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An R-Based Landscape Validation of a Competing Risk Model
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Landscape-scale disease risk quantification and prediction.

Jonathan Yuen1, Asimina Mila

  • 1Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences, Uppsala SE 750 07, Sweden;

Annual Review of Phytopathology
|June 7, 2015
PubMed
Summary
This summary is machine-generated.

Predicting plant disease epidemics requires integrating pathogen, environmental, and host factors. Incorporating all elements of the disease triangle can improve landscape-scale disease occurrence predictions.

Keywords:
Bayesian decision theorydisease triangleepidemiologymodeling

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Area of Science:

  • Plant pathology
  • Epidemiology
  • Landscape ecology

Background:

  • Plant disease epidemics are studied at landscape scales for predictive insights.
  • Current prediction systems often focus on pathogen and environmental factors within the disease triangle.
  • Host plant presence and landscape patterns are less frequently incorporated into predictive models.

Purpose of the Study:

  • To explore the development of landscape-scale plant disease prediction systems.
  • To examine the role of the disease triangle components in predictive modeling.
  • To identify potential improvements for existing predictive systems.

Main Methods:

  • Review of existing parametric methods relating environmental conditions to disease.
  • Analysis of systems incorporating pathogen and environmental data.
  • Consideration of host plant distribution and landscape patterns.

Main Results:

  • Parametric methods effectively link environmental conditions to disease via inoculum production and infection processes.
  • Landscape-scale prediction systems often omit host plant factors.
  • Migratory paths of pathogen propagules are sometimes included in predictions.

Conclusions:

  • Integrating all components of the disease triangle (pathogen, environment, host) is crucial for enhancing landscape-scale disease prediction accuracy.
  • Future research should focus on incorporating host plant dynamics into predictive models.
  • Improved models will lead to better management strategies for plant diseases.