Disease cycle approach to plant disease prediction

Erick D. De Wolf, Scott Alan Isard

Research output: Contribution to journalReview article

91 Citations (Scopus)

Abstract

Plant disease cycles represent pathogen biology as a series of interconnected stages of development including dormancy, reproduction, dispersal, and pathogenesis. The progression through these stages is determined by a continuous sequence of interactions among host, pathogen, and environment. The stages of the disease cycle form the basis of many plant disease prediction models. The relationship of temperature and moisture to disease development and pathogen reproduction serve as the basis for most contemporary plant disease prediction systems. Pathogen dormancy and inoculum dispersal are considered less frequently. We found extensive research efforts evaluating the performance of prediction models as part of operation disease management systems. These efforts appear to be greater than just a few decades ago, and include novel applications of Bayesian decision theory. Advances in information technology have stimulated innovations in model application. This trend must accelerate to provide the disease management strategies needed to maintain global food supplies.

Original languageEnglish (US)
Pages (from-to)203-220
Number of pages18
JournalAnnual Review of Phytopathology
Volume45
DOIs
StatePublished - Jun 1 2008

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plant diseases and disorders
dormancy
prediction
pathogens
disease control
host-pathogen relationships
information technology
management systems
inoculum
pathogenesis
Biological Sciences
temperature

All Science Journal Classification (ASJC) codes

  • Plant Science

Cite this

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abstract = "Plant disease cycles represent pathogen biology as a series of interconnected stages of development including dormancy, reproduction, dispersal, and pathogenesis. The progression through these stages is determined by a continuous sequence of interactions among host, pathogen, and environment. The stages of the disease cycle form the basis of many plant disease prediction models. The relationship of temperature and moisture to disease development and pathogen reproduction serve as the basis for most contemporary plant disease prediction systems. Pathogen dormancy and inoculum dispersal are considered less frequently. We found extensive research efforts evaluating the performance of prediction models as part of operation disease management systems. These efforts appear to be greater than just a few decades ago, and include novel applications of Bayesian decision theory. Advances in information technology have stimulated innovations in model application. This trend must accelerate to provide the disease management strategies needed to maintain global food supplies.",
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Disease cycle approach to plant disease prediction. / De Wolf, Erick D.; Isard, Scott Alan.

In: Annual Review of Phytopathology, Vol. 45, 01.06.2008, p. 203-220.

Research output: Contribution to journalReview article

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