In Kermit the Frog’s signature ballad, “It’s Not Easy Being Green,” Kermit struggles with his green nature, but eventually accepts his greenness and is actually quite pleased about it.
Turfgrass is green, of course, but it’s those vast shades of green that spark debate as to what’s the preferred green color for turf. Some prefer the darkest green, while others prefer lighter hues similar to emeralds, a Granny Smith apple or a lime.
There is no best or worst shade of green, as long as the turfgrass is healthy. Turfgrass color through the seasons can be used to indicate overall plant health and also possible nutrient deficiencies; damage or injury caused by disease, insect pests or environmental stresses; mechanical or equipment-imposed stresses; or even phytotoxicity from a product application.
Turf playability and performance are not influenced by genetic color of a species or cultivar; however, color can make a difference with the aesthetic appeal of a stand. For example, on creeping bentgrass putting greens, visual uniformity may be important when blending two or more cultivars. Creeping bentgrasses tend to segregate over time, particularly seen in late fall and winter, so a darker green cultivar blended with lighter green cultivars results in a patchy or mottled surface. This may be an acceptable putting surface to some but not others. In those situations, the yellow-green color of annual bluegrass makes it stand out more among the darker green creeping bentgrasses.
As another example, for athletic fields, where three or four cultivars of Kentucky bluegrass are blended together, it’s important that the shades of green among those cultivars are compatible and pleasing to the eye versus one dark green cultivar appearing as polka dots against a canvas of light green.
In research plots, scientists have traditionally measured and interpreted turf color on a visual basis. The first documented use of visual color to evaluate turf research plots was back in 1934, in a report from the Rhode Island Agricultural Experimental Station. Since then, there have been several studies on ways to improve visual evaluation techniques of turf color and other parameters. In the early 1980s, the National Turfgrass Evaluation Program formalized the visual 1-9 scale, where 9 = darkest green color, and 1 = straw brown. Why not a 1-10 scale? The story goes that at the time, the computer program only allowed for one single-digit entry, thus the 1-9 subjective-qualitative scale used today.
In the early 2000s, along with the advent of digital photography replacing 35 mm film cameras, Doug Karcher, Ph.D., and Mike Richardson, Ph.D., at the University of Arkansas developed a method using digital image analysis to quantify turfgrass color, resulting in an objective dark green color index (DGCI), which has become the standard practice for turf research. A digital camera is mounted onto a lightbox (a metal box with lightbulbs inside) with an opening for the lens to peer through. The box is placed over the plot and an image captured. The image is then analyzed by a computer program to calculate the DGCI number.
Recently, a study was conducted on bermudagrass to evaluate the analysis of those digital images with the color-distance modeling algorithm commonly used in color science. The model was used to determine whether a color difference from a turfgrass image would likely be perceptible to the human eye. This is important, because slight differences in DGCI values might go completely unnoticed and skew research results. With bermudagrass color evaluations, this method further validated the accuracy of the DGCI.
But still, there’s something about our human eye that can draw upon many years of experience to integrate our visual perception of turf color, density, uniformity, stand and surface characteristics, and overall plant health — elements that a digital image and computer program can’t quite capture yet. So, like the song says, “It’s not easy being green.” And if you like Kermit’s song, Ray Charles’ version is great too.
Source: Berndt, W.L., D.E. Karcher and M.D. Richardson. 2020. Color-distance modeling improves differentiation of colors in digital images of hybrid bermudagrass. Crop Science 60(4):2138-2148 (https://doi.org/10.1002/csc2.20158).
Mike Fidanza is a professor of plant and soil science in the Division of Science at the Penn State University Berks Campus in Reading, Pa. He is a 19-year member of GCSAA.