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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Disease and Pest Management Research Unit » Research » Research Project #437092

Research Project: Nematode Community Assessment as Part of Defining Potato Soil Health

Location: Horticultural Crops Disease and Pest Management Research Unit

Project Number: 2072-22000-046-007-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Aug 16, 2019
End Date: Oct 31, 2023

To utilize nematode community analysis to characterize potato soil health.

Sample collection and nematode extraction: We will focus on obtaining samples from Oregon/Washington, Idaho, and Wisconsin. A total of 60 samples will be considered for nematode community analysis, 20 each from each region. This will allow for the inclusion of 20 distinct sampling locations from several fields in each region. Samples will be split into two equal parts and the nematode community extracted. One extraction will be used for metabarcoding analysis while the other will be preserved for morphological analysis. Nematode community analysis: The total number of nematodes in a sample will be counted under a stereo microscope. The sample will then be mounted on a glass slide and the first 100 nematodes encountered on a slide will be identified to family or genus level under a compound microscope. Any plant-parasitic nematodes encountered will be identified to genus/species. Nematodes will be assigned to trophic groups to allow for comparison of the densities and proportional representations of different groups of nematodes. Data analysis: Diversity and ecological indexes will be calculated using data from the morphological and metabarcoding analyses including richness, Shannon and Simpson’s diversity indexes, Bongers Maturity Index, and related indices including enrichment and structure indices and metabolic footprint. These indexes will then be combined with the SCRI data (i.e., microbiome, pathogen assessments, physiochemical characteristics, and crop assessments). Correlation and regression of data as well as constrained linear canonical ordination using CANOCO will be used to compare morphological to metabarcode data.