Examination and characterization of key factors to seasonal epidemics of downy mildew in native grape species Vitis davidii in southern China with the use of disease warning systems

  • T. Yi
  • Y. Hong
  • M. Li
  • X. LiEmail author


Spine grape (Vitis davidii) is a major native grape species cultivated in south central China, and the industry is threatened by downy mildew outbreaks caused by Plasmopara viticola in this rainy and warm region. To examine and characterize the local epidemics of this disease in this region, experiments were conducted in Jingzhou in 2012–2013 with variety ‘Gaoshan-2’ and Zhongfang in 2013–2016 with variety ‘Xiangzhenzhu’ in Hunan Province. The number of diseased leaves with new infections (DL_Sum) and disease severity were assessed every 5 days. First, we used a mechanistic simulation model originally developed in Europe to simulate the primary inoculum seasons in these years and found that they ended in early May, suggesting little influences from oospores to the disease in summer. Then we used negative binomial regression to model the temporal changes of DL_Sum in summer. The predictive variables included weather variables and two disease risk indices derived based on two other empirical disease warning systems developed in Europe and North America. Relative humidity (RH) was identified the best predictive variable among all variables. Ordinal logistic regression identified mean temperature and days of heavy rainfall (>7 mm) as key weather factors for disease development. In general, RH was more important for infection process, and temperatures and rainfall were more important for symptom development and spore dispersal. Canopy structures could also have significant effects on disease development. These results may help for the development of new disease warning or forecast systems more suitable for this disease in southern China.


Negative binomial regression Ordinal logistic regression Plasmopara viticola Simulation model Wild grape 



We are grateful for local staff for the assistance in field survey. We also greatly appreciate Dr. X.F. Niu at the University of Missouri.


This study was funded by the Coordinated Research Program for Downy Mildew by the National Agro-tech Extension & Service Center (NAESC, Project No. 201203035) and the Shen-Long Visiting Scholar Program at Hunan Agricultural University.

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest to declare.

Human and animal studies

The study did not have human participants or animals involved.


  1. Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: John Wiley & Sons Inc..CrossRefGoogle Scholar
  2. Agrios, G. N. (2005). Plant Pathology (5ed.). Burlington, MA: Elsevier Academic Press.Google Scholar
  3. American Meteorological Society (2016). "Rain". Glossary of Meteorology. [Available online at]. (2nd ed., Vol. 2016).
  4. Blaeser, M., & Weltzien, H. C. (1979). Epidemiological studies to improve the control of grapevine downy mildew (Plasmopara viticola). Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz, 86, 489–498.Google Scholar
  5. Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261–304.CrossRefGoogle Scholar
  6. Caffi, T., Gilardi, G., Monchiero, M., & Rossi, V. (2013). Production and release of asexual sporangia in Plasmopara viticola. Phytopathology, 103, 64–73.CrossRefGoogle Scholar
  7. Caffi, T., Legler, S. E., González-Domínguez, E., & Rossi, V. (2016). Effect of temperature and wetness duration on infection by Plasmopara viticola and on post-inoculation efficacy of copper. European Journal of Plant Pathology, 144, 737–750.CrossRefGoogle Scholar
  8. Caffi, T., Rossi, V., Bugiani, R., Spanna, F., Flamini, L., Cossu, A., et al. (2009). A model predicting primary infections of Plasmopara viticola in different grapevine-growing areas of Italy. Journal of Plant Pathology, 91, 535–548.Google Scholar
  9. Caffi, T., Rossi, V., Carisse, O. (2011). Evaluation of a dynamic model for primary infections caused by Plasmopara viticola on grapevine in Quebec. Plant Health Progress, Online Publication,, 12, 22.
  10. Carisse, O. (2015). Development of grape downy mildew (Plasmopara viticola) under northern viticulture conditions: Influence of fall disease incidence. Eur. J. of Plant Pathol., 144(4), 773–783.CrossRefGoogle Scholar
  11. Coakley, S. M., Line, R. F., & McDaniel, L. R. (1988). Predicting stripe rust severity on winter wheat using an improved method for analyzing meteorological and rust data. Phytopathology, 78, 543–550.CrossRefGoogle Scholar
  12. Dalla Marta, A., De Vincenzi, M., Dietrich, S., & Orlandini, S. (2005). Neural network for the estimation of leaf wetness duration: Application to a Plasmopara viticola infection forecasting. Physics and Chemistry of the Earth, 30(1), 91–96.CrossRefGoogle Scholar
  13. Du, F., Deng, W., Yang, M., Wang, H. J., Mao, R., Shao, J., et al. (2015). Protecting grapevines from rainfall in rainy conditions reduces disease severity and enhances profitability. Crop Protection, 67, 261–268.CrossRefGoogle Scholar
  14. Gessler, C., Pertot, I., & Perazzolli, M. (2011). Plasmopara viticola: A review of knowledge on downy mildew of grapevine and effective disease management. Phytopathologia Mediterranea, 50(1), 3–44.Google Scholar
  15. Hosmer, D., Sturdivant, R., & Lemeshow, S. (2013). Applied logistic regression (3rd ed.). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  16. Kennelly, M. M., Gadoury, D. M., Wilcox, W. F., Magarey, P. A., & Seem, R. C. (2007). Primary infection, lesion productivity, and survival of sporangia in the grapevine downy mildew pathogen Plasmopara viticola. Phytopathology, 97(4), 512–522.CrossRefGoogle Scholar
  17. Lalancette, N., Ellis, M. A., & Madden, L. V. (1988a). Development of an infection efficiency model for Plasmopara viticola on American grape based on temperature and duration of leaf wetness. Phytopathology, 78(6), 794–800.CrossRefGoogle Scholar
  18. Lalancette, N., Madden, L. V., & Ellis, M. A. (1988b). A quantitative model for describing the sporulation of Plasmopara viticola on grape leaves. Phytopathology, 78(10), 1316–1321.CrossRefGoogle Scholar
  19. Li, C. (1998). Flora of China (Vol. 48(2)). Beijing. China: Science Press.Google Scholar
  20. Li, X., Wang, L., Chen, D., Yang, K., Xue, B., & Sun, L. (2013). Near-surface air temperature lapse rates in the mainland China during 1962-2011. J. of Geophys. Res.: Atmospheres, 118, 7505–7515.Google Scholar
  21. Liu, C., Jiang, J., Fan, X., & Zhang, Y. (2014). The utilization of Chinese wild grape species in production and breeding. Journal of Plant Genetic Resources, 15(4), 720–727.Google Scholar
  22. Madden, L. V., Ellis, M. A., Lalancette, N., Hughes, G., & Wilson, L. L. (2000). Evaluation of a disease warning system for downy mildew of grapes. Plant Disease, 84(5), 549–554.CrossRefGoogle Scholar
  23. Madden, L. V., Hughes, G., & van den Bosch, F. (2007). The Study of Plant Disease Epidemics. St. Paul, MN: The American Phytopathological Society, APS Press.Google Scholar
  24. Marta, A. D., Magarey, R. D., & Orlandini, S. (2005). Modelling leaf wetness duration and downy mildew simulation on grapevine in Italy. Agricult. and Forest Meteorol., 132, 84–95.CrossRefGoogle Scholar
  25. Orlandini, S., Massetti, L., & Dalla Marta, A. (2008). An agrometeorological approach for the simulation of Plasmopara viticola. Computers and Electronics in Agriculture, 64(2), 149–161.CrossRefGoogle Scholar
  26. Park, E. W., Seem, R. C., Gadoury, D. M., & Pearson, R. C. (1997). DMCAST: A prediction model for grape downy mildew development. Viticulture Enology Science, 52, 182–189.Google Scholar
  27. Pellegrini, A., Prodorutti, D., Frizzi, A., Gessler, C., & Pertot, I. (2010). Development and evaluation of a warming model for the optimal use of copper in organic viticulture. Journal of Plant Pathology, 92(1), 43–55.Google Scholar
  28. Reis, E. M., Sônego, O. R., & Mendes, C. d. S. (2013). Application and validation of a warning system for grapevine downy mildew control using fungicides. Summa Phytopathologica, 39(1), 10–15.CrossRefGoogle Scholar
  29. Rossi, V., & Caffi, T. (2012). The role of rain in dispersal of the primary inoculum of Plasmopara viticola. Phytopathology, 102(2), 158–165.CrossRefGoogle Scholar
  30. Rossi, V., Caffi, T., Giosuè, S., & Bugiani, R. (2008). A mechanistic model simulating primary infections of downy mildew in grapevine. Ecological Modelling, 212(3), 480–491.CrossRefGoogle Scholar
  31. Rossi, V., Salinari, F., Poni, S., Caffi, T., & Bettati, T. (2014). Addressing the implementation problem in agricultural decision support systems: The example of®. Computers and Electronics in Agriculture, 100, 88–99.
  32. Sentelhas, P., Dalla Marta, A., Orlandini, S., Santos, E., Gillespie, T., & Gleason, M. (2008). Suitability of relative humidity as an estimator of leaf wetness duration. Agricultural and Forest Meteorology, 148, 392–400.CrossRefGoogle Scholar
  33. Tran Manh Sung, C., Strizyk, S., & Clerjeau, M. (1990). Simulation of the date of maturity of Plasmopara viticola oospores to predict the severity of primary infections in grapevine. Plant Disease, 74(2), 120–124.CrossRefGoogle Scholar
  34. Wan, Y., Schwaninger, H., He, P., & Wang, Y. (2007). Comparison of resistance to powdery mildew and downy mildew in Chinese wild grapes. VITIS-Journal of Grapevine Research, 46(3), 132–136.Google Scholar
  35. Wan, Y., Schwaninger, H., Li, D., Simon, C. J., Wang, Y., & Zhang, C. (2008). A review of taxonomic research on Chinese wild grapes. VITIS-Journal of Grapevine Research, 47(2), 81–88.Google Scholar
  36. Wilcox, W. F., Gubler, W. D., & Uyemoto, J. K. (2015). Compendium of Grape Diseases, Disorders, and Pests (2nd ed.). St. Paul, MN: American Phytopathological Society.Google Scholar

Copyright information

© Koninklijke Nederlandse Planteziektenkundige Vereniging 2019

Authors and Affiliations

  1. 1.College of Plant Protection & Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Plant PestsHunan Agricultural UniversityChangshaChina
  2. 2.Huaihua Agrometeorology Experimental StationHuaihuaChina
  3. 3.Department of Plant Pathology and MicrobiologyIowa State UniversityAmesUSA

Personalised recommendations