Method for estimating inoculum of the soilborne fungal pathogen Verticillium dahliae in Australian cotton soils

Author:

Young S.ORCID,Kirkby K.ORCID,Roser S.,Harden S.ORCID

Abstract

The ability to quantify inoculum levels of the soilborne fungal pathogen Verticillium dahliae in field soil is essential for understanding potential disease pressure of Verticillium wilt in cotton and for making informed management decisions. Several semi-selective media and techniques for pathogen isolation have been developed for determining the inoculum levels of V. dahliae present in soil. The objective of this study was to gather data on soil plating techniques, media, sampling depths, and times of sampling for the detection and quantification of V. dahliae in field soils, in order to develop a V. dahliae isolation protocol for Australian cotton-growing soils. Two soil plating techniques (dilution plating or ‘wet plating’, and direct spreading by hand or ‘dry plating’) on four semi-selective media (Sorenson’s NP-10, potato dextrose agar, and acidified versions of each) were compared for their efficacy in quantifying soil inoculum as germinated microsclerotia propagules per gram soil. Soil was sampled from three depths to examine the vertical distribution of the fungus and so determine the ideal sampling depth. Field soil sampling was conducted pre-planting and post-harvest to examine differences in inoculum with sampling time. Based on the results of this study, a soil sampling protocol has been developed for Australian cotton farms that includes sampling soil before planting, at a depth of 2–24 cm, and using the direct dry plating method on Sorenson’s NP-10 media.

Publisher

CSIRO Publishing

Subject

Plant Science,Agronomy and Crop Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Biotic and abiotic responses to soilborne pathogens and environmental predictors of soil health;Soil Biology and Biochemistry;2024-02

2. Design and implementation of smart agricultural soil acquisition system based on FPGA;Third International Conference on Computer Vision and Data Mining (ICCVDM 2022);2023-02-03

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