A University of Arkansas drone prior to takeoff at a 2024 GCSAA Conference and Trade Show Interactive Facility Tour. Photos by Darrell J. Pehr
Identifying and controlling turfgrass pests with data science
Turfgrass managers put much of their effort and budget into integrated pest management programs. Thousands of dollars are allocated to the eradication of diseases and weeds in turfgrass systems. Samuel Kreinberg, a current master’s student at the
University of Arkansas under the direction of Wendell Hutchens, Ph.D., and Mike Richardson, Ph.D., is researching ways to reduce the amount of resources needed for control of pests. A bachelor’s degree in mathematics coupled with a passion for
data science complements Kreinberg’s research in turfgrass pest management. In this thesis research, the two primary pests of interest are large patch of zoysiagrass and winter weeds in dormant bermudagrass. A few of the analytical techniques
included are remote sensing, programming and machine learning.
More specifically, the large patch study aims to enhance the community’s knowledge of spatial and temporal dynamics of the disease. Large patch will be mapped with drone imagery to determine spread patterns and analyzed to identify common soil and
environmental characteristics that drive epidemics. With this information, Kreinberg wishes to notice consistent traits that contribute to the spreading of large patch. In terms of weed control, the goal of the project is to treat present weeds in
dormant bermudagrass turf more effectively. Site-specific spraying of nonselective herbicides (i.e. glyphosate) in real time will greatly reduce the amount of product needed to treat areas that have high weed pressure. A boom sprayer constructed with
unique software and hardware systems will apply herbicide to weeds as soon as the spray rig approaches the weed. Both of these projects seek to improve efficiency of pest control.
—Samuel Kreinberg (stkreinb@uark.edu), University of Arkansas, Fayetteville
Auburn University researcher Bridgette Johnson
Utilization of glufosinate for Poa annua control in bermudagrass
Annual bluegrass (Poa annua) is the most pervasive weed in turfgrass worldwide. Poa annua has developed resistance to acetolactate synthase inhibitors, glyphosate, mitotic inhibiting herbicides, triazine PSII inhibitors and likely indaziflam. The number
of herbicide options has diminished considerably. Glufosinate is a contact nonselective postemergence herbicide in the glycine family known to act quickly and show symptoms in less than three hours. Glufosinate has been reported to control Poa annua;
however, due to its contact nature, it is generally seen as less effective than glyphosate and sulfonylurea herbicides. Combinations of glufosinate with other herbicides could be more effective than glufosinate alone and could aid in preventing resistance
development. Field studies were initiated from January to March 2023 to investigate herbicide combinations with glufosinate to control Poa annua in bermudagrass (Cynodon dactylon) turf.
Treatments included glufosinate at 0.76 pounds active ingredient per acre (0.85 kilograms per hectare), glyphosate at 0.25 pounds active ingredient per acre (0.28 kilograms per hectare), oxadiazon at 2 pounds active ingredient per acre (2.27 kilograms
per hectare), indaziflam at 0.029 pounds active ingredient per acre (0.033 kilograms per hectare) and flumioxazin at 0.25 pounds active ingredient per acre. Each herbicide was applied alone and in a tank mixture with glufosinate. Application was made
in January 2023 with a CO2-pressurized backpack sprayer calibrated at 280 liters per hectare. Results showed the herbicides applied independently effectively controlled Poa annua, but in a mixture with glufosinate, increased control to above 90% at
25 days after application (DAA). Oxadiazon and indaziflam alone displayed <10% of Poa annua control at any rating date. In conclusion, adding glufosinate into herbicide tank mixtures successfully controls Poa annua in bermudagrass turfgrass.
— Bridgette Johnson (bcg0018@auburn.edu) and J. Scott McElroy, Auburn University, Auburn, Ala.
Darrell J. Pehr (dpehr@gcsaa.org) is GCM’s science editor.