Weather-based predictive modeling of Cercospora beticola infection events in sugar beet in Belgium

El Jarroudi, Moussa and Chairi, Fadia and Kouadio, Louis ORCID: https://orcid.org/0000-0001-9669-7807 and Antoons, Kathleen and Sallah, Abdoul-Hamid Mohamed and Fettweis, Xavier (2021) Weather-based predictive modeling of Cercospora beticola infection events in sugar beet in Belgium. Journal of Fungi, 7 (9):777.

[img]
Preview
Text (Published Version)
jof-07-00777.pdf
Available under License Creative Commons Attribution 4.0.

Download (3MB) | Preview

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.


Statistics for USQ ePrint 43707
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: © 2021 by the authors. 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 (http://creativecommons. org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 28 Sep 2021 04:46
Last Modified: 08 Nov 2021 00:42
Uncontrolled Keywords: Cercospora beticola; fungal foliar disease; plant disease risk; integrated plant disease management
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070308 Crop and Pasture Protection (Pests, Diseases and Weeds)
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3004 Crop and pasture production > 300409 Crop and pasture protection (incl. pests, diseases and weeds)
Identification Number or DOI: https://doi.org/10.3390/jof7090777
URI: http://eprints.usq.edu.au/id/eprint/43707

Actions (login required)

View Item Archive Repository Staff Only