Volume 19 Part 1 Article 18
Title: Mushroom disease detection, surveillance and farm health
Author: Werner Rossouw, Stacey Duvenage & Lise Korsten
Mushrooms are susceptible to various diseases of which the causal agent may be bacterial, fungal or viral. Four main mycoparasitic fungal diseases are of economic importance on white button mushrooms and include; compost green mould (Trichoderma aggressivum), dry bubble (Lecanicillium fungicola), cobweb (Cladobotryum dendroides) and wet bubble (Mycogone perniciosa). A series of test methods have been developed, optimized and used in the industry to detect the presence of these fungal pathogens prior to symptom expression. The use of standard microbiological methods, advanced identification techniques and molecular methods provides a diagnostic platform for accurate and reliable monitoring as well as disease diagnosis. Molecular methods currently include conventional PCR and real-time PCR. This paper explores the possibility of using a quantitative PCR (qPCR) method in the form of droplet digital PCR (ddPCR) for more accurate detection of these pathogens. Once optimized, quantification using ddPCR is possible without the reliance on a DNA quantitative standard, as required by other qPCR methods. Commercial mushroom farms in South Africa were monitored for mycoparasitic fungal disease presence over a period of four years, using conventional and real-time PCR. Samples (n = 912) were collected from three areas on each farm (n = 13) i.e. composting area, preparation area and production area, presence or absence where determined using the novel ddPCR, with the future vision of pathogenic load quantification. Analysis showed that Trichoderma spp. and Cladobotryum spp. are the most frequently detected pathogens in all three production areas. Isolation trends per annum for all four pathogens show an increased pathogen/disease presence in the months of January to March and again from May to July as well as November to December, with fluctuations throughout the rest of the year. The development and application of the described detection methods have proven to be an invaluable tool in assisting farmers to control disease more effectively through an early warning system.