Project Overview

Reports

Commodities

• Agronomic: sugarbeets

• Vegetables: beets

Target Pests

Practices

• Pest Management: chemical control, decision support system, forecasting, genetic resistance, modeling, monitoring/scouting, pesticide application timing, pesticide resistance management, pesticides, weather monitoring

Cercospora leaf spot (CLS), caused by Cercospora beticola, remains the most significant foliar disease of sugarbeets in the North Central U.S. and Great Lakes region, resulting in substantial yield losses and driving intensive fungicide use. Current management programs rely on infection-based forecasting models to guide fungicide applications, but recently failed to predict early-season risk to best inform industry practices. Resistance to key fungicide classes – particularly demethylase inhibitors (DMIs) – has also become an escalating concern across the region. Recent epidemiological studies in Michigan and Ontario have identified early-season risk factors, including elevated airborne conidia levels, that show promise for improving disease prediction and optimizing spray timing. Additional studies of C. beticola in this region have identified a need for timely, accurate tools to detect fungicide resistance.

This project seeks to enhance integrated pest management (IPM) strategies by 1) validating spore-based forecasting models under field conditions, 2) refining model-informed fungicide application recommendations, and 3) improving diagnostic tools for fungicide resistance detection. A key focus will be identifying genetic markers more predictive of DMI resistance in regional C. beticola populations, which will support more responsive and effective monitoring efforts.

Our work is supported by strong partnerships with Michigan Sugar Company, industry stakeholders, and regional extension programs. Outcomes will include improved tools for early disease risk assessment, more sustainable fungicide use practices, and practical guidance for managing resistance development. These efforts will contribute to a more resilient and economically viable sugarbeet production system in the North Central region through integrated, science-based approaches to CLS management.

Objective 1: Validate spore-based modeling tools to predict elevated airborne Cercospora spp. levels and improve timing of CLS management.

Objective 2: Assess model-based timing of contact and systemic fungicides to enhance early-season CLS control.

Objective 3: Evaluate PCR-based tools to improve monitoring of C. beticola fungicide resistance, focusing primarily on common DMI active ingredients.