Literature DB >> 29177798

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

K N Neufeld1, A P Keinath2, B K Gugino3, M T McGrath4, E J Sikora5, S A Miller6, M L Ivey6,7, D B Langston8, B Dutta9, T Keever1, A Sims10, P S Ojiambo11.   

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.

Entities:  

Keywords:  Aerobiological modeling system; Cucurbit downy mildew; ROC analysis; Risk prediction; Spore deposition; True skill statistic

Mesh:

Year:  2017        PMID: 29177798     DOI: 10.1007/s00484-017-1474-2

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  15 in total

1.  PCR error and molecular population genetics.

Authors:  N Kobayashi; K Tamura; T Aotsuka
Journal:  Biochem Genet       Date:  1999-10       Impact factor: 1.890

2.  Predicting the risk of soybean rust in Minnesota based on an integrated atmospheric model.

Authors:  Zhining Tao; Dean Malvick; Roger Claybrooke; Crystal Floyd; Carl J Bernacchi; Greg Spoden; James Kurle; David Gay; Van Bowersox; Sagar Krupa
Journal:  Int J Biometeorol       Date:  2009-06-14       Impact factor: 3.787

Review 3.  Epidemiology and population biology of Pseudoperonospora cubensis: a model system for management of downy mildews.

Authors:  Peter S Ojiambo; David H Gent; Lina M Quesada-Ocampo; Mary K Hausbeck; Gerald J Holmes
Journal:  Annu Rev Phytopathol       Date:  2015-05-18       Impact factor: 13.078

4.  Perceptions of disease risk: from social construction of subjective judgments to rational decision making.

Authors:  N McRoberts; C Hall; L V Madden; G Hughes
Journal:  Phytopathology       Date:  2011-06       Impact factor: 4.025

5.  A multiplexed immunofluorescence method identifies Phakopsora pachyrhizi Urediniospores and determines their viability.

Authors:  R Vittal; J S Haudenshield; G L Hartman
Journal:  Phytopathology       Date:  2012-12       Impact factor: 4.025

6.  Spatiotemporal spread of cucurbit downy mildew in the eastern United States.

Authors:  P S Ojiambo; G J Holmes
Journal:  Phytopathology       Date:  2011-04       Impact factor: 4.025

7.  Quantitative models for germination and infection of Pseudoperonospora cubensis in response to temperature and duration of leaf wetness.

Authors:  L F Arauz; K N Neufeld; A L Lloyd; P S Ojiambo
Journal:  Phytopathology       Date:  2010-09       Impact factor: 4.025

8.  In planta distribution of 'Candidatus Liberibacter asiaticus' as revealed by polymerase chain reaction (PCR) and real-time PCR.

Authors:  Satyanarayana Tatineni; Uma Shankar Sagaram; Siddarame Gowda; Cecile J Robertson; William O Dawson; Toru Iwanami; Nian Wang
Journal:  Phytopathology       Date:  2008-05       Impact factor: 4.025

9.  Using Next-Generation Sequencing to Develop Molecular Diagnostics for Pseudoperonospora cubensis, the Cucurbit Downy Mildew Pathogen.

Authors:  S Withers; E Gongora-Castillo; D Gent; A Thomas; P S Ojiambo; L M Quesada-Ocampo
Journal:  Phytopathology       Date:  2016-06-17       Impact factor: 4.025

10.  Improved protocols for functional analysis in the pathogenic fungus Aspergillus flavus.

Authors:  Zhu-Mei He; Michael S Price; Gregory R Obrian; D Ryan Georgianna; Gary A Payne
Journal:  BMC Microbiol       Date:  2007-11-26       Impact factor: 3.605

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  1 in total

1.  Real-time automatic detection of starch particles in ambient air.

Authors:  Branko Šikoparija; Predrag Matavulj; Gordan Mimić; Matt Smith; Łukasz Grewling; Zorica Podraščanin
Journal:  Agric For Meteorol       Date:  2022-08-15       Impact factor: 6.424

  1 in total

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