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Updated: Oct 7, 2026

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
Published on: May 2, 2018
Metagenomic Tools as Predictive Approaches to Decipher Soil and Rhizosphere Microbial Communities and Their Functions
Oluwaseun Emmanuel Shittu1, Jing Ge2, Olubukola Oluranti Babalola1,3
1Soil-Plant-Microbe Interactions Group Faculty of Natural and Agricultural Science North-West University Mmabatho South Africa nwu.ac.za.
Abstract:
The increasing demand for resilient and sustainable food production necessitates a comprehensive understanding of rhizosphere microbial communities and their functional roles in enhancing plant health, nutrient acquisition, stress resilience, and crop productivity, thereby supporting food security and sustainable agriculture. However, soil microbiomes are composed of complex and unculturable communities of beneficial microorganisms that drive critical ecosystem functions, which traditional culture methods cannot capture. This limits the understanding of their roles and impacts on plant health and agricultural sustainability. Metagenomic tools have emerged as transformative approaches for characterizing microbial diversity in soil. Therefore, this review presents an overview of how metagenomic techniques can be harnessed to predict microbial community structures and their associated functional potentials, ultimately contributing to the development of resilient farming strategies. Next-generation sequencing and bioinformatics enable metagenomics to analyze microbial genetic material in soil and rhizospheres. This study provides insights into microbial diversity and profiles of key functional genes linked to nutrient availability and soil functions. We discuss the use of metagenomics tools to predict soil health and microbial functions for sustainable agroecosystems, emphasizing predictive models that help farmers optimize yields and minimize environmental impact. This review explores how metagenomics tools in agriculture can serve as a predictive model to transform our understanding of soil-microbe interactions. This advancement can facilitate the formulation of novel, more resilient, productive, and sustainable agricultural systems.
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