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

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 f...

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Autores principales: El Jarroudi, Moussa, Chairi, Fadia, Kouadio, Louis, Antoons, Kathleen, Sallah, Abdoul-Hamid Mohamed, Fettweis, Xavier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470031/
https://www.ncbi.nlm.nih.gov/pubmed/34575815
http://dx.doi.org/10.3390/jof7090777
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author El Jarroudi, Moussa
Chairi, Fadia
Kouadio, Louis
Antoons, Kathleen
Sallah, Abdoul-Hamid Mohamed
Fettweis, Xavier
author_facet El Jarroudi, Moussa
Chairi, Fadia
Kouadio, Louis
Antoons, Kathleen
Sallah, Abdoul-Hamid Mohamed
Fettweis, Xavier
author_sort El Jarroudi, Moussa
collection PubMed
description 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.
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spelling pubmed-84700312021-09-27 Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium El Jarroudi, Moussa Chairi, Fadia Kouadio, Louis Antoons, Kathleen Sallah, Abdoul-Hamid Mohamed Fettweis, Xavier J Fungi (Basel) Article 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. MDPI 2021-09-18 /pmc/articles/PMC8470031/ /pubmed/34575815 http://dx.doi.org/10.3390/jof7090777 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
El Jarroudi, Moussa
Chairi, Fadia
Kouadio, Louis
Antoons, Kathleen
Sallah, Abdoul-Hamid Mohamed
Fettweis, Xavier
Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium
title Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium
title_full Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium
title_fullStr Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium
title_full_unstemmed Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium
title_short Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium
title_sort weather-based predictive modeling of cercospora beticola infection events in sugar beet in belgium
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470031/
https://www.ncbi.nlm.nih.gov/pubmed/34575815
http://dx.doi.org/10.3390/jof7090777
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