Monitoring inoculum density of the stripe rust pathogen of wheat and evaluating weather variables in relation to disease severity in eastern Washington

Kristen L Bullough

Puccinia striiformis f. sp. tritici (Pst), is a foliar fungal pathogen that causes stripe rust, resulting in yield loss and quality reduction of wheat across the U.S., including the Pacific Northwest (PNW) region. Stripe rust monitoring has relied heavily on visual disease observations, but recently efforts to sample airborne inoculum followed by identification and quantification through polymerase chain reaction (PCR) have been used to detect airborne fungal pathogens early in the season. These spore sampling techniques are being used due to a need for increased monitoring as disease dynamics shift with changing interactions among hosts, pathogens, and their environment. Predictive models, which have been used and improved upon for stripe rust in the PNW since the early 1980s look at how the environment affects stripe rust severity or yield loss based on the historical weather and disease data. As the climate continues to change, the models may need to be periodically improved. Modeling based on inoculum density, which is independent of historical data, could offer a new approach for direct prediction and monitoring the disease. In this study, a spore trapping network was set up at multiple locations in eastern Washington using two types of samplers: Burkard air samplers and rotorod-like samplers, as well as a biological assay with susceptible wheat plants to monitor inoculum densities of Pst and to detect other fungal pathogens as well. Air samples were collected from each location and sent to the National Agricultural Genotyping Center in Fargo, ND, for detecting target pathogens using quantitative real-time PCR assays. Pst spores were detected in all locations at least once from 2022 – 2024. In addition to Pst, spores of Puccinia graminis f. sp. tritici (stem rust), as well as spores of wheat leaf blotch fungi, including Parastagonospora nodorum, Pyrenophora tritici-repentis, and Zymoseptoria tritici (Stagonospora nodorum leaf blotch, tan spot, and Septoria tritici blotch respectively), were also detected. In comparison of the different air samplers, Burkard air samplers collected more stripe rust spores than rotorod-like samplers, but they were inconsistent when compared to rotorod-like samplers in collecting other fungal spores. Temperature and moisture variables were evaluated for changing trends over 60 years in Pullman, WA. These variables were then evaluated using similar procedures from a previous study completed by Sharma-Poudyal and Chen in 2011 to evaluate if weather changes affect variables significantly associated with stripe rust damage. The stripe rust yield loss data and weather data from 1975 – 2024 were used to compare significant variables identified in the previous study and the current study. Several single variable models were developed using the significant variables found, and some were significant at p-value = 0.5, such as the August sum of maximum temperature (r2 adjusted = 0.189, p-value = 0.002). This portion of the study was inconclusive, as there are likely differences in variable analysis and weather data sources that contributed to the differing significance of the variables found in each study, as well as other confounding variables that were not accounted for in the single variable models. In a separate study, three candidate models were developed using monthly weather variables from 1960 – 2023 and a set of stripe rust severity data compiled from several different sources for Washington state. The logistical regression models were developed using several temperature and moisture variables, as well as the stripe rust level data. The models developed in this study varied from other models currently being utilized worldwide and in the U.S., but they all utilized similar climate components that indicate the weather plays a crucial role in stripe rust disease development. These models could be used as preliminary framework for more complex models in the future that encompass the changing weather trends and allow for more accurate predictions for growers managing stripe rust in eastern Washington.

Monitoring inoculum density of the stripe rust pathogen of wheat and evaluating weather variables in relation to disease severity in eastern Washington

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