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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1094/PDIS-02-18-0245-RE
|2 doi
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|a pubmed24n1224.xml
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|a (DE-627)NLM289693101
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|a (NLM)30334675
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|a DE-627
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|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Willbur, Jaime F
|e verfasserin
|4 aut
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| 245 |
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|a Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 28.02.2019
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|a Date Revised 13.12.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a In soybean, Sclerotinia sclerotiorum apothecia are the sources of primary inoculum (ascospores) critical for Sclerotinia stem rot (SSR) development. We recently developed logistic regression models to predict the presence of apothecia in irrigated and nonirrigated soybean fields. In 2017, small-plot trials were established to validate two weather-based models (one for irrigated fields and one for nonirrigated fields) to predict SSR development. Additionally, apothecial scouting and disease monitoring were conducted in 60 commercial fields in three states between 2016 and 2017 to evaluate model accuracy across the growing region. Site-specific air temperature, relative humidity, and wind speed data were obtained through the Integrated Pest Information Platform for Extension and Education (iPiPE) and Dark Sky weather networks. Across all locations, iPiPE-driven model predictions during the soybean flowering period (R1 to R4 growth stages) explained end-of-season disease observations with an accuracy of 81.8% using a probability action threshold of 35%. Dark Sky data, incorporating bias corrections for weather variables, explained end-of-season disease observations with 87.9% accuracy (in 2017 commercial locations in Wisconsin) using a 40% probability threshold. Overall, these validations indicate that these two weather-based apothecial models, using either weather data source, provide disease risk predictions that both reduce unnecessary chemical application and accurately advise applications at critical times
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Research Support, U.S. Gov't, P.H.S.
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| 650 |
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|a Fungicides, Industrial
|2 NLM
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| 700 |
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|a Fall, Mamadou L
|e verfasserin
|4 aut
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|a Byrne, Adam M
|e verfasserin
|4 aut
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| 700 |
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|a Chapman, Scott A
|e verfasserin
|4 aut
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|a McCaghey, Megan M
|e verfasserin
|4 aut
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| 700 |
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|a Mueller, Brian D
|e verfasserin
|4 aut
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| 700 |
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|a Schmidt, Roger
|e verfasserin
|4 aut
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| 700 |
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|a Chilvers, Martin I
|e verfasserin
|4 aut
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| 700 |
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|a Mueller, Daren S
|e verfasserin
|4 aut
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| 700 |
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|a Kabbage, Mehdi
|e verfasserin
|4 aut
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| 700 |
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|a Giesler, Loren J
|e verfasserin
|4 aut
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|a Conley, Shawn P
|e verfasserin
|4 aut
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| 700 |
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|a Smith, Damon L
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Plant disease
|d 1997
|g 102(2018), 12 vom: 17. Dez., Seite 2592-2601
|w (DE-627)NLM098181742
|x 0191-2917
|7 nnns
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| 773 |
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|g volume:102
|g year:2018
|g number:12
|g day:17
|g month:12
|g pages:2592-2601
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| 856 |
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|u http://dx.doi.org/10.1094/PDIS-02-18-0245-RE
|3 Volltext
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