International Journal of Biometeorology

, Volume 62, Issue 4, pp 655–668 | Cite as

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

  • K. N. Neufeld
  • A. P. Keinath
  • B. K. 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
Original Paper


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.


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



This work was supported by grants from the United States Department of Agriculture-National Institute of Food and Agriculture (USDA-NIFA) OREI Award 2012-51300-20006 and USDA-NIFA RIPM Awards 2012-34103-19622 and 2012-41530-19623.


  1. Accadia C, Mariani S, Casaioli M, Lavaqnini A, Speranza A (2005) Verification of precipitation forecasts from two limited-area models over Italy and comparison with ECMWF forecasts using a resampling technique. Weather Forecast 20(3):276–300. CrossRefGoogle Scholar
  2. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232. CrossRefGoogle Scholar
  3. Arauz LF, Neufeld KN, Lloyd AL, Ojiambo PS (2010) Quantitative models for germination and infection of Pseudoperonospora cubensis in response to temperature and duration of leaf wetness. Phytopathology 100(9):959–967. CrossRefGoogle Scholar
  4. Aylor DE (1999) Biophysical scaling and the passive dispersal of fungus spores: relationship to integrated pest management strategies. Agric For Meteorol 97(4):275–292. CrossRefGoogle Scholar
  5. Aylor DE (2003) Spread of plant disease on a continental scale: role of aerial dispersal of pathogens. Ecology 84(8):1989–1997. CrossRefGoogle Scholar
  6. Choudhury RA, Koike ST, Fox AD, Anchieta A, Subbarao KV, Klosterman SJ, McRoberts N (2016) Season-long dynamics of spinach downy mildew determined by spore trapping and disease incidence. Phytopathology 106(11):1311–1318. CrossRefGoogle Scholar
  7. Cohen Y (1977) The combined effects of temperature, leaf wetness, and inoculum concentration on infection of cucumbers with Pseudoperonospora cubensis. Can J Bot 55(11):1478–1487. CrossRefGoogle Scholar
  8. Cohen Y, Rotem J (1969) The effects of lesion development, air temperature, and duration of moist period on sporulation of Pseudoperonospora cubensis in cucumbers. Israel J Bot 18:135–140Google Scholar
  9. Doran JC, Fast JD, Barnard JC, Laskin A, Desyaterik Y, Gilles MK, Hopkins RJ (2008) Applications of Lagrangian dispersion modeling to the analysis of changes in the specific absorption of elemental carbon. Atmos Chem Phys 8(5):1377–1389. CrossRefGoogle Scholar
  10. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49. CrossRefGoogle Scholar
  11. Gent DH, Nelson ME, Farnsworth JL, Grove GG (2009) PCR detection of Pseudoperonospora humuli in air samples from hop yards. Plant Pathol 58(6):1081–1091. CrossRefGoogle Scholar
  12. Gent DH, Mahaffee WF, McRoberts N, Pfender WF (2013) The use and role of predictive systems in disease management. Annu Rev Phytopathol 51(1):267–289. CrossRefGoogle Scholar
  13. Granke LL, Morrice JJ, Hausbeck MK (2014) Relationships between airborne Pseudoperonospora cubensis sporangia, environmental conditions, and cucumber downy mildew severity. Plant Dis 98(5):674–681. CrossRefGoogle Scholar
  14. He Z, Price MS, O’Brian GR, Georgianna DR, Payne GA (2007) Improved protocols for the functional analysis in the pathogenic fungus Aspergillus flavus. BMC Microbiol 7(1):104. CrossRefGoogle Scholar
  15. Holmes GJ, Ojiambo PS, Hausbeck MK, Quesada-Ocampo L, Keinath AP (2015) Resurgence of cucurbit downy mildew in the United States: a watershed event for research and extension. Plant Dis 99(4):428–441. CrossRefGoogle Scholar
  16. Isard SA, Barnes CW, Hambleton S, Ariatti A, Russo JM, Tenuta A, Gay DA, Szabo LJ (2011) Predicting soybean rust incursions into the North American continental interior using crop monitoring, spore trapping, and aerobiological modeling. Plant Dis 95(11):1346–1357. CrossRefGoogle Scholar
  17. Isard SA, Gage SH, Comtois P, Russo JM (2005) Principles of the atmospheric pathway for invasive species applied to soybean rust. Bioscience 55(10):851–861.[0851:POTAPF]2.0.CO;2 CrossRefGoogle Scholar
  18. Kanetis L, Holmes GJ, Ojiambo PS (2010) Survival of Pseudoperonospora cubensis sporangia exposed to solar radiation. Plant Pathol 59(2):313–323. CrossRefGoogle Scholar
  19. Kirchner JW (2006) Getting the right answers for the right reasons: linking measurements, analyses, and models to advance the science of hydrology. Water Resour Res 42:W03S04CrossRefGoogle Scholar
  20. Kobayashi N, Tamura K, Aotsuka T (1999) PCR error and molecular population genetics. Biochem Genet 37(9/10):317–321. CrossRefGoogle Scholar
  21. Kretzer AM, Molina R, Spatafora JW (2000) Microsatellite markers for the ectomycorrhizal basidiomycete Rhizopogon vinicolor. Mol Ecol 9(8):1190–1191. CrossRefGoogle Scholar
  22. Lebeda A, Cohen Y (2011) Cucurbit downy mildew (Pseudoperonospora cubensis) - biology, ecology, epidemiology, host-pathogen interaction and control. Eur J Plant Pathol 129(2):157–192Google Scholar
  23. Luster DG, McMahon MB, Edwards HH, Boerma BL, Ivey MLL, Miller SA, Dorrance AE (2012) Novel Phakopsora pachyrhizi extracellular proteins are ideal targets for immunological diagnostic assays. Appl Environ Microbiol 78(11):3890–3895. CrossRefGoogle Scholar
  24. Main CE, Keever T, Holmes GJ, Davis JM (2001) Forecasting long-range transport of downy mildew spores and plant disease epidemics. APSnet Features.
  25. McRoberts N, Hall C, Madden LV, Hughes G (2011) Perceptions of disease rick: from social construction of subjective judgements to rational decision making. Phytopathology 101(6):654–665. CrossRefGoogle Scholar
  26. Monserud RA, Leemans R (1992) Comparing global vegetation maps with the kappa statistic. Ecol Model 62(4):275–293. CrossRefGoogle Scholar
  27. Neufeld KN, Isard SA, Ojiambo PS (2013) Relationship between disease severity and escape of Pseudoperonospora cubensis sporangia from a cucumber canopy during downy mildew epidemics. Plant Pathol 62(6):1366–1377. CrossRefGoogle Scholar
  28. Neufeld KN, Ojiambo PS (2012) Interactive effects of temperature and leaf wetness duration on sporangia germination and infection of cucurbit hosts by Pseudoperonospora cubensis. Plant Dis 96(3):345–353. CrossRefGoogle Scholar
  29. Ojiambo PS, Gent DH, Quesada-Ocampo LM, Hausbeck MK, Holmes GJ (2015) Epidemiology and population biology of Pseudoperonospora cubensis: a model system for management of downy mildews. Annu Rev Phytopathol 53(1):223–246. CrossRefGoogle Scholar
  30. Ojiambo PS, Holmes GJ, Britton W, Keever T, Adams ML, Babadoost M, Bost SC, Boyles R, Brooks M, Damicone J, Draper MA, Egel DS, Everts KL, Ferrin DM, Gevens AJ, Gugino BK, Hausbeck MK, Ingram DM, Isakeit T, Keinath AP, Koike ST, Langston D, McGrath MT, Miller SA, Mulrooney R, Rideout S, Roddy E, Seebold KW, Sikora EJ, Thornton A, Wick RL, Wyenandt CA, Zhang S (2011) Cucurbit downy mildew ipmPIPE: a next generation web-based interactive tool for disease management and extension outreach. Plant Health Progress Online publication.
  31. Ojiambo PS, Holmes GJ (2011) Spatio-temporal spread of cucurbit downy mildew in the eastern United States. Phytopathology 101(4):451–461. CrossRefGoogle Scholar
  32. Ojiambo PS, Kang EL (2013) Modeling spatial frailties in survival analysis of cucurbit downy mildew epidemics. Phytopathology 103(3):216–227. CrossRefGoogle Scholar
  33. Palmer TN, Shutts GJ, Hagedorn R, Doblas-Reyes FJ, Jung T, Leutbecher M (2005) Representing model uncertainty in weather and climate prediction. Annu Rev Earth Planet Sci 33(1):163–193. CrossRefGoogle Scholar
  34. Roberts MJ, Schimmelpfennig D, Ashley E, Livingston M (2006) The value of plant disease early-warning systems: A case study of USDA’s soybean rust coordinated framework. Online. Econ. Res. Rep. No. 18. USDA-ERS, Washington, DCGoogle Scholar
  35. Stohl A, Hittenberger M, Wotawa G (1998) Validation of the Lagrangian particle dispersion model Flexpart against large-scale tracer experiment data. Atmos Environ 32(24):4245–4264. CrossRefGoogle Scholar
  36. Tao Z, Malvick D, Claybrooke R, Floyd C, Bernacchi CJ, Spoden G, Kurle J, Gay D, Bowersox V, Krupa S (2009) Predicting the risk of soybean rust in Minnesota based on an integrated atmospheric model. Int J Biometeorol 53(6):509–521. CrossRefGoogle Scholar
  37. Tatineni S, Sagaram US, Gowda S, Robertson CJ, Dawson WO, Iwanami T, Wang N (2008) In planta distribution of ‘Candidatus Liberibacter asiaticus’ as revealed by polymerase chain reaction (PCR) and real-time PCR. Phytopathology 98(5):592–599. CrossRefGoogle Scholar
  38. Thomas A, Carbone I, Choe K, Quesada-Ocampo L, Ojiambo PS (2017) Resurgence of cucurbit downy mildew in the United States: insights from comparative genomic analysis of Pseudoperonospora cubensis. Ecol Evol 2017 00(16):1–16. Google Scholar
  39. Viljanen-Rollinson SLH, Parr EL, Marroni MV (2007) Monitoring long-distance spore dispersal by wind—a review. N Z Plant Prot 60:291–296Google Scholar
  40. Vittal R, Haudenshield JS, Hartman GL (2012) A multiplexed immunofluorescence method identifies Phakopsora pachyrhizi urediniospores and determines their viability. Phytopathology 102(12):1143–1152. CrossRefGoogle Scholar
  41. Withers S, Gongora-Castillo E, Gent D, Thomas A, Ojiambo PS, Quesada-Ocampo LM (2016) Using next-generation sequencing to develop molecular diagnostics for Pseudoperonospora cubensis, the cucurbit downy mildew pathogen. Phytopathology 106(10):1105–1116. CrossRefGoogle Scholar

Copyright information

© ISB 2017

Authors and Affiliations

  • K. N. Neufeld
    • 1
  • A. P. Keinath
    • 2
  • B. K. Gugino
    • 3
  • M. T. McGrath
    • 4
  • E. J. Sikora
    • 5
  • S. A. Miller
    • 6
  • M. L. Ivey
    • 6
    • 7
  • D. B. Langston
    • 8
  • B. Dutta
    • 9
  • T. Keever
    • 1
  • A. Sims
    • 10
  • P. S. Ojiambo
    • 1
  1. 1.Center for Integrated Fungal Research, Department of Entomology and Plant PathologyNorth Carolina State UniversityRaleighUSA
  2. 2.Department of Plant and Environmental SciencesClemson UniversityCharlestonUSA
  3. 3.Department of Plant Pathology and Environmental MicrobiologyPennsylvania State UniversityUniversity ParkUSA
  4. 4.Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant ScienceCornell UniversityRiverheadUSA
  5. 5.Department of Entomology and Plant PathologyAuburn UniversityAuburnUSA
  6. 6.Department of Plant PathologyOhio State UniversityWoosterUSA
  7. 7.Department of Plant Pathology and Crop PhysiologyLouisiana State UniversityBaton RougeUSA
  8. 8.Tidewater Agricultural Research and Extension Center, Department of Plant Pathology, Physiology and Weed ScienceVirginia Polytechnic Institute and State UniversitySuffolkUSA
  9. 9.Department of Plant PathologyUniversity of GeorgiaTiftonUSA
  10. 10.State Climate Office of North CarolinaNorth Carolina State UniversityRaleighUSA

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