2022 journal article
Evaluation of a Model for Predicting Onset of Septoria nodorum Blotch in Winter Wheat
PLANT DISEASE, 107(4), 1122–1130.
author keywords: cereals and grains; disease development and spread; disease onset; disease warning systems; epidemiology; field crops; model accuracy; prediction model; sensitivity; yield
UN Sustainable Development Goal Categories
2. Zero Hunger
(Web of Science)
13. Climate Action
(Web of Science)
Source: Web Of Science
Added: April 1, 2024
Prediction models that aid growers in making decisions on timing of fungicide application are important components of integrated management programs for several foliar diseases of wheat. The risk of Septoria nodorum blotch (caused by Parastagonospora nodorum) onset in winter wheat has been reported to be influenced by location, amount of wheat residue in the field, and cumulative daily infection values 2 weeks prior to day of year (DOY) 102. A model previously developed based on these predictor variables was evaluated for its ability to predict disease onset under field conditions. An experiment was conducted at three locations in North Carolina in 2018, 2019, and 2020, where plots were either treated with >20% wheat residue or received no residue treatment. Plots were monitored for disease symptoms, and disease onset was defined to have occurred when mean disease incidence in a plot was 50%. Of the 298 disease cases recorded, disease onset occurred early (i.e., prior to DOY 102) in 257 cases, while onset was late (i.e., on or after DOY 102) in 41 cases. Model accuracy based on correct classification ranged from 0.67 to 0.95, with a mean of 0.87 across the study period. Similarly, sensitivity rates of the model ranged from 0.88 to 1.0 with a mean of 0.98 across all years. However, the model had low specificity, with a mean rate of 0.15 across the study period. Overall, there was no significant difference in the frequency of observed and predicted cases in the study (χ2 = 0.50, P = 0.7788, df = 2). Time to disease onset was significantly correlated with grain yield and explained 26% of variation in yield (P < 0.0001). Results indicated that the disease onset model performs well in predicting early disease onset but requires further evaluation and improvement, particularly in the Piedmont, where it over-predicted early onset in 2 successive years.