Weather-based predictive modeling of wheat stripe rust infection in Morocco
Article
Article Title | Weather-based predictive modeling of wheat stripe rust infection in Morocco |
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ERA Journal ID | 200126 |
Article Category | Article |
Authors | El Jarroudi, Moussa (Author), Lahlali, Rachid (Author), Kouadio, Louis (Author), Denis, Antoine (Author), Belleflamme, Alexandre (Author), El Jarroudi, Mustapha (Author), Boulif, Mohammed (Author), Mahyou, Hamid (Author) and Tychon, Bernard (Author) |
Journal Title | Agronomy |
Journal Citation | 10 (2) |
Article Number | 10020280 |
Number of Pages | 18 |
Year | 2020 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2073-4395 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/agronomy10020280 |
Web Address (URL) | https://www.mdpi.com/2073-4395/10/2/280 |
Abstract | Predicting infections by Puccinia striiformis f. sp. tritici, with sufficient lead times, helps determine whether fungicide sprays should be applied in order to prevent the risk of wheat stripe rust (WSR) epidemics that might otherwise lead to yield loss. Despite the increasing threat of WSR to wheat production in Morocco, a model for predicting WSR infection events has yet to be developed. In this study, data collected during two consecutive cropping seasons in 2018–2019 in bread and durum wheat fields at nine representative sites (98 and 99 fields in 2018 and 2019, respectively) were used to develop a weather-based model for predicting infections by P. striiformis. Varying levels of WSR incidence and severity were observed according to the site, year, and wheat species. A combined effect of relative humidity > 90%, rainfall ≤ 0.1 mm, and temperature ranging from 8 to 16 ◦C for a minimum of 4 continuous hours (with the week having these conditions for 5% to 10% of the time) during March–May were optimum to the development of WSR epidemics. Using the weather-based model, WSR infections were satisfactorily predicted, with probabilities of detection ≥ 0.92, critical success index ranging from 0.68 to 0.87, and false alarm ratio ranging from 0.10 to 0.32. Our findings could serve as a basis for developing a decision support tool for guiding on-farm WSR disease management, which could help ensure a sustainable and environmentally friendly wheat production in Morocco. |
Keywords | yellow rust; disease risk; wheat; sustainable agriculture |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 300208. Farm management, rural management and agribusiness |
300409. Crop and pasture protection (incl. pests, diseases and weeds) | |
Byline Affiliations | University of Liege, Belgium |
National School of Agriculture, Morocco | |
Centre for Applied Climate Sciences | |
University of Liege, Belgium | |
Research Centre Julich, Germany | |
Abdelmalek Essaâdi University, Morocco | |
National Institute for Agricultural Research, Morocco | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5q78/weather-based-predictive-modeling-of-wheat-stripe-rust-infection-in-morocco
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