Progress report for LNE25-501R
Project Information
This project aims to develop a decision support tool to guide precision fungicide application against brown patch, gray leaf spot, dollar spot and anthracnose for sod producers in New Jersey and the Northeast. New Jersey sod farms covered 3,359 hectares with an estimated economic impact of $94.7 million in the 2019 census. A survey conducted through the Rutgers University Plant Diagnostic Laboratory revealed that, over the past 18 years, 75% of the diseased samples were foliar, and the four diseases the project plans to address accounted for over 70% of them. Through in-person, email and phone interviews, sod growers in New Jersey expressed that the most common way to control disease is through calendar-based fungicide applications, mainly due to lack of decision support tool that can be easily integrated into the current production system. A calendar-based program is imprecise: it often results in over-applications and, at other times, is susceptible to disease breakthrough due to missed critical timing for disease development. This proposal aims to develop a disease control decision support tool for sod growers to promote informed decision-making and to facilitate environmentally and financially sustainable sod production.
A multiplexed digital PCR (dPCR) pathogen quantification assay was recently developed by our team, and it is capable of quantifying causal agents for the abovementioned turfgrass diseases using turfgrass mowing clippings. Since mowing is part of the day-to-day practice in sod production, using mowing clippings to measure the disease pressure allows seamless integration of this decision support tool into the current operation. This assay was developed using clippings collected from the research farm at Rutgers University. Ground-truthing the assay to relate the dPCR data and observed disease pressure is required to ensure practical utility. Therefore, this proposed project will collaborate with New Jersey sod growers who will work with us to validate the assay and establish practical disease risk thresholds to complete development of this decision support tool.
The collaborative farmers will establish research plots on their farms, monitor the disease development on the control plots, collect mowing clippings and submit the sample weekly for pathogen load monitoring. Once the preliminary disease risk thresholds are established, the collaborators will plan their disease management according to the measured disease risks. We also plan to provide a trial of the prototype decision support tool in the third year of the study so that more sod farmers can be exposed to this tool and potentially take advantage of the service once fully developed. We will also be able to collect grower’s feedback to optimize this tool. The developed service is anticipated to be publicly available through the Rutgers University Plant Diagnostic Laboratory to benefit sod growers in the Northeast and beyond.
A recently developed pathogen quantification assay will be field validated in collaboration with sod producers to evaluate its practical utility, aiming to develop a decision-support tool which informs disease risks for sod growers. This collaborative study will generate pathogen load data, which will be coupled with weather data, field observations and end-user feedback to make the data relevant to real-world scenario and help us optimize assay efficiency and practicality. This proposed decision-support tool will help sod growers in the Northeast better evaluate the disease risks so they can apply fungicides precisely for a more environmentally and financially sustainable sod production.
In 2019, New Jersey sod farms covered 3,359 hectares, generated 1,051 jobs and yielded an estimated economic impact of $94.7 million. A survey of samples submitted to the Rutgers Plant Diagnostic Laboratory since 2006 revealed that 68% of the samples had diseases, with 75% of these diseases being foliar, suggesting the need for disease control program improvement. From the survey data, two major foliar diseases identified are brown patch and gray leaf spot, with minor ones including dollar spot and anthracnose. Through in-person communications, site visits and attendance at sod growers’ meetings, sod growers expressed that they typically schedule fungicide applications on a calendar basis and often incorporate non-specific, contact fungicides due to lack of information on the pathogen population. This imprecise strategy often results in excessive fungicide application and, at other times, is susceptible to disease breakthrough for missing the critical disease development timing. Other issues related to non-specific fungicide application include pollution, elevated costs, and pathogen fungicide resistance. In fact, sod farmers are aware of these issues but lack of tool to guide their fungicide program leading them sticking to conventional practices. All of this indicates that a decision support tool providing foliar disease risk information is needed to guide sod growers in precision fungicide application, including selecting disease-specific products and proper application timing to promote economic and environmental sustainability in sod production in the Northeast.
Digital PCR (dPCR) is becoming the standard molecular disease diagnostic and epidemic prevention tool in the medical field due to its sensitivity, accuracy and reliability. My team has been developing turfgrass pathogen quantification tools using dPCR in the past few years. The dPCR assay we developed has shown great sensitivity and accuracy for quantifying the economically important turfgrass foliar pathogens. This multiplexed assay could be further developed into a disease risk monitoring tool after thorough ground-truthing. Multiple sod growers in New Jersey have expressed interest in participating in the proposed project and believe this tool will help them improve their disease management programs once developed. Two New Jersey sod growers are committed to participate in the on-farm research to assist in the assay validation. Sod producers will be able to use the developed disease risk evaluation tool to make informed decision on fungicide application, allowing specific fungicides to be selected against the diseases and application only when absolutely needed. The potential benefit of using this decision support tool is expected to promote economic and environmental sustainability in sod production in the Northeast and beyond. Considering the acreages of sod farms in the Northeast, reducing one fungicide application could save an average farmer up to $20,000 in material cost. In addition, reduced fungicide input for sod farming facilitates long-term soil and environmental health. All of this aligns well with the Northeast SARE outcome statement.
Cooperators
- (Researcher)
- (Educator)
Research
Objective 1. Determining brown patch, gray leaf spot, dollar spot, and anthracnose disease risk thresholds using multiplexed dPCR
Experimental design
In 2025, the experiment was established in one commercial sod farm from New Jersey and at Rutgers HortFarm II. On the commercial sod farm, turfgrass fields with predominantly tall fescue (Festuca arundinaceae) approximately 10,000 sq. ft. was managed according to the commercial standard for mowing, nutritional, water, weed and insect pest management and following a two-treatment, paired-comparison experimental design with four replications for each treatment and each experimental unit covering 1,000 sqft. At Rutgers HortFarm II, each experimental unit was significantly sized down to 15 sq. ft. with a total experimental plot being 120 sq. ft.
Treatments
Both farms received two treatments. Treatment One received fungicide application following the industry standard calendar-based application, and Treatment Two received fungicide application in a curative, as-needed basis when moderate disease symptom showed up. Fungicides were applied using a GPS sprayer at 2 gallon/1000 sq. ft. tank rate following a chlorothalonil, propiconazole and azoxystrobin rotation program at the label rates. Treatment One provided information on any mistiming of fungicide applications that result in disease breakthrough and potential savings from unnecessary applications. Treatment Two provided critical information to couple the observed disease symptom and dPCR quantified pathogen load to establish disease risk thresholds.
Sample collection
On the commercial site, turf was mowed at 2.5 inches using a Trimax mower approximately twice a week. On Rutgers HortFarm II, experimental plots were mowed at 2.5 inches using a Triplex mower twice a week. The turf clippings were collected to evaluate pathogen load starting on April 29 at the commercial farm and May 19 at HortFarm II, before the first calendar-based fungicide application, and continued through September 29, two weeks after the final calendar-based application at both sites. At every mowing event at the sod farms, turfgrass clippings of each experimental unit were collected immediately after mowing using a pitchfork, homogenized, and randomly sampled to fill up a one-liter ziplock bag. For sample collection at Rutgers HortFarm II, turf clipping samples were collected using a walkbehind rotary mower right before the field was mowed by the triplex mower. The clipping samples were stored in a -4°C freezer before shipped overnight to Rutgers University for the following processing. At each clipping collection, disease incidence was monitored and recorded for each plot.
Sample processing and DNA extraction
Upon receipt, turf clippings from each plot were immediately ground in liquid nitrogen to a fine powder. Approximately 100 mg of ground tissue per plot was transferred into a bead-beating tube for DNA extraction using the Qiagen DNeasy PowerLyzer PowerSoil kit. To each tube, 750 µL of PowerBead solution and 60 µL of solution C1 were added, along with ~500–600 µL of molecular-grade water as needed to facilitate transfer. Samples were bead-beaten at 10,000 rpm for 1 min to ensure thorough homogenization, then centrifuged at 10,000 × g for 1 min. The remaining steps followed the manufacturer’s protocol, and DNA was eluted in 60 µL of C6 elution buffer.
Digital PCR and data generation
The extracted DNA was quantified using a Qubit fluorometer with the broad-range reagent kit and then used to perform the dPCR pathogen quantification. The dPCR reaction followed the instructions for the Absolute Q™ dPCR System (Thermo Fisher, Waltham, MA) using primers and probes targeting Pyricularia oryzae, Rhizoctonia solani, Clarireedia jacksonii, and Colletotrichum cereale. Thermal cycling conditions were 96°C for 10 min, followed by 40 cycles of 96°C for 5 s and 60°C for 15 s.
Data analysis
In the preliminary analysis, time series data for pathogen load and disease severity, collected through September 4, 2025, at Hort Farm II and August 28, 2025, at the commercial farm, were visualized using the ‘ggplot2’ package in R (version 4.4.1; R Core Team, 2024). Seasonal epidemic progress for brown patch and gray leaf spot was quantified by calculating the area under the disease progress stairs (AUDPS) from disease severity ratings using the package ‘agrocolae’. Differences in AUDPS between the two treatments were evaluated using Welch’s two-sample t-test.
Future analysis
The time-series pathogen load data derived from digital PCR will be plotted against the incidence and severity data of two major diseases, brown patch and gray leaf spot, and two minor disease, dollar spot and anthracnose. Segmented regression analysis and pruned exact linear time algorithm will be used to identify the breakpoints, which will be considered as the disease risk thresholds.
Future analysis
The time-series pathogen load data derived from digital PCR will be plotted against the incidence and severity data of two major diseases, brown patch and gray leaf spot, and two minor disease, dollar spot and anthracnose. Segmented regression analysis and pruned exact linear time algorithm will be used to identify the breakpoints, which will be considered as the disease risk thresholds.
Objective 2. Evaluating the brown patch, gray leaf spot, dollar spot, and anthracnose disease control efficacy and fungicide savings in a risk threshold-based fungicide program
Material and methods
Experimental procedure
The experiment for Obj2 will start in year two (2026) and repeat in year three (2027). The research sites, experimental design, turfgrass clipping sampling scheme, sample processing and dPCR assay will be the same as in Obj1 except a third treatment will be added with the same plot dimensions. Plots for treatment three in year two will only receive fungicides when dPCR indicated pathogen loads surpass the disease risk thresholds determined in Obj1 in year one for brown patch, gray leaf spot, dollar spot and anthracnose. As before, disease incident and severity will be evaluated by the growers at each clipping collection. By the end of year two, the disease risk thresholds will be fine-tuned by using the data collected in year one and two and the updated thresholds will be used in year three for treatment three. Treatment three will be used to evaluate the effectiveness of disease control and the potential savings on fungicide application. Area under the disease progress curve for each disease will be calculated and compared using one-way ANOVA to evaluate the disease control efficacy among the three treatments. Number of fungicide application and the associated costs will also be calculated for each treatment.
The dPCR assay successfully detected and quantified the major foliar pathogens associated with brown patch (Rhizoctonia solani), gray leaf spot (Pyricularia oryzae), anthracnose (Colletotrichum cereale), and dollar spot (Clarireedia jacksonii) (Fig 1. and Fig 3.). At HortFarm II, R. solani DNA was detected early in the sampling period (late May to mid June) and remained consistently high throughout the season across both treatments (Fig 1.C). In contrast, P. oryzae DNA levels were comparatively low early in the season but increased later, indicating delayed pathogen accumulation relative to R. solani. Pyricularia oryzae DNA was detected earlier and at substantially higher levels in plots receiving the curative (need based) treatment, with a pronounced peak in late August, whereas plots receiving the standard calendar based treatment exhibited smaller peaks in mid to late August (Fig 1.C).
These pathogen dynamics closely aligned with the timing of disease development observed in the field. For example, P. oryzae DNA was detected at low levels early in the season, with pronounced spikes in mid August, while visible gray leaf spot symptoms were not observed until late August and reached maximum severity in early September (Fig 1.B and Fig 2.A). Similarly, R. solani DNA was detected by dPCR as early as late May to early June, whereas brown patch symptoms were not observed until mid June (Fig 1C and Fig 2B). This early detection window demonstrates that dPCR can detect pathogen activity well before symptom development, providing an early warning for disease monitoring and enabling improved timing of management interventions.
The mean area under the disease progress stairs (AUDPS) was significantly higher (P < 0.01) in plots receiving the curative treatment (brown patch = 177.5; gray leaf spot = 20.25) than in plots receiving the standard calendar based treatment (brown patch = 94.5; gray leaf spot = 7.5), indicating that the calendar based fungicide program provided better overall disease suppression.
For the minor diseases, C. jacksonii DNA remained consistently low across sampling dates and treatments, indicating limited dollar spot pathogen pressure during the study period (Fig 1.D). In contrast, C. cereale DNA was detected throughout the season. Colletotrichum cereale DNA levels were slightly higher in the curative treatment early in the season, and both treatments exhibited a sharp peak in mid July, followed by a steady decline through August (Fig 1.A).
At the commercial farm site, R. solani was detected by dPCR as early as April 29 (Fig 3.C), whereas the first brown patch symptoms were not observed until May 19 in either treatment (Fig 4.). Brown patch severity later in the season was substantially higher in the curative treatment (Fig 4.). Consistent with these observations, mean AUDPS values for brown patch were significantly higher (P < 0.01) in the curative treatment (107.4) than in the standard treatment (26). Pyricularia oryzae was also detected earlier and at substantially higher levels in plots receiving curative treatment, with a pronounced peak in early August, whereas the plots receiving standard calendar based treatment showed a smaller peak in mid August (Fig 3.B). Clarireedia jacksonii DNA remained consistently low in both treatments throughout the season (Fig. 3D). Colletotrichum cereale DNA was detected at low levels early in the season, followed by mid summer peaks, with the plots receiving curative treatment generally showing earlier and higher peaks than the plots receiving standard treatment (Fig 3.A). In both treatments, pathogen levels declined toward late July, with minor increases in August.
Based on the preliminary analysis, the dPCR assay detected pathogen DNA weeks before visible symptoms, indicating that molecular diagnostics can provide an early warning of turfgrass disease development. Dollar spot pressure was minimal throughout the season, while the anthracnose pathogen (C. cereale) showed mid summer peaks but remained secondary to the major diseases. Among the foliar diseases, brown patch was more severe and present at higher levels throughout the sampling window, whereas gray leaf spot was observed later in the season. For brown patch and gray leaf spot, preventive calendar based fungicide programs maintained substantially lower pathogen loads and epidemic progress, as measured by AUDPS, than curative programs at both sites. In plots receiving curative treatments, Rhizoctonia solani and Pyricularia oryzae accumulated earlier and reached higher levels, leading to more severe brown patch and gray leaf spot epidemics than in plots receiving standard calendar based applications. However, it is important to note that curative applications in 2025 were not guided by dPCR data. We are currently analyzing the 2025 dataset to establish disease risk thresholds for the two major diseases, which will be used in 2026, so that curative applications are applied only when pathogen DNA levels exceed those thresholds, enabling more economical and sustainable disease management. Overall, these results demonstrate that dPCR based pathogen monitoring can provide more effective and timely disease management decisions in turfgrass systems.
Education & outreach activities and participation summary
Educational activities:
Participation summary:
Four farm visits and two phone consultations were made with the collaborating sod farms in NJ.
A presentation was delivered to describe the project at Annual Cultivated Sod Association of New Jersey Seminar/Bordentown, NJ, Feb. 2025 (20 sod farmers attended).
Learning Outcomes
The sod farmers and the collaborating sod farms are now aware of the dPCR tool being developed. The sod farmers who attended the winter seminar in Feb. 2025 gained knowledge of the importance for fungicide selection against different diseases to optimize the disease control efficacy and proper mode of action rotation to avoid fungicide resistance development. Since we are still in the process of validating the usefulness of the tool, no plan for promoting the adaptation of this tool has been made. According to our original plan, we aim to perform a second-year validation of the tool as well as determine valid action thresholds for different disease before we promote this tool.
Project Outcomes
This study is in progress. We are currently generating validation results before we can determine effective action thresholds for the sod farmers to precisely select and apply fungicides for optimal disease control efficacy. More outcomes will be updated in the second and third year.
The dPCR tool for early disease warning is still being developed and will require further validation in the second and third year. What we've learned from the first year indicated that the disease evaluation by the sod farmer can be subjective. Field pictures will be requested in the coming years to be used as a more objective method for disease evaluation.