TY - JOUR
T1 - Disease cycle approach to plant disease prediction
AU - De Wolf, Erick D.
AU - Isard, Scott A.
PY - 2008/6
Y1 - 2008/6
N2 - 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.
AB - 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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U2 - 10.1146/annurev.phyto.44.070505.143329
DO - 10.1146/annurev.phyto.44.070505.143329
M3 - Review article
C2 - 17408356
AN - SCOPUS:35148874157
SN - 0066-4286
VL - 45
SP - 203
EP - 220
JO - Annual Review of Phytopathology
JF - Annual Review of Phytopathology
ER -