Adapting disease forecasting models to coarser scales: Global potato late blight prediction

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dc.contributor.authorSparks, Adam H.en
dc.contributor.authorForbes, Gregory Allanen
dc.contributor.authorGarrett, Karen A.en
dc.contributor.departmentSustainable Agriculture and Natural Resource Management (SANREM) Knowledgebaseen
dc.date.accessioned2016-04-19T20:07:04Zen
dc.date.available2016-04-19T20:07:04Zen
dc.date.issued2009en
dc.description.abstractMany predictive models of plant disease rely upon fine-scale weather data collected in hourly increments, or finer. This data requirement is a major constraint when applying disease prediction models in areas of the world where hourly weather data are unreliable or unavailable. In response to the need to apply predictive models where only coarse resolution weather data are available, we developed a framework to adapt an existing potato late blight forecast model, SimCast. We envision this type of coarser resolution model being useful in long term decision making rather than for within growing season. For long term modeling we may be satisfied with being able to estimate the magnitude of upward and downward trends.en
dc.description.notesLTRA-4 (Practices and Strategies for Vulnerable Agro-Ecosystems)en
dc.format.mimetypeapplication/pdfen
dc.identifier4401en
dc.identifier.citationPoster presented at The American Phytopathological Society Annual Meeting, Portland, Oregon, 1-5 Aug 2009en
dc.identifier.other4401_adapting_disease.pdfen
dc.identifier.urihttp://hdl.handle.net/10919/68705en
dc.language.isoen_USen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDisease controlen
dc.subjectAgricultural ecosystemsen
dc.subjectDisease forecasting modelen
dc.subjectCoarser scalesen
dc.subjectPotatoesen
dc.subjectLate blighten
dc.subjectEcosystemen
dc.titleAdapting disease forecasting models to coarser scales: Global potato late blight predictionen
dc.typePosteren
dc.type.dcmitypeTexten

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