Molecular characterization epidemiology and management of Bacterial Blight of Soybean Pseudomonas Savastanoi Pv glycinea

Abstract

Soybean (Glycine max L. Merill) is one of the oldest and most important oilseed crop newlinegrown throughout the world. Bacterial blight of soybean caused by Pseudomonas savastanoi pv newlineglycinea is one of the major constraints in successful cultivation of soybean causing up to 40 per newlinecent yield losses in different areas of world. During the course of field surveys from 2019 to newline2021, the disease was found to be prevalent in different districts of Uttarakhand with highest newlineincidence in Udham Singh Nagar, Pithoragarh and Champavat. The bacterial isolates from newlinedifferent regions were found to slightly differ in morphology with round colonies of diameter 1 to newline4 mm, white to creamish white in colour, raised and flat elevation with filamentous and entire newlinemargins. The bacterial cells were seen as rod-shaped with rounded ends in TEM photographs, newlinewith size of 0.84 and#956;m - 1.04 and#956;m x 0.35 - 0.43 and#956;m. On the basis of biochemical tests, the bacterium newlinewas characterized as genus Pseudomonas. Specific markers used in the study were found useful newlinein detecting presence of pathogen. Sequence analysis of 21 Psg isolates yielded a similarity of newline93.02 to 99.33 per cent with Pseudomonas savastanoi in NCBI. The disease initiation was newlinenoticed during last week of July to first week of August. The highest and lowest values of PDI, newlineinfection rate and AUDPC were noticed in variety Shilajeet and PK-472, respectively. Highest newlineinfection rate was observed from 33 to 36th SMW. The correlation with maximum and minimum newlinetemperature and evening RH was found to be negative, while rainfall and sunshine hour show newlinepositive correlation with the disease. The multiple regression analysis yielded R2 values of 0.91 to newline0.93, indicating greater than 90 per cent involvement of weather factors in affecting variation of newlinedisease severity. Minimum temperature and evening RH were found to be the most contributing newlinefactor to disease development with the help of step-wise regression. Gompertz and Logistic newlinemodels were found to predict most accurate values of disease severity. Fifteen gen