Modeling arthropods to support IPM in vineyards

Abstract

This chapter describes the use of mathematical modeling in support of vineyard integrated pest management (IPM) programs. In IPM, models are used to represent aspects of the agroecosystem that includes the crop, insect and mite pests and their natural enemies and external factors (driving variables) such as weather, pesticide applications and horticultural practices (Getz and Gutierrez 1982; Baumgrtner et al. 1988). We are all familiar with verbal, descriptive models which appear in scientific papers or technical articles and give a picture of aspects of the system by means of graphs, tables and verbal descriptions. If they are based on a solid foundation of knowledge, descriptive verbal models will not only clearly portray our understanding of arthropod dynamics in vineyards, but can also suggest solutions, even for complex pest problems. As an example, Mizell et al. (2008) present a detailed overview of the ecology of the glassy-winged sharpshooter, Homalodisca vitripennis (Germar). This leafhopper feeds on a broad array of wild and cultivated plants, including grapevines, and is a vector for the bacterium Xylella fastidiosa Wells et al., the causative agent of Pierces disease of grapevines. Using a flow diagram, a series of tables, and detailed verbal description based on extensive research, the authors describe a conceptual model that integrates insect behavior, life history strategies and their associated risks, with the nutritional requirements of each life stage. The model not only describes the insect-host system, but also shows how appropriate manipulation of plant communities could effectively suppress H. vitripennis and X. fastidiosa, thus protecting crops such as citrus and grapes. In contrast to verbal models, mathematical models can be used to predict aspects of system behavior given an understanding of initial conditions and driving variables. The advantages of mathematical models compared with verbal descriptive models can include greater clarity of structure, clearer exposure of underlying assumptions, and generation of the correct dynamic consequences of the functions contained in the model (Forester 1968). The model is useful in its developmental stages if it clarifies thought, captures and records what we know, and allows us to see the consequences of our assumptions, whether those assumptions are later found to be right or wrong. Later on, the mathematical model succeeds if it opens the road to improving the accuracy with which we can represent reality.