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Influence of Temperature and Inoculum Density of Fusarium oxysporum f. sp. ciceris on Suppression of Fusarium Wilt of Chickpea by Rhizosphere Bacteria

First, second, and third authors: Departamento de Protección de Cultivos, Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Apdo. 4084, 14080 Córdoba, Spain; and fourth author: Departamento de Protección de Cultivos, IASCSIC, and Escuela Técnica Superior de Ingenieros Agrónomos y de Montes, Universidad de Córdoba, Apartado 3048, 14080 Córdoba, Spain

The effects of temperature and inoculum density of Fusarium oxysporum f. sp. ciceris race 5 on suppression of Fusarium wilt in chickpea (Cicer arietinum) cv. PV 61 by seed and soil treatments with rhizobacteria isolated from the chickpea rhizosphere were studied in a model system. Disease development over a range of temperatures (20, 25, and 30°C) and inoculum densities (25 to 1,000 chlamydospores per gram of soil) was described by the Gompertz model. The Gompertz relative rate of disease progress and final amount of disease increased exponentially and monomolecularly, respectively, with increasing inoculum densities. Disease development was greater at 25°C compared with 20 and 30°C. At 20 and 30°C, disease development was greater at 250 to 1,000 chlamydospores per gram of soil compared with 25 to 100 chlamydospores per gram of soil. At 25°C, increasing inoculum densities of the pathogen did not influence disease. Nineteen Bacillus, Paenibacillus, Pseudomonas, and Stenotrophomonas spp. out of 23 bacterial isolates tested inhibited F. oxysporum f. sp. ciceris in vitro. Pseudomonas fluorescens RGAF 19 and RG 26, which did not inhibit the pathogen, showed the greatest Fusarium wilt suppression. Disease was suppressed only at 20 or 30°C and at inoculum densities below 250 chlamydospores per gram of soil. Bacterial treatments increased the time to initial symptoms, reduced the Gompertz relative rate of disease progress, and reduced the overall amount of disease developed.

Additional keywords: biocontrol agents , disease modeling .