A Model for Predicting Onset of Stagonospora nodorum Blotch in Winter Wheat Based on Preplanting and Weather Factors.
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 application after flag leaf emergence. Although SNB can occur prior to flag leaf emergence, the relationship between the time of disease onset and yield has not been determined. Such a relationship is useful in identifying a threshold to facilitate prediction of disease onset in the field. Disease occurred in 390 of 435 disease cases that were recorded across 11 counties in North Carolina from 2012 to 2014. Using cases with disease occurrence, the effect of disease 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 in only 28% of those cases when onset occurred on or after DOY 102. Subsequently, binary logistic regression models were developed to predict the occurrence and onset of SNB using preplanting factors and cumulative daily infection values (cDIV) starting 1 to 3 weeks prior to DOY 102. Logistic regression showed that previous crop, latitude, and cDIV accumulated 2 weeks prior to DOY 102 (cDIV.2) were significant (P < 0.0001) predictors of disease occurrence, and wheat residue, latitude, longitude, and cDIV.2 were significant (P < 0.0001) predictors of disease onset. The disease onset model had a correct classification rate of 0.94 and specificity and sensitivity rates >0.90. Performance of the disease onset model based on the area under the receiver operating characteristic curve (AUC), κ, and the true skill statistic (TSS) was excellent, with prediction accuracy values >0.88. Similarly, internal validation of the disease onset model based on AUC, κ, and TSS indicated good performance, with accuracy values >0.88. This disease onset prediction model could serve as a useful decision support tool to guide fungicide applications to manage SNB in wheat.
First and third authors: Center for Integrated Fungal Research, and second author: United States Department of Agriculture-Agricultural Research Service, Department of Entomology and Plant Pathology, North Carolina State University, Raleigh 27695.
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Mehra
First and third authors: Center for Integrated Fungal Research, and second author: United States Department of Agriculture-Agricultural Research Service, Department of Entomology and Plant Pathology, North Carolina State University, Raleigh 27695.
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Cowger
First and third authors: Center for Integrated Fungal Research, and second author: United States Department of Agriculture-Agricultural Research Service, Department of Entomology and Plant Pathology, North Carolina State University, Raleigh 27695.
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Ojiambo
D001203:Ascomycota / Q000502:physiology*; D000068598:Data Accuracy; D054884:Host-Pathogen Interactions; D016015:Logistic Models; D015233:Models, Statistical; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000382:microbiology; D014908:Triticum / Q000382:microbiology*; D014887:Weather
model accuracy