PREDICTING STRIPE RUST DISEASE SEVERITY IN WHEAT USING METEOROLOGICAL DATA WITH ENVIRONMENTAL RESPONSE MODELING
khalifa, E., oraby, W., saleem, M., hussein, L. (2024). PREDICTING STRIPE RUST DISEASE SEVERITY IN WHEAT USING METEOROLOGICAL DATA WITH ENVIRONMENTAL RESPONSE MODELING. EKB Journal Management System, 9(2), 115-131. doi: 10.21608/mjapam.2024.269626.1034
elsaid khalifa; walid oraby; mohamed saleem; lamiaa hussein. "PREDICTING STRIPE RUST DISEASE SEVERITY IN WHEAT USING METEOROLOGICAL DATA WITH ENVIRONMENTAL RESPONSE MODELING". EKB Journal Management System, 9, 2, 2024, 115-131. doi: 10.21608/mjapam.2024.269626.1034
khalifa, E., oraby, W., saleem, M., hussein, L. (2024). 'PREDICTING STRIPE RUST DISEASE SEVERITY IN WHEAT USING METEOROLOGICAL DATA WITH ENVIRONMENTAL RESPONSE MODELING', EKB Journal Management System, 9(2), pp. 115-131. doi: 10.21608/mjapam.2024.269626.1034
khalifa, E., oraby, W., saleem, M., hussein, L. PREDICTING STRIPE RUST DISEASE SEVERITY IN WHEAT USING METEOROLOGICAL DATA WITH ENVIRONMENTAL RESPONSE MODELING. EKB Journal Management System, 2024; 9(2): 115-131. doi: 10.21608/mjapam.2024.269626.1034

PREDICTING STRIPE RUST DISEASE SEVERITY IN WHEAT USING METEOROLOGICAL DATA WITH ENVIRONMENTAL RESPONSE MODELING

Menoufia Journal of Plant Protection
Article 8, Volume 9, Issue 2, February 2024, Page 115-131  XML PDF (1.51 MB)
Document Type: Original Article
DOI: 10.21608/mjapam.2024.269626.1034
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Authors
elsaid khalifa1; walid oraby2; mohamed saleem1; lamiaa hussein email 1
1كلية الزراعة جامعة المنوفية
2معهد بحوث الصحراء
Abstract
: Eleven wheat cultivars were evaluated against yellow rust disease at two different locations in Egypt i.e. Shibin El-Kom, Minufiya governorate and Itay El-Baroud Agricultural Research Station, Behira governorate, Egypt during three successive growing seasons i.e. 2018/2019, 2019/2020 and 2020/2021. Correlation between weekly eight environmental factors; solar radiation, total precipitation, average wind speed, maximum wind speed, minimum air temperature, maximum air temperature, minimum relative humidity and maximum relative humidity to yellow rust severity (%) was determined using step wise regression analysis. Predictive models for each tested variety to final yellow rust severity disease (%) using environmental data were determined. In general, maximum relative humidity was positively correlated with final yellow rust severity (%). Significant correlation between two environmental factors maximum relative humidity and final yellow rust severity (%) was found in this study. Meanwhile, negative correlation was found between final yellow rust severity (%) and the two environmental factors i.e. solar radiation and minimum relative humidity. High values of R2 for all regression models under study referred to the validation of these models to predict yellow rust severity (%) and facilitate the use of each as a warning forecast to wheat farmers
Keywords
Wheat; yellow rust; environmental factors; prediction models
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