Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields

Jaime F. Willbur, Mamadou L. Fall, Adam M. Byrne, Scott A. Chapman, Megan M. McCaghey, Brian D. Mueller, Roger Schmidt, Martin I. Chilvers, Daren S. Mueller, Mehdi Kabbage, Loren J. Giesler, Shawn P. Conley, Damon L. Smith

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2592-2601
Number of pages10
JournalPlant disease
Volume102
Issue number12
DOIs
StatePublished - Dec 1 2018

Fingerprint

Sclerotinia
stem rot
Sclerotinia sclerotiorum
Glycine max
soybeans
weather
education
irrigated farming
pests
prediction
disease surveillance
ascospores
meteorological data
wind speed
air temperature
relative humidity
inoculum
developmental stages
flowering

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Plant Science

Cite this

Willbur, J. F., Fall, M. L., Byrne, A. M., Chapman, S. A., McCaghey, M. M., Mueller, B. D., ... Smith, D. L. (2018). Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields. Plant disease, 102(12), 2592-2601. https://doi.org/10.1094/PDIS-02-18-0245-RE

Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields. / Willbur, Jaime F.; Fall, Mamadou L.; Byrne, Adam M.; Chapman, Scott A.; McCaghey, Megan M.; Mueller, Brian D.; Schmidt, Roger; Chilvers, Martin I.; Mueller, Daren S.; Kabbage, Mehdi; Giesler, Loren J.; Conley, Shawn P.; Smith, Damon L.

In: Plant disease, Vol. 102, No. 12, 01.12.2018, p. 2592-2601.

Research output: Contribution to journalArticle

Willbur, JF, Fall, ML, Byrne, AM, Chapman, SA, McCaghey, MM, Mueller, BD, Schmidt, R, Chilvers, MI, Mueller, DS, Kabbage, M, Giesler, LJ, Conley, SP & Smith, DL 2018, 'Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields', Plant disease, vol. 102, no. 12, pp. 2592-2601. https://doi.org/10.1094/PDIS-02-18-0245-RE
Willbur, Jaime F. ; Fall, Mamadou L. ; Byrne, Adam M. ; Chapman, Scott A. ; McCaghey, Megan M. ; Mueller, Brian D. ; Schmidt, Roger ; Chilvers, Martin I. ; Mueller, Daren S. ; Kabbage, Mehdi ; Giesler, Loren J. ; Conley, Shawn P. ; Smith, Damon L. / Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields. In: Plant disease. 2018 ; Vol. 102, No. 12. pp. 2592-2601.
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abstract = "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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