Evaluation of the Ryegrass Stem Rust Model STEMRUST_G and Its Implementation as a Decision Aid

Incidence and Distribution of Puccinia coronata and P. graminis on Turfgrass in the Midwestern United States

Crown rust (caused by Puccinia coronata) and stem rust (caused by P. graminis) are two common and destructive diseases of turfgrass in the United States. Crown rust has been associated with perennial ryegrass and stem rust with Kentucky bluegrass when identified based solely on fungal morphology. However, recent studies using molecular identification methods have indicated the host-pathogen relationship of rusts on turf to be more complex. Our primary objective was to quickly and accurately identify P. coronata and P. graminis in symptomatic turfgrass leaves over 3 years on turfgrass samples from across the Midwestern United States. Between 2013 and 2015, 413 samples of symptomatic cool-season turfgrass from Wisconsin and surrounding states were screened using real-time polymerase chain reaction. Of these samples, 396 were Kentucky bluegrass and 17% of them contained P. coronata, 69% contained P. graminis, and 13% contained both P. coronata and P. graminis. In addition, both year and location effects were observed on the distribution of Puccinia spp. collected annually from two locations in southern Wisconsin. This research supports previous conclusions that have identified variability among P. graminis and P. coronata host relationships on turfgrass, and further demonstrates that rust fungal populations on Kentucky bluegrass may not be consistent between locations in the same year or over multiple years at the same location. The increasing evidence of variation in the turfgrass rust populations will likely affect future rust management and turfgrass breeding efforts.

A simulation model for epidemics of stem rust in ryegrass seed crops

A simulation model (STEMRUST_G, named for stem rust of grasses) was created for stem rust (caused by Puccinia graminis subsp. graminicola) in perennial ryegrass grown to maturity as a seed crop. The model has a daily time step and is driven by weather data and an initial input of disease severity from field observation. Key aspects of plant growth are modeled. Disease severity is modeled as rust population growth, where individuals are pathogen colonies (pustules) grouped in cohorts defined by date of initiation and plant part infected. Infections due to either aerial spread or within-plant contact spread are modeled. Pathogen cohorts progress through life stages that are modeled as disease cycle components (colony establishment, latent period, infectious period, and sporulation) affected by daily weather variables, plant growth, and fungicide application. Fungicide effects on disease cycle components are modeled for two commonly used active ingredients, applied preinfection or postinfection. Previously validated submodels for certain disease cycle components formed the framework for integrating additional processes, and the complete model was calibrated with field data from 10 stem rust epidemics. Discrepancies between modeled outcomes and the calibration data (log10[modeled]-log10[observed]) had a mean near zero but considerable variance, with 1 standard deviation=0.5 log10 units (3.2-fold). It appears that a large proportion of the modeling error variance may be due to variability in field observations of disease severity. An action threshold for fungicide application was derived empirically, using a constructed weather input file favorable for disease development. The action threshold is a negative threshold, representing a level of disease (latent plus visible) below which damaging levels of disease are unable to develop before the yield-critical crop stage. The model is in the public domain and available on the Internet.