Models of Plant Resistance Deployment
Loup Rimbaud1,2, Frédéric Fabre3, Julien Papaïx4
1INRAE, Pathologie Végétale, 84140 Montfavet, France; email: loup.rimbaud@inrae.fr, benoit.moury@inrae.fr.
Annual Review of Phytopathology
|April 30, 2021
Summary
Mathematical models help optimize plant disease resistance strategies. This review analyzes 69 studies, offering insights into effective resistance management for durable crop protection against evolving plant pathogens.
Area of Science:
- Agricultural Science
- Plant Pathology
- Mathematical Modeling
Background:
- Plant pathogens evolve rapidly, overcoming genetic resistance and causing crop epidemics.
- Effective resistance management strategies are crucial for sustainable agriculture.
- Large-scale experimental testing of these strategies is logistically challenging.
Purpose of the Study:
- To review and analyze mathematical modeling studies on plant disease resistance deployment strategies.
- To evaluate different model structures, assumptions, and criteria used in resistance management research.
- To identify key findings and future research directions in modeling resistance durability.
Main Methods:
- Systematic review and analysis of 69 published mathematical modeling studies.
- Categorization of models based on structure (e.g., demographic, spatial) and assumptions (e.g., pre-adapted pathogens).
- Evaluation of studies based on criteria like resistance durability, disease control, and cost-effectiveness.
Main Results:
- Modeling studies provide valuable insights into optimizing resistance deployment strategies.
- Key factors influencing resistance durability include pathogen evolutionary potential and deployment scale.
- Different model types offer complementary perspectives on resistance management.
Conclusions:
- Mathematical modeling is a powerful tool for guiding resistance management strategies.
- Further research should focus on integrating diverse model structures and real-world complexities.
- Improved modeling can enhance the durability of plant resistance and reduce crop losses.
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