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| DOI: | 10.1094/PHYTO.2009.99.6.S1 |
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| Web URL(s): | http://apsjournals.apsnet.org/doi/pdf/10.1094/PHYTO.2009.99.6.S1#page=121
Last checked: 06/11/2009
Requires: PDF Reader
Notes: Item is within a single large file |
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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.;
Payne, A. F.;
Walker, N. R. |
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| Author Affiliation: | Oklahoma State University, Stillwater, Oklahoma |
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| Title: | Logistic regression modeling of dollar spot epidemics using weather variables as inputs |
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| Section: | Abstracts submitted for presentation at the 2009 APS annual meeting
Other records with the "Abstracts submitted for presentation at the 2009 APS annual meeting" Section
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| Meeting Info.: | Portland, Oregon: August 1-5, 2009 |
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| Source: | Phytopathology. Vol. 99, No. 6, June Supplement 2009, p. S121. |
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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: Agrostis stolonifera; Dollar spot; Fungicide application; Golf greens; Sclerotinia homoeocarpa; Relative humidity; Temperatures; Weather
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| Abstract/Contents: | "Dollar spot, cause by Sclerotinia homoeocarpa, is the most damaging disease of many turfgrasses in Oklahoma. A reliable dollar spot prediction model would be useful for making management decisions for high-value turfgrasses. Logistic regression was used to develop a model that input weather variables to predict probability of the occurrence of dollar spot on creeping bentgrass putting greens. Numbers of disease foci were determined daily in plots receiving no fungicide or treated preventatively or curatively with fungicide in the spring and fall seasons of 2008. Various on-site weather variables were recorded hourly. Weather data were transformed to 2-, 3-, 4-, and 5-day moving averages. Weather data and class variables (season and fungicide application) were used as independent variables and disease data as dependent variables in logistic regression analysis to identify best fitting models. Models using 5-day moving averages were better than models using other moving averages. Relative humidity was the only highly significant (P = 0.0006) weather variable. The best models also included season and fungicide application class variables. Temperature variables were not significant (P = 0.60), but minimum thresholds for disease symptom appearance were established to determine when to implement the predictive model. The model will be validated for use in determining the risk of dollar spot development to aid in fungicide application decision making process." |
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| Language: | English |
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| References: | 0 |
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| Note: | This item is an abstract only!
"2009 APS Annual Meeting" |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Smith, D. L., A. F. Payne, and N. R. Walker. 2009. Logistic regression modeling of dollar spot epidemics using weather variables as inputs. Phytopathology. 99(6):p. S121. |
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| DOI: 10.1094/PHYTO.2009.99.6.S1 |
| Web URL(s):
http://apsjournals.apsnet.org/doi/pdf/10.1094/PHYTO.2009.99.6.S1#page=121
Last checked: 06/11/2009
Requires: PDF Reader
Notes: Item is within a single large file |
| 
MSU catalog number: b2219736a |
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Digitally in TIC by file name: phytp2009junpres |
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