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231225s2016 xx |||||o 00| ||eng c |
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|a 10.1094/PDIS-11-15-1321-RE
|2 doi
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|a eng
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| 100 |
1 |
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|a Grabow, B S
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Environmental Conditions Associated with Stripe Rust in Kansas Winter Wheat
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| 264 |
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|c 2016
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|a Date Revised 20.11.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Stripe rust has reemerged as a problematic disease in Kansas wheat. However, there are no stripe rust forecasting models specific to Kansas wheat production. Our objective was to identify environmental variables associated with stripe rust epidemics in Kansas winter wheat as an initial step in the longer-term goal of developing predictive models for stripe rust to be used within the state. Mean yield loss due to stripe rust on susceptible varieties was estimated from 1999 to 2012 for each of the nine Kansas crop reporting districts (CRD). A CRD was classified as having experienced a stripe rust epidemic when yield loss due to the disease equaled or exceeded 1%, and a nonepidemic otherwise. Epidemics were further classified as having been moderate or severe if yield loss was 1 to 14% or greater than 14%, respectively. The binary epidemic categorizations were linked to a matrix of 847 variables representing monthly meteorological and soil moisture conditions. Classification trees were used to select variables associated with stripe rust epidemic occurrence and severity (conditional on an epidemic having occurred). Selected variables were evaluated as predictors of stripe rust epidemics within a general estimation equations framework. The occurrence of epidemics within CRD was linked to soil moisture during the fall and winter months. In the spring, severe epidemics were linked to optimal (7 to 12°C) temperatures. Simple environmentally based stripe rust models at the CRD level may be combined with field-level disease observations and an understanding of varietal reaction to stripe rust as part of an operational disease forecasting system in Kansas
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|a Journal Article
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1 |
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|a Shah, D A
|e verfasserin
|4 aut
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| 700 |
1 |
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|a DeWolf, E D
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Plant disease
|d 1997
|g 100(2016), 11 vom: 01. Nov., Seite 2306-2312
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|g volume:100
|g year:2016
|g number:11
|g day:01
|g month:11
|g pages:2306-2312
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|u http://dx.doi.org/10.1094/PDIS-11-15-1321-RE
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