Principles of predicting plant virus disease epidemics
Material type:
ArticleLanguage: En Publication details: 2010Subject(s):
In:
Annual Review of PhytoPathology v. 48, p. 179-203Summary: Predicting epidemics of plant virus disease constitutes a challenging undertaking due to the complexity of the three-cornered pathosystems (virus, vector, and host) involved and their interactions with the environment. A complicated nomenclature is used to describe virus epidemiological models. This review explains how the nomenclature evolved and provides a historical account of the development of such models. The process and steps involved in devising models that incorporate weather variables and data retrieval and are able to forecast plant virus epidemics effectively are explained. Their application to provide user-friendly, Internet-based decision support systems (DSSs) that determine when and where control measures are needed is described. Finally, case studies are provided of eight pathosystems representing different scenarios in which modeling approaches have been used with varying degrees of effectiveness to forecast virus epidemics in parts of the world with temperate, Mediterranean, subtropical, and tropical climates.
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
| Article | CIMMYT Knowledge Center: John Woolston Library | Reprints Collection | Available |
Peer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0066-4286
Predicting epidemics of plant virus disease constitutes a challenging undertaking due to the complexity of the three-cornered pathosystems (virus, vector, and host) involved and their interactions with the environment. A complicated nomenclature is used to describe virus epidemiological models. This review explains how the nomenclature evolved and provides a historical account of the development of such models. The process and steps involved in devising models that incorporate weather variables and data retrieval and are able to forecast plant virus epidemics effectively are explained. Their application to provide user-friendly, Internet-based decision support systems (DSSs) that determine when and where control measures are needed is described. Finally, case studies are provided of eight pathosystems representing different scenarios in which modeling approaches have been used with varying degrees of effectiveness to forecast virus epidemics in parts of the world with temperate, Mediterranean, subtropical, and tropical climates.
English
Carelia Juarez
Reprints Collection