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Predicting variability in biological control of a plant-pathogen system using stochastic models
G J Gibson1, C A Gilligan, A Kleczkowski
1Biomathematics & Statistics Scotland, Edinburgh, UK.
Proceedings. Biological Sciences
|October 13, 1999
Summary
This study models plant-pathogen dynamics, showing the biological control agent Trichoderma viride primarily impacts primary infections by Rhizoctonia solani. This helps predict biological control effectiveness.
Area of Science:
- Agricultural Science
- Plant Pathology
- Mycology
Background:
- Plant-pathogen interactions are complex, influenced by infection mechanisms and host susceptibility.
- Understanding these dynamics is crucial for disease management and biological control.
- Rhizoctonia solani is a significant fungal pathogen affecting various crops.
Purpose of the Study:
- To develop and validate a stochastic model for plant-pathogen dynamics.
- To investigate the impact of the antagonistic fungus Trichoderma viride on Rhizoctonia solani infection.
- To predict the efficacy of biological control agents using epidemiological parameters.
Main Methods:
- Developed a stochastic model incorporating primary/secondary infection and host susceptibility.
- Fitted the model to experimental data from radish-Rhizoctonia solani interactions.
- Utilized parameter likelihoods and profile likelihoods to analyze model fit and parameter influence.
Main Results:
- The stochastic model accurately captured variability in disease epidemics.
- Trichoderma viride was found to primarily affect primary infection rates.
- Model analysis revealed the time evolution of disease variability based on epidemiological parameters.
Conclusions:
- The developed stochastic model effectively describes plant-pathogen dynamics and variability.
- Trichoderma viride's primary role in inhibiting initial infection was confirmed.
- The model provides a predictive tool for assessing biological control agent effectiveness.