An epidemiological framework for modelling fungicide dynamics and control.

Area of Science:

• Agricultural Science
• Mathematical Biology
• Plant Pathology

Background:

• Existing models for fungicidal control of plant diseases often lack explicit fungicide dynamics and do not account for infection during host growth.
• Effective policy development for agricultural fungal disease management necessitates robust theoretical frameworks.

Purpose of the Study:

• To introduce a novel modeling framework for fungicide application that explicitly incorporates fungicide dynamics.
• To investigate the impact of explicit fungicide modeling on the invasion and persistence of plant pathogens.
• To analyze the influence of host growth phase infection on disease dynamics.

Main Methods:

• Development of a mathematical modeling framework for fungicide application.
• Analysis of model behavior, including bistability zones and invasion thresholds.
• Comparison of explicit models with classical models for plant disease dynamics.
• Inclusion of demographic stochasticity to test model robustness.

Main Results:

• Explicit models demonstrate bistability zones where invasion and persistence thresholds are dependent on initial infection levels, unlike classical models solely reliant on the basic reproductive number (R0).
• An additional 'invasion zone' can emerge for R0 < 1 when infection occurs during the host growth phase, leading to non-persistent epidemics.
• Ideal fungicides (high efficacy, low application/decay rates) promote the existence of these bistability zones.

Conclusions:

• Explicitly modeling fungicide dynamics and considering host growth phase infection significantly alters predictions of plant pathogen invasion and persistence.
• The findings highlight the importance of initial infection levels and fungicide characteristics in disease management strategies.
• The developed modeling framework provides a more nuanced understanding for designing effective fungal disease control policies in agriculture.