Development and Validation of a Mechanistic, Weather-Based Model for Predicting Puccinia graminis f. sp. tritici Infections and Stem Rust Progress in Wheat

Abstract

Stem rust (or black rust) of wheat, caused by Puccinia graminis f. sp. tritici (Pgt), is a re-emerging, major threat to wheat production worldwide. Here, we retrieved, analyzed, and synthetized the available information about Pgt to develop a mechanistic, weather-driven model for predicting stem rust epidemics caused by uredospores. The ability of the model to predict the first infections in a season was evaluated using field data collected in three wheat-growing areas of Italy (Emilia-Romagna, Apulia, and Sardinia) from 2016 to 2021. The model showed good accuracy, with a posterior probability to correctly predict infections of 0.78 and a probability that there was no infection when not predicted of 0.96. The model’s ability to predict disease progress during the growing season was also evaluated by using published data obtained from trials in Minnesota, United States, in 1968, 1978, and 1979, and in Pennsylvania, United States, in 1986. Comparison of observed versus predicted data generated a concordance correlation coefficient of 0.96 and an average distance between real data and the fitted line of 0.09. The model could therefore be considered accurate and reliable for predicting epidemics of wheat stem rust and could be tested for its ability to support risk-based control of the disease.

Puccinia Spore Concentrations in Relation to Weather Factors and Phenological Development of a Wheat Crop in Northwestern Spain

“…The severity of rust epidemics depends on the timing of infection by the primary inoculum, plant resistance, and the climatic conditions [13]. Various disease models with different levels of complexity and data requirements have been developed worldwide to predict rust progression in wheat [10,[13][14][15][16][17]. These models are site-specific, due to the climatic variability that affects spore dispersal and deposition.…”

“…Previous studies on the detection of P. conducted in the air of wheat-growing areas utilized volumetric collectors and non-viable identification methodologies [5,[11][12][13]. By combining the aerobiological study of rust incidence and severity data with meteorological data, models for rust forecasting have been developed in Canada, the United States, Mexico, Argentina, India, Ethiopia, and Morocco [10,[13][14][15][16][17]. Phenological studies should also be considered to define regularities in the crop's growth in relation to its environment and enable the application of disease control measures at the appropriate time [18].…”

SYMPATHIQUE: Image-based tracking of Symptoms and monitoring of Pathogenesis to decompose Quantitative disease resistance in the field

“…Numerous studies have used simulation to investigate how hypothetical changes in different epidemiological parameters affect overall disease progression [4,20]. Others have employed mechanistic, spatially explicit models to predict disease levels as a function of various parameters driving disease progression at the leaf and canopy level [21][22][23][24]. Both types of studies provide useful insights into the sensitivity of the outcome to changes in any of the parameters that drive seasonal epidemics and may therefore help identify the most effective components of QR.…”

Irrigation can create new green bridges that promote rapid intercontinental spread of the wheat stem rust pathogen

“…Eversmeyer et al 1973, Rossi et al 1997, Bregaglio and Donatelli 2015, Savary et al 2015, Naseri and Sabeti 2021, Salotti et al 2022, given the spatial extent of our study area (which covers a wide range of climatic regimes) and the lack of survey data available to calibrate a more complex model, we adopt a simpler approach in our study. The crop calendar is defined in terms of a monthly timestep and planting and harvest date assumptions for each site (figure3) are based on those used inMeyer et al (2017b), the National Institute of Statistics of Rwanda (…”