Application of logistic regression model for predicting the association of climate change resilient cultural practices with early blight of tomato (Alternaria solani) epidemics in the East Shewa, Central Ethiopia

Abstract

A field survey was conducted to determine the distribution and importance of early blight of tomato in East Shewa, Central Ethiopia. A total of 140 tomato fields were inspected in 4 districts (Adama, Lome, Dugda, and Bora). The associations of early blight incidence and severity with independent variables were evaluated. Disease incidence was found higher in Dugda (72.19%) and Bora (62.28%) districts. The highest mean disease severity was 31.39% in Dugda and 26.09% in Bora district, while the lowest was recorded in Adama (14.71%) district. In reduced multiple variable models, early blight percentage severity index >25% showed a high probability of association with all parameters. Logistic regression analyses of disease severity revealed that sometraits were found the most significant variables. Overall, proper weed management practices, crop rotation with non-solanaceous hosts, and other related farm practices should be carried out to reduce the impact of early blight on tomatoes.

Keywords

• alternaria solani
• disease severity
• solanum lycopersicum
• weed density