Modelling induced resistance to plant diseases

Area of Science:

• Plant Pathology
• Mathematical Biology
• Agricultural Science

Background:

• Traditional plant disease control relies on agrochemicals with environmental risks.
• Induced resistance (IR) offers an alternative but shows variable field efficacy.
• Mathematical modeling can elucidate IR dynamics for improved disease management.

Purpose of the Study:

• To propose and analyze a mathematical model for IR triggered by chemical elicitors.
• To understand the temporal dynamics of plant resistance and susceptibility.
• To quantitatively estimate the effectiveness of elicitors in plant disease control.

Main Methods:

• Developed a prototype mathematical model based on epidemiological principles.
• Modeled IR using reversible processes to describe transitions between Susceptible (S), Resistant (R), and Diseased (D) plant compartments.
• Used computer-based algorithms to match model parameters with experimental data.

Main Results:

• The model predicts the proportion of plants in S, R, and D compartments over time.
• Effectiveness of chemical elicitors in enhancing plant defense is quantitatively estimated.
• The model provides a generic framework applicable to various plant-pathogen-elicitor systems.

Conclusions:

• Mathematical modeling is a valuable tool for understanding and optimizing induced resistance in crops.
• The proposed model can guide the development of more effective and sustainable plant disease management strategies.
• This approach aids in predicting disease dynamics and assessing the impact of elicitors.