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

Area of Science:

• Agricultural Entomology
• Pest Management Science
• Ecological Modeling

Background:

• Bemisia tabaci (whitefly) poses a significant threat to global agriculture, causing crop damage and transmitting viruses.
• Existing pest management strategies often lack precise application timing, leading to reduced efficacy.
• Understanding the toxicological impacts of pesticides on different life stages is crucial for effective control.

Purpose of the Study:

• To develop and parameterize a dynamic energy budget theory-based toxicokinetic-toxicodynamic (TKTD) model for Bemisia tabaci.
• To integrate the TKTD model with an individual-based model (IBM) for predicting population dynamics and pesticide efficacy.
• To identify optimal spidoxamat application strategies for managing whitefly populations under various field conditions.

Main Methods:

• Development and parameterization of a TKTD model assessing whitefly mortality and reproductive effects.
• Integration of the TKTD model into an IBM to simulate population dynamics and pesticide efficacy.
• Validation of the integrated model using field trial data from India, Pakistan, and Brazil.

Main Results:

• The integrated TKTD-IBM model accurately predicted whitefly population dynamics and spidoxamat efficacy.
• Optimal application timing for enhanced population control was identified, particularly a second application 7-14 days post-initial treatment.
• Application timing effectiveness is influenced by ambient temperature, population structure, efficacy half-life, and immature stage duration.

Conclusions:

• Integrating empirical data with mechanistic modeling provides a robust framework for understanding pesticide effects on Bemisia tabaci.
• Precise, data-driven application strategies are essential for effective whitefly management and sustainable agriculture.
• The developed model offers a valuable tool for optimizing pest control interventions and mitigating agricultural losses.