Optimizing Control Strategies for the Cotton Whitefly Bemisia tabaci: Insights from Individual-Based Modeling

TL;DR

This study uses modeling to optimize pesticide application strategies for controlling the cotton whitefly, improving pest management under varying conditions.

Contribution

A dynamic energy budget-based TKTD model is integrated into an IBM to optimize whitefly control strategies.

Findings

A second pesticide application between 7 and 14 days post-initial treatment improves whitefly control.

Optimal strategies depend on temperature, pest pressure, and developmental stages of whiteflies.

Modeling integrates empirical data to guide effective pest management decisions.

Abstract

The whitefly, Bemisia tabaci, significantly
threatens agricultural productivity through crop damage and virus
transmission. This study developed and parametrized a dynamic energy
budget theory-based toxicokinetic-toxicodynamic (TKTD) model to assess
the mortality of immature whitefly stages and the impacts of spidoxamat
exposure on fecundity and fertility in adults. The TKTD model was
integrated into an individual-based model (IBM) to predict population
dynamics and efficacy under field conditions, validated with field
trial data from different locations in India, Pakistan, and Brazil.
The integrated model identified optimal application strategies across
varying pest pressure and temperature regimes. The IBM simulation
results indicate that a second application can substantially enhance
population control, particularly when timed between 7 and 14 days
postinitial treatment, depending on ambient temperature and population
structure. This timing is influenced by the efficacy half-life and
the developmental duration of immature stages, emphasizing the importance
of precise application strategies in managing B. tabaci populations effectively. The findings underscore the importance
of integrating empirical data with modeling for a mechanistic understanding
of effects and the development of effective pest management strategies
in the face of evolving agricultural challenges.

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