Complex interactions shape Red Crown Rot development under controlled conditions and highlight sensitive parameters for disease quantification

Authors: PAREDES, JUAN - University Of Illinois Urbana-Champaign; RUIZ, ANDRES - University Of Illinois Urbana-Champaign; SHRESTHA, SHRISHA - University Of Illinois Urbana-Champaign; PUGLIESE, BRUNO - University Of Illinois Urbana-Champaign; ALESSIO, FLORENCIA - University Of Illinois Urbana-Champaign; CARIVALI, FLORENCIA - University Of Illinois Urbana-Champaign; PANDEY, KRISHNA - University Of Illinois Urbana-Champaign; Karki, Hari; Herman, Theresa; Clough, Steven; CAMILETTI, BORIS - University Of Illinois Urbana-Champaign

Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2026
Publication Date: 8/16/2026
Citation: Paredes, J.A., Ruiz, A.F., Shrestha, S., Pugliese, B.D., Carivali, A.F.B,Alessio, F., Pandey, K.R., Karki, H.S., Herman, T.K., Clough, S.J., Camiletti, B.X. 2026. Complex interactions shape Red Crown Rot development under controlled conditions and highlight sensitive parameters for disease quantification. Plant Disease. https://doi.org/10.1094/PDIS-04-26-0811-RE.
DOI: https://doi.org/10.1094/PDIS-04-26-0811-RE

Interpretive Summary: Red crown rot (RCR), a soybean disease caused by the fungus Calonectria ilicicola, is spreading quickly in Illinois and can cut yields by nearly half—even when plants look healthy above ground. This makes it hard for farmers to detect and manage the disease based on visible symptoms alone.To better understand the problem, researchers studied two local strains of the fungus under controlled conditions. They found that disease severity increases with higher levels of infection and that soil type affects how symptoms appear and how much yield is lost. Importantly, they showed that new image-based methods for measuring root damage are more accurate than traditional techniques, helping to detect disease effects that would otherwise go unnoticed.This research helps farmers and scientists recognize hidden yield losses and highlights the need for better tools to measure and manage RCR. It also confirms that both fungal strains respond well to the fungicide pydiflumetofen, offering a potential option for control.

Technical Abstract: Red crown rot (RCR), caused by Calonectria ilicicola, has rapidly intensified in Illinois, yet little is known about the aggressiveness of local isolates or the factors shaping disease expression under controlled conditions. This study compared two Illinois isolates (CC1 and MAD) using in vitro, growth chamber, and greenhouse assays to quantify aggressiveness in response to inoculum density and soil substrate, and evaluated the performance of image-based measurements for assessing disease severity. In vitro, CC1 exhibited greater mycelial growth, whereas both isolates showed high sensitivity to pydiflumetofen. Under growth chamber conditions, aggressiveness differed between isolates and increased with inoculum density, but the magnitude of these effects varied by parameter. Image-derived root area provided superior sensitivity for detecting treatment differences compared with traditional biomass or length measurements, revealing significant isolate separation at intermediate inoculum densities where other metrics failed. Necrosis percentage and disease rate were also consistently responsive indicators. Greenhouse experiments demonstrated strong substrate effects: torpedo sand mix enhanced foliar symptom expression but reduced overall plant productivity, whereas general-purpose mix supported higher yield yet displayed variable foliar symptoms. Across both substrates, inoculation reduced yield by up to 48%. Notably, substantial yield losses occurred even in plants exhibiting little or no visible foliar symptoms, suggesting that early root colonization and belowground damage can impair plant performance without clear aboveground expression. Together, these results highlight the complex interactions among isolate, inoculum density, and substrate, emphasize the value of sensitive root-based and image-derived metrics, and underscore the potential need to account for hidden yield penalties when evaluating management strategies for RCR.