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

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.