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| Web URL(s): | http://apsjournals.apsnet.org/doi/pdf/10.1094/PHYTO.2010.100.6.S1#page=120
Last checked: 11/24/2010
Requires: PDF Reader |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Smith, D. L.;
Kerns, J. P. |
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| Author Affiliation: | Smith: Oklahoma State University, Stillwater, OK; Kerns: University of Wisconsin, Madison, WI |
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| Title: | Using weather variables to predict the probability of dollar spot development |
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| Section: | 2010 APS Annual Meeting abstracts of presentations
Other records with the "2010 APS Annual Meeting abstracts of presentations" Section
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| Source: | Phytopathology. Vol. 100, No. 65, June supplement 2010, p. S120. |
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| Publishing Information: | St. Paul, MN: American Phytopathological Society |
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| # of Pages: | 1 |
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| Keywords: | TIC Keywords: Application timing; Disease forecasting; Disease severity; Dollar spot; Fungicide application; Weather data; Weather monitoring
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| Abstract/Contents: | "Dollar spot, caused by Sclerotinia homoeocarpa, is the most damaging disease of cool-season turfgrasses throughout the U.S. A reliable dollar spot prediction model would be useful for timing fungicide applications for highvalue turfgrasses. Logistic regression was used to develop a model to predict the probability of dollar spot development on creeping bentgrass using weather variables as inputs at sites in Oklahoma (2008 and 2009) and Wisconsin (2009). Numbers of dollar spot foci were counted daily in plots receiving no fungicide or treated with fungicide. Various on-site weather variables were recorded hourly. Weather data were transformed to 5-day moving averages. Disease severity/plot was converted to a binomial variable where 1 was average severity ā„1 spot and 0 was average severity < 1 spot. Transformed weather data and the class variables season and fungicide, were used as independent variables with average disease severity (DS) as the dependent variable in model development. The best model included the class variable fungicide, and 5-day moving averages of daily relative humidity and minimum daily air temperature (Max-rescaled R-square=0.46; C=0.89). Minimum temperature thresholds to activate the model were set at 14Ā°C based on field and controlled environment chamber studies. Independent validation exercises demonstrated that the model accurately recommended fungicide sprays when an action threshold of 30% was used in the model at both locations." |
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| Language: | English |
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| References: | 0 |
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| Note: | This item is an abstract only! |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Smith, D. L., and J. P. Kerns. 2010. Using weather variables to predict the probability of dollar spot development. Phytopathology. 100(65):p. S120. |
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| Web URL(s):
http://apsjournals.apsnet.org/doi/pdf/10.1094/PHYTO.2010.100.6.S1#page=120
Last checked: 11/24/2010
Requires: PDF Reader |
| 
MSU catalog number: b2219736a |
| Find from within TIC:
Digitally in TIC by file name: phytp2010junpres |
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