Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Genetic Improvement for Fruits & Vegetables Laboratory » Research » Publications at this Location » Publication #323660

Title: Dissecting the blueberry soil microbiome to assess soil health

item Polashock, James
item OUDEMANS, PETER - Rutgers University

Submitted to: Acta Horticulturae
Publication Type: Proceedings
Publication Acceptance Date: 2/2/2016
Publication Date: 10/1/2017
Publication URL:
Citation: Polashock, J.J., Oudemans, P. 2017. Dissecting the blueberry soil microbiome to assess soil health. Acta Horticulturae. 1180:405-408.

Interpretive Summary: Soils contain a lot of microorganisms. The type and quantity of microorganisms present can be an indicator of soil health and a predictor of how well crop plants might perform in a given soil. We collected blueberry soils that were determined to be poor for plant growth and some that were found to be good for plant growth. The microorganisms in all soils collected were determined and compared to obtain clues as to which microorganisms are associated with each soil type (good or bad). This is a first step in developing methods for soil improvement. The results will ultimately benefit farmers.

Technical Abstract: The plant rhizosphere is made up of not only soil, but a myriad of living organisms; these living organisms can both contribute to, and be indicators of, soil health. We explored the possibility of assaying the soil microbiome in areas where blueberry fields are declining, as compared to healthy fields in an effort to extract clues as to what might be causing the decline. To assay the microbiome, soil samples were collected from representative fields and total DNA was extracted. The DNA was amplified using 16S primers for bacteria and ITS primers for fungi. Primers were also designed and tested for nematodes. Then resulting amplicons were purified and used to prepare DNA libraries by following the Illumina TruSeq DNA library preparation protocol. Sequencing was performed on the MiSeq next-generation sequencing platform. Sequences were joined and depleted of barcodes. Sequences that failed quality control parameters were deleted. Operational taxonomic units (OTUs) were defined by clustering at 3% divergence (97% similarity). Final OTUs were taxonomically classified using BLASTn against a curated database derived from GreenGenes, RDPII and NCBI. These data are being compared to see if there is a biological ‘signature’ associated with poor soils. The ultimate goal is to use these data to develop plans for remediation using, for example, cover crops. However, it is likely that the problem varies from location to location and customized plans will need to be implemented.