Investigation of the efficacy of epiphytic bacteria and yeasts in the biological control of Erwinia amylovora

Fire blight, caused by Erwinia amylovora, a Gram negative bacterium of the Enterobacteriaceae family, is a devastating disease of Rosaceae family. The most severe infections of the pathogen usually appear on blossoms. Copper based compounds are phytotoxic during blooming stage and usage of antibiotic are restricted in Turkey, that's why, new approaches are in need for an effective control of disease at this stage. Thus, antagonistic efficacy of epiphytic bacteria and yeasts isolated from healthy blossoms of apple, pear and quince plants were tested in vitro, semi in vivo and in vivo conditions. A total of 564 presumptive epiphytic bacteria and yeasts were isolated from apple, pear and quince blossoms/leaves collected from five districts in Turkey with protected natural flora in 2014 and 2015. According to in vitro tests, 92 antagonistic bacteria and seven antagonistic yeasts inhibited pathogen growth. Since the pathogen inhibition on immature pear fruits ranged from 11% to 78% under semi in vivo, disease has suppressed among 8-58% on flowering shoots. Two promising antagonists (KAD3 and PBAL10) were tested under apple orchard conditions. Two commercial microorganisms Aureobasidium pullulans DSM 14940 and DSM 14941 (Blossom Protect, Astranova) and Bacillus subtilis QST 713 (Serenede, Bayer) and Herkul (%20 Maneb + %37.5 Bakır Oksiklorür) were included as controls. Antagonists and commercial products were reduced disease development from 86% to-99% in apple blossoms of cv. Gala variety. PBAL10 and KAD3 encoded antagonists were more effective than Aureobasidium pullulans yeast and Streptomycin antibiotics, suppressing the disease by 90% and 89%. In Golden Delicious variety, the rate of disease suppression was from 77% to 98%. PBAL10 and KAD3 encoded antagonists were more effective than Aureobasidium pullulans yeast, Herkul fugucide and Streptomycin antibiotics, suppressing the disease by 95% and 91%. Local antagonists and other applications have proven to be effective by taking different statistical groups than positive controls.