Soil Bacterial Communities from Three Agricultural Production Systems in Rural Landscapes of Palmira, Colombia

SIMPLE SUMMARY: Three production systems with different management strategies were evaluated for soil bacterial diversity. Soil samples were taken from agroecological, organic, and conventional agricultural production systems, and physicochemical and metabarcoding analyses were carried out to evaluate if bacterial diversity was directly influenced by the management type. Differences in soil bacterial communities were not found to depend directly on the management, likely due to the non-aggressive practices used in all three systems. Rather, the differences can be attributed mainly to the use of different fertilizers rich in nitrogen and phosphorus and the use of pesticides. ABSTRACT: Soils play important roles in the proper functioning of agroecosystems. Using molecular characterization methods such as metabarcoding, soils from eight farms (57 samples) belonging to three production system types—agroecological (two farms with twenty-two sampling points), organic (three farms with twenty-one sampling points), and conventional (three farms with fourteen sampling points)—were compared from the rural villages of El Arenillo and El Mesón in Palmira, Colombia. Amplification and sequencing of the hypervariable V4 region of the 16S rRNA gene was performed using next-generation sequencing (Illumina MiSeq) to estimate the bacterial composition and the alpha and beta diversity present. Across all soil samples, we found 2 domains (Archaea and Bacteria), 56 phylum, 190 classes, 386 orders, 632 families, and 1101 genera to be present. The most abundant phyla in the three systems were Proteobacteria, (agroecological 28%, organic 30%, and conventional 27%), Acidobacteria (agroecological 22%, organic 21%, and conventional 24%), and Verrucomicrobia (agroecological 10%, organic 6%, and conventional 13%). We found 41 nitrogen-fixing and phosphate-dissolving genera which promote growth and pathogens. Alpha and beta diversity indices were very similar across the three agricultural production systems, as reflected by shared amplicon sequence variants (ASVs) among them, likely due to the proximity of the sampling sites and recent management changes.