Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium

Moussa El Jarroudi,Abdoul-Hamid Mohamed Sallah,Xavier Fettweis,Fadia Chairi,Louis Kouadio,Kathleen Antoons

doi:10.3390/jof7090777

Moussa El Jarroudi, Abdoul-Hamid Mohamed Sallah + Show 4 more

Open Access

https://doi.org/10.3390/jof7090777

Copy DOI

Abstract

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beet-growing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84%, ≥78%, and ≤15%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH > 90% during the first four hours and RH > 60% during the following 9 h, daytime T > 16 °C and nighttime T > 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season.

Highlights

The model performance of M1, M2, and M3 that we demonstrated in this study can be readily embedded within a decision support tool to optimize fungicide sprays in sugar beet farms
Considering the damage that Cercospora leaf spot (CLS) can cause in sugar beet fields when weather conditions are favorable, it is critical to ensure that C. beticola infection events can be reliably predicted using weather-based disease risk models
We evaluated the performance of weather-based models in predicting C. beticola infection events at various sites in Belgium during three consecutive sugar beet cropping seasons

Summary

Introduction

When weather conditions are favorable, C. beticola can complete several asexual cycles within a single cropping season under favorable weather conditions and can survive between growing seasons on infected plant residues, primarily as overwintering conidia-producing hyphal structures (pseudostromata) [12,13,14]. Other potential sources of primary inocula include windborne conidia, infested seed or beet roots, dispersal of C. beticola through tools and machinery, and stromata from other host plants [4,5,13,15,16]. Preventive and prudent cultural practices, including rotation with non-host crops, growing disease-resistant cultivars, and application of fungicides with various modes of action, are widely used to reduce inoculum levels in infested residue levels and manage CLS disease

Objectives

Methods

Results

Discussion

Conclusion