared to no-traffic during fall; however, DIA was unable to discern no-traffic and traffic plots during summer. During spring, there was no change in green cover for most entries but, interestingly, the green cover increased under traffic for 21 entries. Forty-five entries had the greatest green turf cover after autumn traffic when traffic was most effective at reducing green cover. The inability of DIA to detect green cover differences during spring and summer can be attributed to low levels of damage caused by traffic and thus more subtle turfgrass canopy differences that image analysis of only green color cannot detect.