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

Brian L Beres,Bernard D Hill,David Weaver,Hector Carcamo,Janet Knodel,Richard Douglas Cuthbert

doi:10.1139/cjps-2016-0364

Abstract

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 a plant’s resistance is based on its ability to develop pith in the culm of the stem, which is influenced greatly by interactions between the genotype and 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...

Highlights

Complete List of Authors: Beres, Brian; Agriculture and Agri-Food Canada, Sustainable Production Systems Hill, Bernard; Retired Carcamo, Hector; AAFC, Lethbridge Reseach Centre Knodel, Janet; North Dakota State University Weaver, David Cuthbert, Richard; Semiarid Prairie Agricultural Research Centre, Agriculture & Agri-Food Canada
An artificial neural network model to predict wheat stem sawfly cutting in solid-stemmed wheat cultivars
Our objective was to improve upon past models that used regression analyses by deploying an Artificial Neural Network (ANN) model to predict in-season stem cutting of wheat by wheat stem sawfly

Summary

Introduction

Complete List of Authors: Beres, Brian; Agriculture and Agri-Food Canada, Sustainable Production Systems Hill, Bernard; Retired Carcamo, Hector; AAFC, Lethbridge Reseach Centre Knodel, Janet; North Dakota State University Weaver, David Cuthbert, Richard; Semiarid Prairie Agricultural Research Centre, Agriculture & Agri-Food Canada. 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), has been a major pest of wheat (Triticum aestivum L.) in the northern Great Plains of North America for more than 100 years Within this geographical region, the areas subjected to greatest attack historically, have been southern Alberta and Saskatchewan, southwestern Manitoba, eastern and northern Montana, North Dakota, northern South Dakota, and western Minnesota (Beres et al 2011). The adult sawfly inflicts little injury on its host plant but the stem boring activity of the larva destroys parenchyma tissue and vascular bundles of the host, causing a significant reduction in photosynthetic capacity (Macedo et al 2005) This can translate into >15% losses in grain weight (Holmes 1977; Morrill et al 1992; Seamans et al 1944). The heterogeneity implies that, for S615, a portion of the plants within each field/plot may not express an ideal solid phenotype because they are not carriers of SSt1 (Beres et al 2013)

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