Scalable Agricultural Microbiome Sampling: Operational Definitions, Pooling Strategies, and Preservation Methods
- 1Departamento de Microbiologia, Facultad de Ciencias, Campus Universitario de Teatinos s/n, Universidad de Malaga, 29010 Malaga, Spain
Abstract
Scalable soil microbiome monitoring requires sampling methods that are reproducible across operators, field sites, and logistical constraints. Here, we evaluated three key methodological choices that commonly limit comparability in agricultural rhizosphere studies: how the rhizosphere sampling unit is operationally defined, sample pooling strategies, and preservation methods. We introduce the RhizoCore, a standardized root-zone soil core defined by core diameter, depth, position relative to the plant, and subsample volume, as a practical proxy for traditional rhizosphere sampling. The RhizoCore method captured more than 92% of the sequencing depth found in traditional rhizosphere samples, with differences limited predominantly to low-abundance taxa. Preservation methods significantly affected bacterial communities, while sample pooling showed greater impact on fungal diversity and substantially reduced within-group variability across all treatments. Despite these effects, differential abundance analysis revealed minimal compositional changes, with only a small fraction of microbial taxa significantly affected by either pooling or preservation method. Our findings demonstrate that the RhizoCore method provides a reproducible, and scalable approach for rhizosphere sampling that balances scientific rigor with practical field implementation, offering a framework for large-scale soil microbiome monitoring programs and for improving comparability among agricultural microbiome studies across diverse environmental conditions.
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BibTeX
@misc{ossowicki2026scalable,
title = {{Scalable Agricultural Microbiome Sampling: Operational Definitions, Pooling Strategies, and Preservation Methods}},
author = {A. Ossowicki and T. Griffioen and E. Mileti and V. Attanasi and C. Hames and V. J. Carrion and B. Oyserman},
howpublished = {bioRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.19.725853},
url = {https://www.biorxiv.org/content/10.64898/2026.05.19.725853v3},
note = {CC BY},
}
RIS
TY - UNPB
TI - Scalable Agricultural Microbiome Sampling: Operational Definitions, Pooling Strategies, and Preservation Methods
AU - A. Ossowicki
AU - T. Griffioen
AU - E. Mileti
AU - V. Attanasi
AU - C. Hames
AU - V. J. Carrion
AU - B. Oyserman
PY - 2026
DA - 2026/05/19
PB - bioRxiv
DO - 10.64898/2026.05.19.725853
UR - https://www.biorxiv.org/content/10.64898/2026.05.19.725853v3
AB - Scalable soil microbiome monitoring requires sampling methods that are reproducible across operators, field sites, and logistical constraints. Here, we evaluated three key methodological choices that commonly limit comparability in agricultural rhizosphere studies: how the rhizosphere sampling unit is operationally defined, sample pooling strategies, and preservation methods. We introduce the RhizoCore, a standardized root-zone soil core defined by core diameter, depth, position relative to the plant, and subsample volume, as a practical proxy for traditional rhizosphere sampling. The RhizoCore method captured more than 92% of the sequencing depth found in traditional rhizosphere samples, with differences limited predominantly to low-abundance taxa. Preservation methods significantly affected bacterial communities, while sample pooling showed greater impact on fungal diversity and substantially reduced within-group variability across all treatments. Despite these effects, differential abundance analysis revealed minimal compositional changes, with only a small fraction of microbial taxa significantly affected by either pooling or preservation method. Our findings demonstrate that the RhizoCore method provides a reproducible, and scalable approach for rhizosphere sampling that balances scientific rigor with practical field implementation, offering a framework for large-scale soil microbiome monitoring programs and for improving comparability among agricultural microbiome studies across diverse environmental conditions.
ER -