Skip to main content
ARS Home » Southeast Area » Raleigh, North Carolina » Plant Science Research » Research » Publications at this Location » Publication #328096

Research Project: Genetic Improvement of Small Grains for Biotic and Abiotic Stress Tolerance and Characterization of Pathogen Populations

Location: Plant Science Research

Title: A model for predicting onset of stagonospora nodorum blotch in winter wheat based on pre-planting and weather factors

Author
item MEHRA, LUCKY - North Carolina State University
item Cowger, Christina
item OJIAMBO, PETER - North Carolina State University

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/2/2017
Publication Date: 5/30/2017
Citation: Mehra, L., Cowger, C., Ojiambo, P.S. 2017. A model for predicting onset of stagonospora nodorum blotch in winter wheat based on pre-planting and weather factors. Phytopathology. 107:635-644.

Interpretive Summary: Stagonospora nodorum blotch (SNB) caused by Parastagonospora nodorum is a serious disease of wheat worldwide. In the United States, the disease is prevalent on winter wheat in many eastern states, and its management relies mainly on fungicide applications after flag leaf emergence. Epidemics can start at varying times during crop development, and it has not been known how the timing of disease onset affects yield. This knowledge is needed to identify a useful threshold to help predict disease onset in the field. We collected SNB data from a field experiment in 11 counties in North Carolina from 2012 to 2014, and analyzed the relationship between timing of SNB onset and yield. Our analysis showed that timing of disease onset explained 32% of the variation in yield (P < 0.0001). From the relationship between onset and yield, we identified the 102nd day of the year (DOY 102) as a disease onset threshold: below-average yield occurred 87% of the time when disease onset occurred before DOY 102, but only 28% of the time when onset occurred on or after DOY 102. Subsequently, a statistical model was developed to predict the time of SNB onset using pre-planting factors and the accumulation of favorable weather up until DOY 102. The statistical model had high rates of correctly identifying cases that would have above- or below-average yield, and could serve as a useful decision support tool for fungicide application to manage SNB in wheat.

Technical Abstract: Stagonospora nodorum blotch (SNB) is a serious disease of wheat worldwide, and it is prevalent on winter wheat in many eastern states. Management relies mainly on fungicide application after flag leaf emergence. The disease can occur prior to flag leaf emergence, however, the impact of the time of disease onset on yield has not been determined to identify a useful threshold to facilitate prediction of disease onset in the field. Using 390 disease cases collected across eleven counties in North Carolina from 2012 to 2014, the effect of SNB onset on yield was analyzed to identify a disease onset threshold that related time of disease onset to yield. Regression analysis showed that disease onset explained 32% of the variation in yield (P < 0.0001) and from this relationship, day of year (DOY) 102 was identified as the disease onset threshold. Below-average yield occurred in 87% of the disease cases when disease onset occurred before DOY 102, but only in 28% of these cases when onset occurred on or after DOY 102. Subsequently, a binary logistic regression model was developed to predict the time of SNB onset using pre-planting factors and cumulative daily infection values (cDIV) starting 1 to 3 weeks prior to DOY 102. The logistic model showed that cDIV accumulated two weeks prior to DOY 102 and wheat residue were significant (P < 0.0001) predictors of SNB onset. The model had a correct classification rate of 0.94 and specificity and sensitivity rates = 0.91. Performance of the logistic model based on several test statistics was excellent with a prediction accuracy = 0.89. Internal validation of the logistic model also indicated good performance with an accuracy = 0.83. This binary logistic regression model could serve as a useful decision support tool for fungicide application to manage SNB in wheat.