Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

K. N. Neufeld, A. P. Keinath, Beth Krueger Gugino, M. T. McGrath, E. J. Sikora, S. A. Miller, M. L. Ivey, D. B. Langston, B. Dutta, T. Keever, A. Sims, P. S. Ojiambo

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

Original languageEnglish (US)
Pages (from-to)655-668
Number of pages14
JournalInternational Journal of Biometeorology
Volume62
Issue number4
DOIs
StatePublished - Apr 1 2018

Fingerprint

Sporangia
Disease Outbreaks
Rain
Spores
Gulf of Mexico
Oomycetes
rainwater
spore
pathogen
Disease Management
fungicide
Polymerase Chain Reaction
Water
growing season
assay
coast
monitoring
modeling

All Science Journal Classification (ASJC) codes

  • Ecology
  • Atmospheric Science
  • Health, Toxicology and Mutagenesis

Cite this

Neufeld, K. N. ; Keinath, A. P. ; Gugino, Beth Krueger ; McGrath, M. T. ; Sikora, E. J. ; Miller, S. A. ; Ivey, M. L. ; Langston, D. B. ; Dutta, B. ; Keever, T. ; Sims, A. ; Ojiambo, P. S. / Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model. In: International Journal of Biometeorology. 2018 ; Vol. 62, No. 4. pp. 655-668.
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abstract = "Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75{\%}, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73{\%}. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.",
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Neufeld, KN, Keinath, AP, Gugino, BK, McGrath, MT, Sikora, EJ, Miller, SA, Ivey, ML, Langston, DB, Dutta, B, Keever, T, Sims, A & Ojiambo, PS 2018, 'Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model', International Journal of Biometeorology, vol. 62, no. 4, pp. 655-668. https://doi.org/10.1007/s00484-017-1474-2

Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model. / Neufeld, K. N.; Keinath, A. P.; Gugino, Beth Krueger; McGrath, M. T.; Sikora, E. J.; Miller, S. A.; Ivey, M. L.; Langston, D. B.; Dutta, B.; Keever, T.; Sims, A.; Ojiambo, P. S.

In: International Journal of Biometeorology, Vol. 62, No. 4, 01.04.2018, p. 655-668.

Research output: Contribution to journalArticle

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AU - Neufeld, K. N.

AU - Keinath, A. P.

AU - Gugino, Beth Krueger

AU - McGrath, M. T.

AU - Sikora, E. J.

AU - Miller, S. A.

AU - Ivey, M. L.

AU - Langston, D. B.

AU - Dutta, B.

AU - Keever, T.

AU - Sims, A.

AU - Ojiambo, P. S.

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N2 - Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

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