Pest Management BMP March 2022

Pest Management BMP Reducing Risk with Data Driven Fairway Pest Management

March 2022

Thresholds of Resistance A threshold value must be established at every site (golf course), preferably using “indicator areas” where Dollar Spot becomes evident on untreated turf. Researchers suggest that susceptible turfgrass varieties have a threshold of 20%, while resistant varieties such as Declaration creeping bentgrass have a threshold of 40% (Zhang et al., 2021). However, it is best to have data that calibrate site-specific thresholds through observations. Once a threshold is established, monitoring of the model should occur routinely to assess risk of Dollar Spot infections. When the Smith-Kerns model exceeds the threshold, a pesticide can be applied. It can be assumed that the treated fairway turf will not experience disease until the fungicide interval expires, typically between 14 and 28 days. Once the interval lapses, the model is once again consulted to see if threshold levels are still exceeded, and the process repeats. A common critique of this system is the flexibility it requires in timing of pesticide applications. In most cases, facilities are constrained in the days they can apply chemical products (e.g., Monday), and therefore may not be able to apply treatment when the model based approach necessitates. In these cases, it may be appropriate to round the model-based date to the closest available day. Consideration can be made to the slope of the model line when rounding. For example, an interval may run out on a Thursday where the model projects pressure to be above threshold but following a downward trend. In this case, application could be delayed until the following Monday. Conversely, if the pressure was following an upward trend, a preventative application on the Monday prior may be warranted. Assessing Pest Management Costs Some managers question if this model-based approach will achieve the same results as a preventative program, i.e., high quality disease-free fairway turf. Fortunately, research at University of Wisconsin-Madison over the last three years shows that various model-based approaches provide equal results to a preventative program both in turf quality and disease suppression (Melton et al., 2019, Melton et al., 2020, Fenner et al., 2021)). It follows then that golf operations would be interested in cost savings associated with a risk model based approach to Dollar Spot control.

A recent analysis of pesticide use on golf courses in New York showed that pesticide applications made to fairways contributed the most pesticide risk of any playing surface (Bekken et al., 2021). Putting greens receive more pesticide use on a per area basis but, fairways cover more land and therefore require larger quantities of pesticide use, thereby increasing cost and risk. In New York, the most common and troublesome pest affecting finely mowed golf course areas is the fungus Clarireedia jacksonii , commonly known as Dollar Spot. Traditionally, routine, frequent fairway fungicide applications are made preventatively for Dollar Spot control on fairways throughout the season. Often this can result in as many as eight applications to an average 30 acres of turf or 240 total treated acres. Furthermore, routine frequent applications assume there is persistent disease pressure when studies have shown pressure fluctuates throughout the season. This results invariably in over-applying material. Transforming Treatment Strategy A preventative approach is an effective way to control Dollar Spot however, it is excessively risk averse. Preventative approaches do not consider the variability in Dollar Spot emergence as weather conditions change. Therefore, it is highly likely that pesticide applications are made during times when Dollar Spot is not active. The result is unwarranted pesticide use that increases cost and environmental risk. To reduce unnecessary pesticide use without compromising turf quality, Superintendents have a variety of options, two of which are: strategically withholding pesticide applications when pest pressure is predicted to be below threshold, and planting disease resistant turfgrass species that require fewer pesticide applications. In recent years, turfgrass researchers have sought to address the inherent issues with preventative management programs by creating disease forecasting models. These models use temperature and relative humidity data from weather stations to assess and predict disease pressure to guide pest management decision-making. The popular and verified Smith Kerns Dollar Spot Prediction Model, estimates the likelihood of Dollar Spot outbreaks and has been calibrated through field research across the United States. It is available for free online at: https://www. greencastonline.com/dollar-spot-solutions

Pest Management BMP Reducing Risk with Data Driven Fairway Pest Management

2

35

1st Fungicide Application Made

2nd Fungicide Application Made

30

25

20

28 Day Spray Interval: Ignore Model Output

15

20% spray threshold

10

Dollar Spot Model Probability

5

Smith-Kerns Model Output

0

5/1

5/8 5/15 5/22 5/29 6/5 6/12 6/19 6/26 7/13

Image 1. Depiction of Smith-Kerns model use (University of Wisconsin Madison Turfgrass Diagnostic Lab)

Model-based + Resistant Varieties Applications to resistant varieties were triggered on a 28-day interval when the Smith-Kerns threshold of 40% is exceeded. Figure 1 shows the forecasted risk in 2020 for Ithaca, NY. Using the data-driven model approach extends the application intervals when disease pressure is below the verified threshold, and three fewer applications are made when compared to the preventative program for the 2020 season. However, combining resistant varieties with a data driven model-based approach required only two applications, or a 50-75 percent reduction from susceptible varieties and preventative strategies. It’s possible only one application was needed in 2020 considering the second application was triggered when the Smith-Kerns model rose above 40% for only one day.

The risk models can be used to look at historical weather data as a means of planning for future pesticide needs. As an example, simulations were run for two locations in New York (Ithaca and Millbrook) over a 5-year period. This simulation assumes management of 20 acres of fairways and fungicide applications costing $125 per acre for each scenario. Three different scenarios were simulated below: Preventative Applications to susceptible varieties begin when Dollar Spot is first observed in late May. The Smith-Kerns model was used as a proxy for the first application when the model output exceeded 20%. Applications were then made every 21 days until October. Model-based Applications to susceptible varieties were triggered on a 28-day interval when the Smith-Kerns threshold of 20% is exceeded. If model outputs exceed 40%, intervals shift to 21 days to account for high disease pressure situations.

Pest Management BMP Reducing Risk with Data Driven Fairway Pest Management

3

Preventive Model-based Model-based + resistant varieties

70

60

50

40

40% Threshold

30

20% Threshold

20

Dollar Spot Model Probability

10

0

5/1/20 5/15/20 5/29/20 6/12/20 6/26/20 7/10/20 7/24/20 8/7/20 8/21/20 9/4/20 9/18/20 10/2/20

Figure 1. Dollar Spot control simulation for Ithaca, NY (2020)

Table 1. Simulated Pesticide Applications made to Fairways for Dollar Spot control

Control approach

2017 2018 2019

2020

2021

5-yr Total Total Fungicide Cost***

Ithaca, NY

Preventative

7

7

7

7

7

35

$ 87,500

Model-based*

6

6

5

4

6

27

$ 67,500

Model-based + resistant varieties**

4

5

3

2

3

17

$ 42,500

Millbrook, NY

Preventative

7

8

7

7

7

36

$ 90,000

Model-based*

6

7

6

5

6

30

$ 75,000

Model-based + resistant varieties**

4

4

3

2

2

15

$ 37,500

*Applications made on a 28-day interval when SK model is above 20%, and a 21-day interval when SK model is above 40% **Applications made on a 28-day interval when SK model is above 40% ***Assumes an average $150 per acre fungicide cost and 20 acres of fairway

Pest Management BMP Reducing Risk with Data Driven Fairway Pest Management

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University test plots being used for Dollar Spot research.

disease-resistant varieties, a value on par with the annual pesticide use of a full NY golf course! Data-driven BMPs such as disease risk forecast models can be calibrated to fit facility needs. An easily implemented practice (model-based approach) can achieve significant results for any facility, while a more intensive practice (new turfgrass varieties) can result in greater chemical reductions if a facility has the will and available resources. A misconception about BMPs is that there is only one BEST practice. Instead, there is a sliding scale of good/better/best that presents a sequential path to improvement towards a more sustainable operation. Thinking of BMPs in this respect can allow facilities to make realistic improvements instead of being overwhelmed by the expectation of adopting the best BMP. This strategy is more likely to lead to industry wide improvements in BMP adoption that create a more resilient, high performing playing surface and a better environment.

Table 1 shows the difference in number of pesticide applications annually when simulating the three control approaches. Employing a model-based approach would lead to a 20% reduction in pesticide applications for NY golf courses and a cost savings of $3,500 per year when compared to a preventative approach. The numbers are even more significant when switching to disease resistant varieties, where a NY golf course would achieve over a 50% reduction in pesticide use and a cost savings of $9,750 per year. Data Driven Environmental Stewardship Environmental risk reductions are computed using the Environmental Impact Quotient (EIQ). Using an average Field Use EIQ value of 25, over a 5-year period would equate to 3,500 EIQ units for a model-based approach, the equivalent of a growing seasons worth of pesticide risk associated with all putting green pest management on an average NY golf course. Environmental risk savings balloon to 9,500 EIQ units when using

Pest Management BMP Reducing Risk with Data Driven Fairway Pest Management

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References Bekken, M. A., Schimenti, C. S., Soldat, D. J., & Rossi, F. S. (2021). A novel framework for estimating and analyzing pesticide risk on golf courses. The Science of the Total Environment , 783, 146840. https://doi.org/10.1016/j. scitotenv.2021.146840 Fenner, J., Nagel, J., Hockemeyer, K., Koch, P. (2021). Smith-Kerns Dollar Spot Model – Upper Limit. University of Wisconsin-Madison. https://tdl.wisc.edu/wp-content/ blogs.dir/42/files/Interactive%20Pages/2021_Summer/ Reports/UWUpper_Limit_2021.pdf Melton, R., Hockemeyer, K., Cruz, C., Koch, P. (2019). Dollar Spot Model Upper Limit . University of Wisconsin Madison. https://tdl.wisc.edu/wp-content/blogs.dir/42/ files/Interactive%20Pages/2019_Summer/Reports/ UWUpperLimit_2019.pdf Melton, R., Hockemeyer, K., Koch, P. (2020). Smith-Kerns Dollar Spot Model – Upper Limit . University of Wisconsin Madison. https://tdl.wisc.edu/wp-content/blogs.dir/42/ files/Interactive%20Pages/2020_Summer/Reports/ UWUpper_Limit_2020.pdf Smith, D.L., Kerns, J.P., Walker, N.R., Payne, A.F., Horvath, B., Inguiagiato, J.C., Kaminski, J.E., Tomaso Peterson, M., Koch, P.L. (2018). Development and validation of a weather-based warning system to advise fungicide applications to control Dollar Spot on turfgrass. PLOS ONE, 13 (3), 194216. https://doi. org/10.1371/journal.pone.0194216 University of Wisconsin Madison Turfgrass Diagnostic Lab. (n.d.). Dollar Spot Model . Retrieved December 1, 2021. https://tdl.wisc.edu/dollar-spot-model/ Zhang, P., Ward, D., Murphy, J., Clarke, B. (2021, June). Action threshold of the Smith-Kerns Dollar Spot model on two creeping bentgrass cultivars. Golf Course Management, 89 (6), 79.

A Dollar Spot lesion on a Rygrass leaf blade.

Pest Management BMP Reducing Risk with Data Driven Fairway Pest Management

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Early stages of Dollar Spot on a putting green.

Pest Management BMP Reducing Risk with Data Driven Fairway Pest Management

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