CAN HETERODERA SCHACHTII SUPPRESSION BE PREDICTED BY INDIGENOUS POPULATIONS OF FUNGI BELONGING TO THE HYALORBILIA OVIPARASITICA CLADE?

Our long-term goal is to create safe, cost-effective, and sustainable management strategies to reduce crop damage caused by plant-parasitic cyst nematodes (Heterodera spp.). The family Heteroderidae is one of the most damaging groups of obligate pathogens of economically important crops worldwide. In the US and Canada, H. glycines, H. schachtii, and H. avenae are closely related cyst nematodes of primary importance. Earlier, we examined indigenous populations of nematophagous fungi, the Hyalorbilia oviparasitica clade (anamorph: Dactylella oviparasitica), in field soils cropped to sugarbeets in the Imperial Valley of California. After two nematode generations, soils with detectable levels of these fungi substantially suppressed the population densities of the sugarbeet cyst nematode (H. schachtii). In addition, the extent of nematode suppression was not correlated to the initial population densities of these fungi. To the best of our knowledge, this is the first study showing that the presence of indigenous biological control microorganisms could be used to accurately predict whether the population densities of a plant pathogen would be suppressed.The objective of this seed-grant project:Determining whether the presence of indigenous populations of fungi belonging to the H. oviparasitica clade can be used to predict H. schachtii population suppression in soils cropped to broccoli along California's Central Coast.In contrast to our previous study, we will examine a different geographical and climatic region where other species of H. schachtii-susceptible crops (primarily cole crops) are commonly grown. Suppose we get a similar result with this small-scale study, where we can accurately predict whether H. schachtii will be suppressed based on the presence of these fungi. In that case, we anticipate that we will be in a solid position to secure funding for a larger-scale study that thoroughly examines additional essential variables.