Abstract

Idaho is ranked 5th in the United States in overall wheat production and makes over $500 million in profit annually from wheat. Many pests have detrimental effects on wheat; some of the most predominant ones are aphids. Four species of aphids having economic effects on wheat crops in Idaho are: Diuraphis noxia, Metopolophium dirhodum, Rhopalosiphum padi, Sitobion avenae. Predictive regression models could be useful for better understanding of the occurrence of these aphid species. Count data for the four species were collected over 17 years via suction traps at 12 locations in wheat fields throughout Idaho. Species specific nonlinear logistic growth models were fitted to each suction trap location to model the aphid accumulation process during the wheat growing season. The nonlinear model used was parameterized to provide inference on three main aphid characteristics, the onset of trapped aphid accumulation, the rate of increase in aphid accumulation, and the maximum accumulated abundance of trapped aphids. Suction trap locations were further aggregated into 5 environments using hierarchical clustering based on climate data. Species specific models were then fitted to each of the 5 environments. Within each environment, the maximum yearly aphid abundance was determined to have a lag (1) autocorrelation structure across years, indicating a biotic feedback. A full nonlinear logistic growth model was then fitted to the entire data set using dummy variable regression to investigate potential climatic environmental patterns in the aphid accumulation process. Predicted models were validated both externally and internally. External validation used suction trap locations in Idaho that were excluded from the model building process to assess the predictive capabilities of the specified models. Internal validation was conducted using bootstrap simulation of the residuals for each model. Statistical models similar to those developed in this study can aid in understanding and evaluating the dynamics of the abundance of cereal crop aphid species in Idaho.

Keywords

Nonlinear Regression, Logistic Growth Models, Autocorrelation, Suction Traps

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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Jan 1st, 1:01 AM

MODELING THE OCCURRENCE OF FOUR CEREAL CROP APHID SPECIES IN IDAHO

Idaho is ranked 5th in the United States in overall wheat production and makes over $500 million in profit annually from wheat. Many pests have detrimental effects on wheat; some of the most predominant ones are aphids. Four species of aphids having economic effects on wheat crops in Idaho are: Diuraphis noxia, Metopolophium dirhodum, Rhopalosiphum padi, Sitobion avenae. Predictive regression models could be useful for better understanding of the occurrence of these aphid species. Count data for the four species were collected over 17 years via suction traps at 12 locations in wheat fields throughout Idaho. Species specific nonlinear logistic growth models were fitted to each suction trap location to model the aphid accumulation process during the wheat growing season. The nonlinear model used was parameterized to provide inference on three main aphid characteristics, the onset of trapped aphid accumulation, the rate of increase in aphid accumulation, and the maximum accumulated abundance of trapped aphids. Suction trap locations were further aggregated into 5 environments using hierarchical clustering based on climate data. Species specific models were then fitted to each of the 5 environments. Within each environment, the maximum yearly aphid abundance was determined to have a lag (1) autocorrelation structure across years, indicating a biotic feedback. A full nonlinear logistic growth model was then fitted to the entire data set using dummy variable regression to investigate potential climatic environmental patterns in the aphid accumulation process. Predicted models were validated both externally and internally. External validation used suction trap locations in Idaho that were excluded from the model building process to assess the predictive capabilities of the specified models. Internal validation was conducted using bootstrap simulation of the residuals for each model. Statistical models similar to those developed in this study can aid in understanding and evaluating the dynamics of the abundance of cereal crop aphid species in Idaho.