Predicting wheat stripe rust epidemics according to influential climatic variables
From 2009 to 2018, a total of 80 wheat crops were studied at plot and regional scales to predict stripe rust epidemics based on influential climatic indicators in Kermanshah province, Iran. Disease onset time and epidemic intensity varied spatially and temporarily. The disease epidemic variable was classified as having experienced nonepidemic, moderate or severe epidemics to be used for statistical analysis. Principal component analysis (PCA) was used to identify climatic variables associated
... iables associated with occurrence and intensity of stripe rust epidemics. Two principal factors accounting for 70% of the total variance indicated association of stripe rust epidemic occurrence with the number of icy days with minimum temperatures below 0°C (for subtropical regions) and below −10°C (for cool temperate and semi-arid regions). Disease epidemic intensity was linked to the number of rainy days, the number of days with minimum temperatures within the range of 7−8°C and relative humidity (RH) above 60%, and the number of periods involving consecutive days with minimum temperature within the range of 6−9°C and RH% > 60% during a 240-day period, from September 23 to May 21. Among mean monthly minimum temperatures and maximum relative humidity examined, mean maximum relative humidity for Aban (from October 23 to November 21) and mean minimum temperature for Esfand (from February 20 to March 20) indicated higher contributions to stripe rust epidemic development. Confirming PCA results, a multivariate logit ordinal model was developed to predict severe disease epidemics. The findings of this study improved our understanding of the combined interactions between air temperature, relative humidity, rainfall, and wheat stripe rust development over a three-season period of autumn-winter-spring.