Optimization of Fungicide Application Conditions for Managing Disease Severity Caused by Rhizoctonia solani in Lettuce

Subject Areas : Plant Pathology

Mahdi Eshaghi ¹ , ُSaminn Seddigh ^{2 *} , Mojdeh Maleki ³

1 - PhD. Student, Department of Plant Pathology, VaP.C., Islamic Azad University, Varamin, Iran
2 - Associated Professor, Department of Plant Pathology, VaP.C., Islamic Azad University, Varamin, Iran
3 - Associated Professor, Department of Plant Pathology, VaP.C., Islamic Azad University, Varamin, Iran

Received: 2025-06-14 Accepted : 2025-09-02 Published : 2025-09-19

Keywords: Rhizoctonia solani, lettuce, biological fungicide, carbendazim, soil-borne disease control,

Abstract :

Rhizoctonia solani Kühn is recognized as one of the most destructive soil-borne pathogens, responsible for causing collar and root rot in numerous economically important crops, including lettuce (Lactuca sativa L.). Due to its broad host range and remarkable persistence in soil ecosystems, effective management of this pathogen remains a significant challenge for sustainable agriculture. In the present study, the Response Surface Methodology (RSM) integrated with a Central Composite Design (CCD) was employed to model and optimize the pathogenicity intensity of R. solani on lettuce. The primary objective was to identify the optimal conditions for applying three fungicidal treatments- a chemical fungicide (carbendazim) and two biological formulations (Pars Basil containing Bacillus velezensis and Trichomix HV containing Trichoderma harzianum- against two distinct isolates (B and C) of R. solani under open-field conditions in Varamin, Iran. Significant differences in pathogenicity intensity were observed between the isolates. Isolate B exhibited higher resistance to the applied treatments, whereas isolate C showed a markedly greater sensitivity to carbendazim. The use of biological fungicides provided substantial protective effects, particularly during the early growth stages of the crop. Furthermore, the statistical modeling demonstrated a strong goodness-of-fit, indicating the model’s high predictive accuracy in estimating disease severity. Optimization results revealed that the maximum efficacy of carbendazim was achieved at a concentration of 3‰ (3 gL⁻¹) with three applications per month. In contrast, the biological fungicides showed optimal preventive performance at a concentration of 2‰ (2 gL⁻¹) with two applications. Collectively, these findings highlight that the selection of fungicide type, application timing, and treatment frequency plays a pivotal role in the sustainable management of lettuce collar rot disease caused by R. solani. This study underscores the potential of integrating chemical and biological control strategies within predictive optimization frameworks to enhance disease suppression while minimizing environmental impact.

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