Class
Article
College
College of Science
Department
English Department
Faculty Mentor
Luis Gordillo
Presentation Type
Poster Presentation
Abstract
Curly Top disease (CT), caused by a family of curtoviruses, infects a wide variety of agricultural crops. Historically, CT has caused extensive damage in tomato crops resulting in substantial economic loss for the tomato industry. Control methods for CT are scarce, and methods for predicting and assessing the scope of CT outbreaks are limited. In this paper, we formulate two theoretical models, a deterministic model and a stochastic model, for the spread of CT in a heterogeneous environment consisting of beets (preferred host) and tomatoes. The models are composed of two susceptible classes and two infected classes (infectious beets and non-infectious tomatoes). The deterministic model is formulated as a system of coupled ordinary differential equations, and the stochastic model is implemented using a stochastic simulation framework. We parameterize the models using data from a field experiment and assess the variability of CT outbreaks through simulations.
Location
Logan, UT
Start Date
4-8-2022 12:00 AM
Included in
Modeling the Spread of Curly Top Disease in Tomato Crops
Logan, UT
Curly Top disease (CT), caused by a family of curtoviruses, infects a wide variety of agricultural crops. Historically, CT has caused extensive damage in tomato crops resulting in substantial economic loss for the tomato industry. Control methods for CT are scarce, and methods for predicting and assessing the scope of CT outbreaks are limited. In this paper, we formulate two theoretical models, a deterministic model and a stochastic model, for the spread of CT in a heterogeneous environment consisting of beets (preferred host) and tomatoes. The models are composed of two susceptible classes and two infected classes (infectious beets and non-infectious tomatoes). The deterministic model is formulated as a system of coupled ordinary differential equations, and the stochastic model is implemented using a stochastic simulation framework. We parameterize the models using data from a field experiment and assess the variability of CT outbreaks through simulations.