DOI: 10.5061/dryad.gqnk98sw1
title: Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites

description: 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. 
description: 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<sup>®</sup> (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).
description: # **Arlington and Lancaster ARS, cheese maker and vegetable processor land
 application soil microbiome dataset**
 [https://doi.org/10.5061/dryad.gqnk98sw1](https://doi.org/10.5061/dryad.gqnk98sw1) This dataset contains the raw data from soil 16S ribosomal RNA, phospholipid fatty acid (PLFA) and soil microbial respiration analysis of samples collected from the University of Wisconsin Arlington and Lancaster Agricultural Research Stations, the Klondike (Cheese 1) and Maple Leaf (Cheese 2) cheese making ridge and furrow wastewater disposal fields, and the Seneca Cambria (Vegetable 1) and Del Monte Plover (Vegetable 2) vegetable processing wastewater disposal spray fields. **Description of the data and file structure** 1.     File list: a.     Wastewater_Soil_Respiration_Data.xlsx b.     Wastewater_Land_application_study_PLFA_raw_data.xlsx c.     Wastewater_Land_Application_study_soil_PLFA_summary_data.xlsx d.     Wastewater_land_application_bacterial_data.xlsx e.     Univ_of_Missouri_Initial_Exploratory_PLFA_Data_Analysis.doc f.      Abiotic_and_Summary_Microbe_data.xlsx 2.     Relationship between files: None 3.     Additional related data collected that was not included in current data package: no 4.     Are there multiple versions of the dataset? No Data-specific information for: Wastewater_Soil_Respiration_Data.xlsx Number of variables (columns): 3 (respiration data) Number of Rows: 33 (samples) This file contains raw data (3x33) as well as calculated averages of soil respiration and glucose induced respiration.   Data-specific information for: Wastewater_Land_application_study_PLFA_raw_data.xlsx Number of variables (columns): 77 (individual PLFA data) Number of Rows: 30 (samples) This file contains the raw PLFA data on seven tabs. The columns are either individual fatty acids or summary of fatty acids indicative of different microbe types (fungi, actinomycetes, anaerobe, etc….). Full explanation of this data can be found in Initial Exploratory PLFA Data Analysis.   Data-specific information for: Wastewater_Land_Application_study_soil_PLFA_summary_data.xlsx Number of variables (columns): 7 (microbial group) Number of rows: 30 (samples) This file contains ANOVA analysis of the microbial groups in pmol/g dry soil.   Data-specific information for: Wastewater_land_application_bacterial_data.xlsx Number of variables (columns): 30 (samples) Number of rows: 9381 (mRNA channels) This file contains two tables. One of mRNA data summed by phyla. One the entire mRNA analysis channels.   Data-specific information for: Univ_of_Missouri_Initial_Exploratory_PLFA_Data_Analysis.doc This file is provided by the University of Missouri Soil Analysis laboratory to assist researchers in analysis of PLFA datasets.   Data-specific information for: Abiotic and Summary Microbe data.xlsx Number and type of Columns: 39 Number and type of Rows: 30 This table contains soil system abiotic factor data (ARS and industry), incubation study % denitrification loss, and summary soil respiration, PLFA and 16S RNA data. This data was used to calculate correlations between data sets.   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).