Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites

Cheese making and vegetable processing are trillion-dollar industries globally. However, they generate immense volumes of high nitrogen wastewater that must be processed safely and cost effectively. Land application systems are frequently used by rural medium and smaller processing facilities that lack ready access to wastewater resource recovery facilities. This study utilized soil microbial data to determine system differences leading to high denitrification rates observed in incubation studies in agricultural soil collected from University of Wisconsin Agricultural Research Stations (ARS), Arlington and Lancaster stations, compared to industry cheese making and vegetable processing land application water treatment facilities. It was hypothesized that decade long frequent treatment with facility wastewater would alter the microbial communities in the system soils, but this is not the case. No clear correlations were found between soil denitrification rates and biotic or abiotic system factors and the microbial communities observed in the industry systems are similar to the ARS soils under agricultural production and to literature reported denitrifying systems such as wetlands and wastewater resource recovery facilities. Knowing that land application system management does not alter the microbial biome will allow any management advances that increase denitrification efficiency in other denitrifying systems to be readily applied to industry wastewater land application facilities. 

Methods
Basal and glucose-induced soil respiration was measured as an indicator of general soil microbial activity and biomass, respectively. Respiration was measured by trapping carbon dioxide released in a 24-hour period incubation following rewetting of an air-dried sample, followed by another 24-hour period after glucose addition (Batterman et al., 2022). An ANOVA comparing soil sources (industry versus ARS) was conducted using JMP to evaluate statistical variability basal and induced respiration.

PLFA analysis was conducted by the University of Missouri Soil Health Assessment Center following Byron and Sasser (2012) using an Agilent 6890 gas chromatograph (GC) (Agilent Technologies, Wilmington, DE, USA) controlled with MIS Sherlock® (MIDI, Inc., Newark, DE, USA) and Agilent ChemStation software. Fatty acid methyl esters were identified using the MIDI PLFAD1 calibration mix and naming table. The PLFA data were analyzed for species richness, Shannon diversity, fungal to bacterial ratio, Gram+ to Gram- bacteria ratio, and stress ratio(cy17:0 + cy19:0) / (16:1ω7c + 18:1ω7c) (Guckert et al., 1986; Pennanen et al., 1996). Fungi are represented by 18:2ω6 and bacteria the sum all the bacterial categories provided by MIDI (G+, G-, methanotrophs, anaerobes, and Actinobacteria) (Frostegård & Bååth, 1996).

Microbial communities were also assessed in each sample by amplicon sequencing of the 16S ribosomal RNA gene for bacteria and archaea and a portion of the internal transcribed spacer (ITS) region for fungi at the University of Minnesota Genomics Center. DNA was extracted with the Powersoil Pro DNA extraction kit (Qiagen, Germantown, MD, USA) and sequencing was performed on an Illumina MiSeq (Illumina, Madison, WI, USA) using 2 x 300 bp chemistry as described in (Gohl et al., 2016). The total number of sequences obtained for the 16S gene and ITS region were 3,650,076 and 2,481,678, respectively. Forward and reverse reads were denoised and merged using DADA2 (Callahan et al., 2016). For the ITS gene, merging the reads resulted in a loss of more than half sequences due to lower quality scores on the reverse reads so we proceeded with the forward reads only. The resulting sequences were clustered at 99% sequence identity into operational taxonomic units (OTUs) via open reference clustering using the Silva v. 138 (bacterial) and Unite v. 27.10.2022 (fungal) databases with vsearch in the QIIME2 platform (Bolyen et al., 2019; Nilsson et al., 2019; Quast et al., 2013; Rognes et al., 2016). The OTUs were exported into R and further analysis included use of the phyloseq, vegan, lme4, car, emmeans, and ggplot2 packages (Bates et al., 2015; Fox & Weisberg, 2019; Lenth, 2021; McMurdie & Holmes, 2013; Oksanen et al., 2020; R Core Team, 2021; Wickham, 2016).