16S metabarcoding, total soil DNA content, and functional bacterial genes quantification to characterize soils under long-term organic and conventional farming systems

Abstract

Abstract Background The threatening impact of conventional agriculture (CA) on soils could be due to the detrimental effects on soil microbial communities. Conversely, organic agriculture (OA) is envisaged as potentially enhancing helpful microbial communities and is proposed as environmentally sustainable. The soil microbiome influences soil health and quality, hence, it requires deeper investigation and understanding. In this study, applying 16S metabarcoding and qPCR techniques, we compared the microbial patterns of long-term organically and conventionally managed soils to explore their similarities and differences. Results Total DNA quantification showed an over 20-fold higher amount of DNA in OA soils (mean = 22.1 ± 3.92 μg g−1), compared to CA soils (mean = 0.95 ± 0.17 μg g−1). While 16S metabarcoding evidenced the absence of significant differences among communities of the two farming systems in terms of ecological indices, the qPCR analyses targeting functional genes reported a significantly higher abundance of all considered targets in OA sites spanning up to four-fold log increases. While OA and CA did not appear to affect overall bacterial diversity or evenness per se, qPCR-based functional analysis in OA showed a consistently higher abundance of all the salient microbial genes tested, when compared to CA, underlying a potentially beneficial impact on soil fertility and sustainability. Conclusions In essence, the sequencing-based analysis of absolute bacterial diversity could not differentiate the farming systems based on the amount of diversity but identified a unique set of taxa defining each. Hence, pairing this evaluation with the qPCR-based functional gene analyses can be a suitable approach to distinguish the exerted effects of CA or OA on soils. Graphical Abstract

Keywords

• Organic agriculture
• Conventional agriculture
• Sustainable agriculture
• 16S-metabarcoding
• Multi-amplicon sequencing
• Functional genes