Application of the Skybit model to forecast the brown spot, frogeye and powdery mildew diseases in tobacco based on weather data
List of Authors
  • Nguyen Van Chin

Keyword
  • Pests, disease, tobacco, forecast, and forecast model

Abstract
  • In recent years, Vietnam Tobacco Institute applied the effection of the Skybit model in forecasting insects and diseases in tobacco-growing regions. Input data of the Skybit model is weather information (Temperature, Relative humidity, leaf wetness), the biology of pest diseases, and others such as cultivation, variety, rotation, historical field, counting. The result of the forecast model with Brown spot (Alternaria alternata), Frogeye (Cercospora nicotianae), and Powdery mildew (Erysiphe cichoracearum) is suitable with the development of pests on the tobacco field and decide the proper management. It helps to reduce the use of pesticides and increase the yield and quality of tobacco. To raise the quality of the forecast, this model needs to continue improvement next time.

Reference
  • 1. NJ Deshmukh, CD Deokar and SB Deshmukh. (2019). Effect of plant age and duration of leaf wetness on pea powdery mildew development. Journal of Pharmacognosy and Phytochemistry 2019; 8(1): 1230-1232. E-ISSN: 2278-4136 P-ISSN: 2349-8234 JPP 2019; 8(1): 1230-1232.

    2. Fangfang Li, Wei Liu, Na Sun, Fennglong Wang, and Lili Shen. (2016). Study of a Cercospora nicotinae strain in tobacco in China. Journal of Modern Agriculture. January 2016, Volume 5, Issue 1,PP.1 -7.

    3. József Fodor, Evelin Kámán-Tóth, Tamás Dankó, Ildikó Schwarczinger, Zoltán Bozsó and Miklós Pogány. (2017). Description of the Nicotiana benthamiana-Cercospora nicotianae Pathosystem. Phytopathology 108(1). DOI:10.1094/PHYTO-12-16-0448-R. https://doi.org/10.1094/PHYTO-12-16-0448-R.

    4. Gleason, Mark L.; Elwynn Taylor, S.; Villalobos, Roberto; Arauz, Luis Felipe; Soo Kim, Kwang. (2001). Assessment of the accuracy of site-specific estimates of rainfall, air temperature, relative humidity, and wetness duration in the Northern Pacific region of Costa Rica Agronomía Costarricense, vol. 25, núm. 2, julio-diciembre, 2001, pp. 45-55. ISSN: 0377-9424. http://www.redalyc.org/articulo.oa?id=43625204.

    5. Ghobakhlou, F. Amir1, J. Whalley and P. Sallis. (2015). Estimation of Leaf Wetness Duration Using Adaptive Neuro-Fuzzy Inference Systems. 21st International Congress on Modelling and Simulation, Gold Coast, Australia, 29 Nov to 4 Dec 2015

    www.mssanz.org.au/modsim 2015.

    6. Kim, K. S., Taylor, S. E., Gleason, M. L., and Koehler, K. J. (2002). Model to enhance site-specific estimation of leaf wetness duration. Plant Dis. 86:179-185. Publication no. D-2001-1217-02S. Published Online:23 Feb 2007. https://doi.org/10.1094/PDIS.2002.86.2.179.

    7. E. KOCMÁNKOVÁ, M. TRNKA, J. E ITZ INGER, M. DUBROV SKÝ, P. ŠTEˇ PÁNEK, D. SEMERÁDOVÁ, J. BALEK, P. SKALÁK, A. FARDA, J. JUROCH AND Z. ŽALUD (2011). Estimating the impact of climate change on the occurrence of selected pests at a high spatial resolution: a novel approach. Journal of Agricultural Science (2011), 149, 185–195. © Cambridge University Press 2011 doi:10.1017/S0021859610001140. Https://www.researchgate.net/publication/231890084.

    8. C.E. Main. (1971). Pathogenesis and Halo Formation of the Tobacco Brown lesion. Phytophathology. Vol. 61: 1437-1443. DOI: 10.1094/Phyto-61-1437.

    9. Magarey, R. D., Sutton, T. B., and Thayer, C. L. (2005). A simple generic infection model for foliar fungal plant pathogens. Phytopathology 95:92-100. DOI: 10.1094/PHYTO-95-0092. https://doi.org/10.1094/PHYTO-95-0092.

    10. Matthew Wallhead, Heping Zhu. (2017). Decision Support Systems for Plant Disease and Insect Management in Commercial Nurseries in the Midwest: A Perspective Review. Journal of Environmental Horticulture (2017) 35 (2): 84–92. https://doi.org/10.24266/0738-2898-35.2.84.

    11. Rabiu Olatinwo, GerritHoogenboom. (2014). Chapter 4 - Weather-based Pest Forecasting for Efficient Crop Protection. Integrated Pest Management. Current Concepts and Ecological Perspective 2014, Pages 59-78. https://doi.org/10.1016/B978-0-12-398529-3.00005-1.

    12. Tracy Rowlandson, Mark Gleason, Paulo Sentelhas, Terry Gillespie, Carla Thomas, and Brian Hornbuckle. (2015). Reconsidering leaf wetness duration determination for plant diseaase management. Plant Disease / Vol. 99 No. 3. 2015 The American Phytopathological Society. http://dx.doi.org/10.1094/ PDIS-05-14-0529-FE.

    13. J.R. Stavely and L.J. Slana. (1970). Relation of leaf age to the reaction of tobacco to Alternaria alternatta. Phytopathology 61: 73-78. DOI: 10.1094/Phyto-61-73.

    14. Sentelhas, P. C., Dalla Marta, A., Orlandini, S., Santos, E. A., Gillespie, T. J., & Gleason, M. L. (2008). Suitability of relative humidity as an estimator of leaf wetness duration. Agricultural and forest meteorology, 148(3), 392-400.

    15. Vidita Tilva, Jignesh Patel, and Chetan Bhatt. (2013). Weather based plant diseases forecasting using fuzzy logic. Published in: 2013 Nirma University International Conference on Engineering (NUiCONE). INSPEC Accession Number: 14199142. Print ISSN: 2375-1282. DOI: 10.1109/NUiCONE.2013.6780173.

    16. M. B. Uloth M. P, You M, J. Barbetti. (2017). Plant age and ambient temperature: significant drivers for powdery mildew (Erysiphe cruciferarum) epidemics on oilseed rape (Brassica napus). Plant Pathology (2018) 67, 445–456. Doi: 10.1111/ppa.12740. https://doi.org/10.1111/ppa.12740.

    17. WILKS D.S., SHEN K.W. (1991). Threshold relative humidity duration forecasts for plant disease prediction. J. Appl. Meteorol. 30:463-477. DOI: https://doi.org/10.1175/1520-0450(1991)030<0463:TRHDFF>2.0.CO;2.