An Artificial Neural Network Model to Predict Wheat Stem Sawfly Cutting in Solid-Stemmed Wheat Cultivars

The wheat stem sawfly, Cephus cinctus Norton (Hymenoptera: Cephidae), is a major pest of wheat (Triticum aestivum L.) in the northern Great Plains of North America. The use of solid-stemmed cultivars helps mitigate crop losses and can also affect the survivorship of C. cinctus. The efficacy of ‘resistance’ is based on the plant’s ability to develop pith in the culm of the stem, which is influenced greatly by interactions between the genotype and the environment. Precipitation-related weather interacts with photoperiod to reduce pith expression in solid-stemmed wheat. A model that predicts pith expression could serve as a management tool to prevent losses by alerting producers if in-season precipitation patterns have caused less than ideal pith expression in a cultivar. Artificial Neural Network (ANN) models are used to make predictions for complex, non-linear systems with many co-related variables. Our objective was to improve upon past models that used regression analyses by deploying an ANN model to predict in-season stem cutting of wheat by wheat stem sawfly. Results indicate that stem cutting is influenced by the precipitation within a 5-wk period from June 1 to July 5. These results were successfully deployed in a model that should assist with predictions of potential late season stem cutting. Deployment of this ANN model as a transferable executable file may facilitate predictions of stem cutting by wheat stem sawfly in any given year, which will empower producers to implement the appropriate harvest management strategies to reduce losses.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author