Geno-pheno characterization of crop rhizospheres: an integrated Raman spectroscopy and microbiome approach in conventional and organic agriculture
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Abstract
Agricultural management practices strongly influence soil microbiomes, with broad implications for ecosystem function. Yet, the combined phenotypic and compositional dynamics of rhizosphere microbial communities across conventional and organic farming systems remain poorly characterized, underscoring the need for integrated approaches to understand how management decisions drive microbial assembly and function.
Methods
We investigated microbial communities associated with conventionally and organically cultivated horticultural crops across multiple farms in New York State. To capture both taxonomic and functional dimensions, community composition was characterized using 16S rRNA gene sequencing, and phenotypic traits were assessed with a newly developed single-cell Raman microspectroscopy (SCRS) approach. This dual strategy allowed us to link microbial identity with metabolic potential and adaptive traits.
Results
Farming practice significantly shaped microbiome clustering, independent of site or plant species. SCRS-based phenotyping revealed distinct biochemical profiles: organic systems favored lipid-accumulating phenotypes linked to energy storage and stress resilience, whereas conventional systems promoted carbon-rich phenotypes associated with rapid assimilation and biomass production. Network analysis identified Pseudomonas and nitrogen-fixing taxa as ecological hubs in conventional systems, while organic soils were enriched in Bacilli class plant growth-promoting rhizobacteria (e.g., Tumebacillus, Bacillus, Paenibacillus, Brevibacillus ) and contained microorganisms bearing antibiotic resistance genes.
Discussion
Our findings highlighted that management regimes drive distinct microbial functional traits and community structures. By integrating genotypic and phenotypic analyses, particularly microbial phenotyping via SCRS, we uncovered adaptive traits that differentiate conventional and organic systems, offering new insight into how plant production practices shape microbial assembly and ecological function.
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To717evaluate the relationship between taxonomic and phenotypic alpha diversity metrics, we performed718both linear and log-transformed linear regression analyses between ASV- and OPU-derived richness,719Shannon, and evenness values. Ordinary least squares (OLS) regression models were fitted for each720treatment using the statsmodels Python package (v0.14.1) (105). In the log-transformed models, both721the independent and dependent variables were transformed using the natural logarithm of one plus722the value (log1p) to accommodate zero values and improve numerical stability using Python Numpy723(v2.2.4) (106). For each model, the coefficient of determination (R²) and corresponding P value were724extracted to assess the strength and significance of the relationship.
It would be nice to have a more comprehensive analysis of the …
To717evaluate the relationship between taxonomic and phenotypic alpha diversity metrics, we performed718both linear and log-transformed linear regression analyses between ASV- and OPU-derived richness,719Shannon, and evenness values. Ordinary least squares (OLS) regression models were fitted for each720treatment using the statsmodels Python package (v0.14.1) (105). In the log-transformed models, both721the independent and dependent variables were transformed using the natural logarithm of one plus722the value (log1p) to accommodate zero values and improve numerical stability using Python Numpy723(v2.2.4) (106). For each model, the coefficient of determination (R²) and corresponding P value were724extracted to assess the strength and significance of the relationship.
It would be nice to have a more comprehensive analysis of the relationship between OPU and ASV since there may be many drivers of correlation between OPU and ASV, prevalence of species being one, but also, you might have differing environmental factors diving correlation in OPU that deviates from ASV. If you could examine the correlation between OPU sets or features and environmental factors (such as organic\non-organic, or plant type) after controlling for ASV it might more directly identify aspects of biology that are driven to be similar based on growth conditions and not different species presence.
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