Jiarong Yang, and
First, second, third, fourth, fifth, seventh, and eighth authors: State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China; and sixth author: Institute of Cotton Research of Chinese Academy of Agricultural Sciences/State Key Laboratory of Cotton Biology, Anyang 455100, China.
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Accepted for publication 29 July 2014.
Quantification of Verticillium dahliae microsclerotia is an important component of wilt management on a range of crops. Estimation of microsclerotia by dry or wet sieving and plating of soil samples on semiselective medium is a commonly used technique but this method is resource-intensive. We developed a new molecular quantification method based on Synergy Brands (SYBR) Green real-time quantitative polymerase chain reaction of wet-sieving samples (wet-sieving qPCR). This method can detect V. dahliae microsclerotia as low as 0.5 CFU g−1 of soil. There was a high correlation (r = 0.98) between the estimates of conventional plating analysis and the new wet-sieving qPCR method for 40 soil samples. To estimate the inoculum threshold for cotton wilt, >400 soil samples were taken from the rhizosphere of individual plants with or without visual wilt symptoms in experimental and commercial cotton fields at the boll-forming stage. Wilt inoculum was estimated using the wet-sieving qPCR method and related to wilt development. The estimated inoculum threshold varied with cultivar, ranging from 4.0 and 7.0 CFU g−1 of soil for susceptible and resistant cultivars, respectively. In addition, there was an overall relationship of wilt incidence with inoculum density across 31 commercial fields where a single composite soil sample was taken at each field, with an estimated inoculum threshold of 11 CFU g−1 of soil. These results suggest that wilt risk can be predicted from the estimated soil inoculum density using the new wet-sieving qPCR method. We recommend the use of 4.0 and 7.0 CFU g−1 as an inoculum threshold on susceptible and resistant cultivars, respectively, in practical risk prediction schemes.
disease severity, proportional odds model.
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