Risk zonation mapping of Fusarium Head Blight disease of wheat using Fuzzy and GIS model in Golestan province
Subject Areas : Plant Pests
Hanieh
Naderi
1
(M.Sc. Student, Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Mirmasoud
Kheirkhah Zarkesh
2
(Natural Resources and Environment Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Masoud
Goodarzi
3
(Soil Conservation and Watershed Management Research Institute, Agriculture Research, Education and Extension Organization, Tehran, Iran)
Keywords: Geospatial information system (GIS), Fuzzy logic method, Fusarium Head Blight) FHB),
Abstract :
The purpose of this study was to provide a zoning map of susceptible areas to Fusarium head blight disease of wheat ear; the obtained model was compared by fuzzy logic method with the zonation map obtained from reports of Plant Protection Research stations in Golestan province. For this purpose, the average of humidity, temperature and precipitation parameters were determined for 45 days in autumn wheat growth period in the stage 65 of the Zadox scale. According to the opinion of plant pathologists and the time of occurrence of Fusarium head blight in Golestan province, preparation of zoning maps was determined through early-April to mid-May period in three days intervals. A total of 15 zoning maps were created and susceptible areas to disease were identified in this model. Zoning was classified into four categories: safe (0-25%), low risk (26-50%), hazard (51-75%) and high risk (76-100%).Validation of the results was admitted by Kappa coefficient method. The results showed that if the spikes were in the susceptible growth stage, the incidence and development of the disease would be predictable from early-April to early-May in the determined areas with a validation of 76%. The symptoms of the disease are not recognizable in the spikes during the mentioned period. The results of this study can be helpful for researcher and related experts in forecasting the disease and decision making of disease management at the best time.
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