Abstract

Simple SummaryMathematical models are developed to predict key aspects of insects harmful to many crops, including grapevine. Practical applications of these models include forecasting seasonal occurrence and spread over space in order to make decisions about pest management (e.g., timing of insecticide sprays). Many models have recently been developed to evaluate the spread of insect pests on grapevine under a climate change scenario as well as to forecast the possibility that alien species could settle into new environments. To make the published models available to vine-growers and their stakeholders, a holistic approach presenting these models within the frame of a decision support system should be followed.This paper reviews the existing predictive models concerning insects and mites harmful to grapevine. A brief conceptual description is given on the definition of a model and about different types of models: deterministic vs. stochastics, continuous vs. discrete, analytical vs. computer-based, and descriptive vs. data-driven. The main biological aspects of grapevine pests covered by different types of models are phenology, population growth and dynamics, species distribution, and invasion risk. A particular emphasis is put on forecasting epidemics of plant disease agents transmitted by insects with sucking-piercing mouthparts. The most investigated species or groups are the glassy-winged sharpshooter Homalodisca vitripennis (Germar) and other vectors of Xylella fastidiosa subsp. fastidiosa, a bacterium agent of Pierce’s disease; the European grape berry moth, Lobesia botrana (Denis and Schiffermuller); and the leafhopper Scaphoideus titanus Ball, the main vector of phytoplasmas agents of Flavescence dorée. Finally, the present and future of decision-support systems (DSS) in viticulture is discussed.

Highlights

  • Arthropod pests cause yearly heavy losses in viticulture

  • Many models in grapevine entomology are about single species, the future of modelling in pest management should be directed towards comprehensive frameworks, embracing more aspects of the problem, e.g., economics, crop yields, etc., in which the insect pest becomes a part of the whole [6]

  • Developmental models are usually based on temperature, which is the main abiotic factor driving the physiological response of poikilothermic organisms, including insects [6,10]

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Summary

Introduction

Arthropod pests cause yearly heavy losses in viticulture. The cost of pest management is constantly increasing due to a lack of active ingredients, the introduction of alien species, and so on. The main (ultimate) purpose of a model in agriculture is to produce a pest-management decisionsupport system (DSS) [2,3] This fits phenological and demographical models, which allow for a forecast of insect population dynamics over time, permitting to drive insecticidal sprays [4]. The following keywords (listed here alphabetically) were used in multiple combinations: berry moth(s), calibration, climate change, decision-support system, demographic, deterministic, entomology, epidemiology, Eupoecilia ambiguella, insects, invasion risk, leafhoppers, Lobesia botrana, mealybugs, model (modelling), grape, grapevine, parametrization, pest, pest management, phenology, phytoplasmas, Pierce’s disease, population dynamics, prediction, Scaphoideus titanus, spatial distribution, spider mites, stochastic, validation, vectors, viticulture, Vitis vinifera, and Xylella fastidiosa. When the searched pest was a grapevine specialist (e.g., Scaphoideus titanus), the keyword related to grapevine was omitted

What Is a Model?
Pest Population Growth and Dynamic
Pest Invasion Risk
Modelling Grapevine Pests
1: Grapevine
Case Study 2
Case Study 3
Other Species
Findings
Decision Support Systems
Full Text
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