As golfer traffic increases, golf course superintendents are paying closer attention to how repeated foot traffic impacts putting green quality.
A new study from Cornell Turfgrass researchers, published in the International Turfgrass Society Research Journal, found that predicting putting surface disruption based on golfer traffic remains elusive.
“Since COVID, the increase in golf rounds has been measurable and noticeable to superintendents managing putting greens,” said Carl Schimenti, urban environmental scientist at the Cornell Turfgrass Program.
Golf participation surged by 38 percent after the pandemic, according to the National Golf Foundation, raising concerns about long-term effects on green performance.
Putting greens are among the most intensively managed areas on a golf course. Nearly 43 percent of all golf shots occur on the green, with surface performance—particularly within three meters of the hole—under constant scrutiny from players. Maintaining smooth, consistent surfaces in this area is critical to both playability and course reputation.
The team sought to understand what factors, if any, could reliably predict putting surface disruption (PSD). The study examined two key indicators of PSD: visual spike damage and changes in green speed (ball roll distance).
Among those tested were golf shoe tread type, walker type — player, caddie, maintenance — surface characteristics, and prevailing weather conditions.
The research team found that visual damage could be moderately predicted when both walker type and footwear tread were known. However, changes in green speed due to traffic were not predictable, even with all 115 variables included.
The most important factors influencing visual spike damage were the type of walker and the tread pattern of their shoes, the study found. “Unfortunately, those are the very factors superintendents have little or no control over,” said Carl Schimenti, co-author of the study.
The study mined traffic-impact databases dating back to 2019, encompassing 19 experiments that evaluated various golf shoes, walkers, putting green conditions, and weather patterns. The research team used machine learning models to find the most important variables predicting surface disruption.
Despite not having clear answers on how superintendents can manage putting surfaces to be more consistent as traffic builds, the Cornell Turfgrass team is in the second year of a project with the Tri-State Turfgrass Research Foundation. This new phase is investigating additional management factors that might predict surface disruption, including mowing height, rolling frequency, organic matter content, and plant growth regulator use.
“Now that we have effectively been assisting with optimizing shoe design it’s time to turn to surface maintenance that influences playing performance,” the team shared. “We can assist golf course superintendents with critical decision-making that optimizes maintenance.”
The paper was co-authored by Carl Schimenti, Maggie Reiter, and Frank Rossi. View data and the full manuscript here: https://doi.org/10.1002/its2.70026