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ARS Home » Southeast Area » Byron, Georgia » Fruit and Tree Nut Research » Research » Publications at this Location » Publication #332965
Title: A threshold-based weather model for predicting stripe rust infection in winter wheat

Author
item EL JARROUDI, MOUSSA - Universite De Liege
item KOUADIO, LOUIS - University Of Southern Queensland
item EL JARROUDI, MUSTAPHA - Université Abdelmalek Essaâdi
item JUNK, JURGEN - Luxembourg Institute Of Science & Technology
item Bock, Clive
item TYCHON, BERNARD - Universite De Liege
item DELFOSSE, PHILIPPE - University Of Luxembourg

Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/22/2016
Publication Date: 5/1/2017
Citation: El Jarroudi, M., Kouadio, L., El Jarroudi, M., Junk, J., Bock, C.H., Tychon, B., Delfosse, P. 2017. A threshold-based weather model for predicting stripe rust infection in winter wheat. Plant Disease. 101:693-703.

Interpretive Summary: Wheat stripe rust (WSR) is a major disease of wheat worldwide, and can inflict regular yield losses when environmental conditions are favorable. A disease severity threshold-based disease-forecasting model using a stepwise modeling approach was developed that is based on weather data to provide a disease-warning system. The optimum combined favorable weather variables [air temperature (T), relative humidity (RH), and rainfall (R)] during the most critical infection periods (May-June) was identified and was used to develop the model. Results showed that a combination of RH > 92% and 4°C < T < 16°C for a minimum of 4 consecutive hours, associated with R = 0.1 mm (with the dekad having these conditions for 5-20% of the time), were optimum to WSR epidemic development. The model accurately forecast infection events by P. striiformis with a probability of detection = 0.90. Furthermore, a recent shift (since 2011) in incidence of WSR in Luxembourg was accurately detected by the model. If forecast weather is used, our modeling approach may be used in an operational disease-warning system to guide fungicide applications.

Technical Abstract: Wheat stripe rust (WSR) (caused by Puccinia striiformis sp. tritici) is a major threat in most wheat growing regions worldwide, with potential to inflict regular yield losses when environmental conditions are favorable. We propose a threshold-based disease-forecasting model using a stepwise modeling approach, which is based on weather data to provide an operational disease-warning system. First, we characterized the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model. The optimum combined favorable weather variables [air temperature (T), relative humidity (RH), and rainfall (R)] during the most critical infection periods (May-June) was identified and was used to develop the model. A notion of consecutive hours with favorable weather conditions over each dekad (i.e., 10-day period) during May-June was considered when building the model. Data from 1999-2015 for three representative wheat-growing sites in the Grand-Duchy of Luxembourg were used. Results showed that a combination of RH > 92% and 4°C < T < 16°C for a minimum of 4 consecutive hours, associated with R = 0.1 mm (with the dekad having these conditions for 5-20% of the time), were optimum to WSR epidemic development. The model accurately forecast infection events by P. striiformis with a probability of detection = 0.90, a false alarm ratio = 0.38, and a critical success index range from 0.63 to 1. Furthermore, a recent shift (since 2011) in incidence of WSR in Luxembourg was accurately detected by the model. If forecast weather is used, our modeling approach may be used in an operational disease-warning system to guide fungicide applications.