Reference : Weather-Based Predictive Modeling of Wheat Stripe Rust Infection in Morocco
Scientific journals : Article
Life sciences : Agriculture & agronomy
http://hdl.handle.net/2268/244873
Weather-Based Predictive Modeling of Wheat Stripe Rust Infection in Morocco
English
El Jarroudi, Moussa mailto [Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement >]
Lahlali, Rachid [> >]
Kouadio, Louis [> >]
Denis, Antoine mailto [Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) >]
Belleflamme, Alexandre [> >]
El Jarroudi, Mustapha [> >]
Boulif, Mohammed [> >]
Mahyou, Hamid [> >]
Tychon, Bernard mailto [Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement >]
15-Feb-2020
Agronomy
MDPI AG
10
2
1-18
Yes (verified by ORBi)
International
2073-4395
Switzerland
[en] yellow rust ; disease risk ; wheat ; sustainable agriculture
[en] Predicting infections by Puccinia striiformis f. sp. tritici, with su cient 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 e ect 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.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/244873
10.3390/agronomy10020280
https://www.mdpi.com/2073-4395/10/2/280

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