ABSTRACT Title of Thesis: INVESTIGATING THE EFFECT OF MANAGEMENT ON AGRICULTURAL ORGANIC NITROGEN CYCLING AND ALTERNATIVE NITRIFICATION PATHWAYS Alyssa Nichole Wellman Houde, Master of Science, 2021 Thesis directed by: Professor Stephanie A. Yarwood Department of Environmental Science and Technology This project studied the influence of agricultural management strategies (such as tillage and fertilizer choice) on nitrogen (N) cycling pathways. Soil samples and leachate samples from a series of experimental plots at the Wye Research and Education Center were analyzed using a combination of traditional chemical N measures (DON, PMN, NOx, NH3, TN, and microbial biomass C & N) and novel mass spectrometry techniques (FT-ICR-MS) to characterize shifts in organic matter composition and quantity over time and under three different cover cropping regimes. Analysis of these samples indicate that the DOM (dissolved organic matter) composition of leachate changed significantly with increasing sampling depth. However further research is needed to fully investigate the potential impacts of cover cropping and time on soil and leached DOM and DON. Soil samples were also collected at the Farming Systems Project in Beltsville, MD and at several of the University of Minnesota?s Outreach farms. These samples were analyzed for their abundance of 16S, nirK, nirS, nxrA, and amoA AOB to characterize the nitrifying and denitrifying microbial communities under a combination of management strategies. While the findings were not significant, they indicate that fertilization and tillage may have an impact on the nitrification and denitrification communities. INVESTIGATING THE EFFECT OF MANAGEMENT ON AGRICULTURAL ORGANIC NITROGEN CYCLING AND ALTERNATIVE NITRIFICATION PATHWAYS By Alyssa Nichole Wellman Houde Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Master of Science 2021 Advisory Committee: Professor Stephanie Yarwood, Chair Assistant Professor Nicole Fiorellino Dr. Michel Cavigelli ?Copyright by Alyssa Nichole Wellman Houde 2021 Acknowledgements: This work was funded by the University of Maryland, College Park?s Department of Environmental Science and Technology with collaboration with the USDA and Maryland Agricultural Experiment Station. I would like to sincerely thank my advisor Dr. Stephanie Yarwood and the members of my committee (Dr. Michel Cavigelli and Dr. Nicole Fiorellino) for their help and expertise. I would also like to thank the members of the Yarwood and Baldwin lab for all of their support over the years, Stanley Schlosnagle for his lab assistance, Dr. Malak Tfaily for her assistance with FT-ICR-MS analysis, our collaborators at the USDA, Dr. Rodney Venterea, and our other collaborators at the University of Minnesota. ii Table of Contents Chapter I ? Introduction and Literature Review??????????????????.1 References ? Ch. I?????????????????????????.....10 Chapter II ???????????????????????????????.?14 Title: DOM and DON composition shifts with sampling depth and management strategies in agricultural fields ????????????????????.?..14 Author Names and Affiliation?????????????????????..14 Abstract?????????????????????????????......14 Introduction????????????????????????????...15 Experimental Procedures???????????????????????.18 Sample Site and Collection????????????????????.18 Chemical Analysis???????????????????????...19 FT-ICR-MS Analysis??????????????????????...24 Data Analysis?????????????????????????..27 Results???????????????????????????????27 Leachate Chemical Analysis???????????????????...27 Soil Chemical Analysis?????????????????????...32 Leachate DOM????????????????????????....32 Leachate DON?????????????????????????38 Soil DOM??????????????????????????....46 Soil DON???????????????????????????.55 Discussion??????????????????????.????..??...63 Leachate DOM and DON Composition???????????.????.63 Soil DOM and DON Composition????????????????.?.65 Cover Cropping?????????????????????.???.66 Conclusion???????????????????????????.??68 References ? Ch. II????????????????????????.?..71 Chapter III???????????????????????????????....76 Title: Agricultural management strategies impact nitrification and denitrification microbial community composition???????????????????....76 Author Names and Affiliation???????????????.?????....76 Abstract?????????????????????????.?????76 Introduction???????????????????????????..?77 iii Experimental Procedures????????????????????????81 Sample Site and Collection?????????????????????81 Sample Preparation???????????????????????...82 QPCR?????????????????????????????.86 Data Analysis???????????????.???????????88 Results???????????????????????????????...89 Microbial Abundance???????????????????????.89 Nitrification Functional Genes???????????????????..90 Denitrification Functional Genes??????????????????..94 Overall Microbial Community Composition??????????????.97 Discussion?????????????????????????????...102 N2O Emissions?????????????????????????.102 Microbial Abundance?????????????????????..?102 Nitrification Functional Genes???????????????????103 Denitrification Functional Genes??????????????????105 Conclusion?????????????????????????????..107 References ? Ch. III?????????????????????????...110 Appendix?????????????????????????????????.115 References????????????????????????????????...126 iv List of Tables Chapter II Table 2.1. Biogeochemical Classification Ranges. Adapted from AminiTabriz et al, 2020, the were used to determine the biogeochemical classification of the DOM and DON compounds within the samples???????????????????????????.??.20 Table 2.2. Leachate Chemical Analysis. The sample number, sampling depth, sampling date and cover cropping strategy along with several traditional chemical N measures for each of the leachate samples???????????????????????????.???28 Table 2.3. Soil Chemical Analysis. The plot, sampling date, and cover cropping strategy along with several traditional chemical N measures for each of the soil samples???.????..29 Table 2.4. Leachate DOM Composition. The sample number, sampling date, cover cropping strategy, DOM FT-ICR-MS peaks (indicating the total number of unique compounds), the relative percentage of each compositional class (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amnio sugars, carbohydrates, lignin, tannins, and unclassifiable compounds), the average DBE/C ratio and the percentage of compounds with an AI> 0.67?34 Table 2.5 Leachate DOM Pearson Correlations. The results of Pearson Correlations comparing the sampling depth, the relative percent of highly photodegradable compounds (AI>0.67), the average DBE/C ratio, the amount of DON, TN, TC, NH4-N, and NO3-N, and the relative percentage of each compositional class (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amnio sugars, carbohydrates, lignin, tannins, and unclassifiable compounds) within the leachate DOM samples. Bolded values within the chart indicate statistically significant correlations (p<0.5)????????????????????35 Table 2.6. Leachate DON Composition. The sample number, sampling date, cover cropping strategy, DOM FT-ICR-MS peaks (indicating the total number of unique compounds), the relative percentage of each compositional class (lignin, condensed hydrocarbons, unclassifiable compounds, lipids, tannins, proteins, and amino sugars), the average DBE/C ratio and the percentage of compounds with an AI> 0.67????????????????????42 Table 2.7. Leachate DON Pearson Correlations. The results of Pearson Correlations comparing the sampling depth, the relative percent of N containing peaks, relative percent of highly photodegradable compounds (AI>0.67), the average DBE/C ratio, the amount of DON, TN, TC, NH4-N, and NO3-N, the relative percentage of each compositional class (tannins, lignin, amino sugars, proteins, condensed hydrocarbons, lipids) with the leached DON and the number of N peaks. Bolded values within the chart indicate statistically significant correlations (p<0.5)?...43 v Table 2.8. Soil DOM Composition. The sample number, sampling date, plot number, DOM FT- ICR-MS peaks (indicating the total number of unique compounds), extraction method (water or chloroform), cover cropping strategy, the relative percentage of each compositional class (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amino sugars, carbohydrates, lignin, tannins, and unclassifiable compounds), the average DBE/C ratio and the percentage of compounds with an AI> 0.67???????????????.?????????...47 Table 2.9. Soil Water DOM Pearson Correlations. The results of Pearson Correlations comparing the sulfur containing compounds, the relative percentage of each compositional class in the soil water extractions (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amino sugars, carbohydrates, lignin, tannins, and unknown compounds), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5)?????..??????????????48 Table 2.10. Soil Chloroform DOM Pearson Correlations. The results of Pearson Correlations comparing the sulfur containing compounds, the relative percentage of each compositional class in the soil chloroform extractions (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amino sugars, carbohydrates, lignin, tannins, and unknown compounds), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5)???????????????????..49 Table 2.11. Soil DON Composition. The sample number, sampling date, plot number, DOM FT-ICR-MS peaks (indicating the total number of unique compounds), extraction method (water or chloroform), cover cropping strategy, the relative percentage of each compositional class (lignin, condensed hydrocarbons, unclassifiable compounds, lipids, proteins, and amino sugars), the average DBE/ ration and the percentage of compounds with an AI> 0.67??????58 Table 2.12. Soil Water Pearson Correlations. The results of Pearson Correlations comparing the number of N peaks, the relative percentage of each compositional class (lignin, condensed hydrocarbons, lipids tannins, proteins, and amino sugars), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 extractable TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5)????????????????????????????...59 vi Table 2.13. Soil Chloroform DON Pearson Correlations. The results of Pearson Correlations comparing the number of N peaks, the relative percentage of each compositional class (lignin, condensed hydrocarbons, lipids tannins, proteins, and amino sugars), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 extractable TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5)????????????????????????.60 Chapter III Table 3.1. Maryland Study Site Information. Study site information about the Maryland sampling site. Includes: treatment, location, soil type, number of plots sampled, crop rotation, tillage strategy, weed control strategy, and fertilizer applied in 2019?????????..83 Table 3.2. Minnesota Study Site Information. Study site information about the Minnesota sampling sites. Includes: treatment, location, soil type, number of plots sampled, crop rotation, tillage strategy, weed control strategy, and fertilizer applied in 2019?????????...84 Table 3.3. Common Nitrification and Denitrification Functional Genes. A series of commonly studied functional genes associated with N cycling microbes. For each gene, the table lists the process associated with the gene, the enzyme the gene encodes, common microbes that harbor the gene, the corresponding primer set and sequence we used in this study, the amplicon size, the needed thermocycling conditions, and the source of information???????...85 Table 3.4. Functional Gene Abundance One-Way ANOVA Summary Table. Summary table of One-Way ANOVA values for 16S rRNA, nxrA, amoa AOB, nirS, and nirK gene abundance (average gene copy number per gram of wet soil). Bolded values indicate significant (p<0.05) differences????????????????????????????????..93 Table 3.5. Normalized Functional Gene Abundance and Gene Ratios. Summary table of One-Way ANOVA values for 16S rRNA normalized nxrA, amoa AOB, nirS, and nirK gene abundance as well as for the nxrA:amoA AOB and nirS:nirK ratios. Bolded values indicate significance (p<0.05)????????????????????????????.98 Table 3.6. Functional Gene Pearson Correlations. Pearson Correlations comparing the 16S rRNA, nxrA, amoa AOB, nirS, and nirK gene abundance (average gene copy number per gram of wet soil) as well as 16S rRNA normalized nxrA, amoa AOB, nirS, and nirK gene abundances. Bolded values indicate significance (p<0.05)???????????????????100 Table 3.7. Historical N2O Emissions in Minnesota Sample Sites. Historical N2O emission data for the Minnesota sample sites, under different fertilization regimes. Data adapted from (Breuillin- Sessoms et al., 2017)????????????????????????????..101 vii List of Figures Chapter I Figure 1.1. Diagram of Agricultural N-Cycling Dynamics. The figure illustrates various microbially mediated N transformations that occur in agricultural fields. Emphasis is placed on potential N2O leaks?????????????????????????????...6 Chapter II Figure 2.1. Soil Sample Collection and Preparation for Analysis. The soil sampling method and sample preparation for chemical analysis (water extractable NH4-N, NO3-N, TN & TC, KCl extractable NH4-N and NO3-N, K2SO4 extractable TN & TC, PMN, and Microbial Biomass C & N). Soil DON was calculated by subtracting the DIN from the total N concentration in the soil water extracts. PMN was calculated by determining the difference in NH4-N between the anaerobically incubated KCl extracts and fresh field moist soil samples. Soil microbial biomass C and N was determined by determining the average difference in C and N between the fumigated and fresh field moist K2SO4 extracts and correcting for extraction efficiency by dividing the value by 0.45. A portion of each composite soil sample was shipped to our collaborators in Arizona for FT-ICR-MS analysis???????????????.??.20 Figure 2.2. Leachate Sample Collection and Preparation for Analysis. The leachate sampling method and sample preparation for chemical analysis (NH4-N, NO3-N, TN & TC). A portion of each leachate sample was shipped to our collaborators in Arizona for FT-ICR-MS analysis?.21 Figure 2.3. Precipitation at the Wye REC (Summer 2019). Daily precipitation data gathered using a NOAH IV Precipitation Gauge at the Wye Research and Education center during the summer of 2019. The three sampling dates are noted on the graph with arrows, indicating the date as well as what types of samples (soil or leachate) were collected on the given date?.?.22 Figure 2.4. Compound Classification in a Van Krevelan Diagram. Classification ranges depicted for each of the biogeochemical organic compound classes in a standard Van Krevelan diagram?????????????????????????????????.?25 Figure 2.5. Leachate Chemical Analysis Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on NO3-N, NH4-N, TN, DON, and TC??????????????30 Figure 2.6. Soil Chemical Analysis Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on water extractable NO3-N, NH4-N, KCl extractable NO3-N, NH4-N, water extractable TN and DON, K2SO4 extractable TC and TN, water extractable TC, Microbial biomass C and N, and PMN??????????????????????..????31 viii Figure 2.7. Leachate DOM Van Krevelan Diagrams. The figure is comprised of two sets of Van Krevelan diagrams illustrating the DOM composition of the leachate samples. One separates the DOM based on the sampling depth of the leachate, and one separates the samples based upon the cover cropping strategy of the given sample plot. The graphs are also color coded based upon the aromaticity of the compounds (red indicating highly labile and green indicating highly recalcitrant)?????????????????????????????????33 Figure 2.8. Leachate DOM Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, carbohydrates, lignin, proteins, tannins, unsaturated hydrocarbons, condensed hydrocarbons, and the relative percentage of N containing compounds) within the leachate DOM samples????????36 Figure 2.9. Leachate Photodegradability Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage highly photodegradable compounds (AI>0.67) and the average DBE/C ratio within the leachate DOM and DON samples????????.39 Figure 2.10. Leachate DOM PCoA. The figure illustrates the results of the Principal Coordinate Analysis conducted on the FT-ICR-MS dataset. The shapes of the data points indicate the cover cropping strategy of the sample plot, and the points are shaded based on the sampling depth of the leachate????????????????????????????.????..40 Figure 2.11. Leachate DON Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, lignin, proteins, condensed hydrocarbons and tannins) within the leachate DON samples?????.44 Figure 2.12. Leachate DON Van Krevelan Diagrams. The figure is comprised of two sets of Van Krevelan Diagrams illustrating the DON composition of the leachate samples. One separates the DON based on the sampling depth of the leachate, and one separates the samples based upon the cover cropping strategy of the given sample plot. The graphs are also color coded based upon the aromaticity of the compounds (red indicating highly labile and green indicating highly recalcitrant)??????????????????????????????45 Figure 2.13. Soil Water DOM Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, carbohydrates, lignin, proteins, tannins, unsaturated hydrocarbons, condensed hydrocarbons, and the relative percentage of N containing compounds) within the leachate DOM samples???????...50 Figure 2.14. Soil Chloroform DOM Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, carbohydrates, lignin, proteins, tannins, unsaturated hydrocarbons, condensed hydrocarbons, and the relative percentage of N containing compounds) within the leachate DOM samples???51 ix Figure 2.15. Soil DOM and DON Photodegradability Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage highly photodegradable compounds (AI>0.67) and the average DBE/C ratio within the Soil Water extractable and Soil Chloroform extractable DOM and DON samples?????????????????????..?52 Figure 2.16. Soil Extractable Organic Matter Van Krevelan Diagrams (by Cover Cropping Strategy). The figure is comprised of two sets of Van Krevelan Diagrams illustrating the composition of the extractable soil organic matter composition of the leachate samples. One set represents the water extractable DOM pool while the other set represents the chloroform extractable DOM pool. The graphs are also color coded based upon the aromaticity of the compounds (red indicating highly labile and green indicating highly recalcitrant)?????53 Figure 2.17. Soil Water Extractable DOM PCoA. The figure illustrates the results of the Principal Coordinate Analysis conducted on the soil water extractable DOM FT-ICR-MS dataset. The shapes of the data points indicate the cover cropping strategy of the sample plot, and the points are shaded based on the sampling date???????????????????.56 Figure 2.18. Soil Chloroform Extractable DOM PCoA. The figure illustrates the results of the Principal Coordinate Analysis conducted on the soil chloroform extractable DOM FT-ICR-MS dataset. The shapes of the data points indicate the cover cropping strategy of the sample plot, and the points are shaded based on the sampling date????????????????57 Figure 2.19. Soil Water DON Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, lignin, proteins, condensed hydrocarbons, and tannins) within the soil water extractable DON samples??????????????????????????????????61 Figure 2.20. Soil Chloroform DON Interaction Plots. Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, lignin, proteins, condensed hydrocarbons, and tannins) within the soil chloroform extractable DON samples???????????????????????????????..62 Chapter III Figure 3.1. Microbial Abundance in Minnesota Plots. Average 16S rRNA gene, amoa AOB, nxrA, nirK, and nirS gene abundance (average gene copy number per gram of wet soil) for each of the treatments in the Minnesota sites.?????????????????????.91 Figure 3.2 Microbial Abundance in Beltsville Plots. Average 16S rRNA, amoA AOB, nxrA, nirK, and nirS microbial abundances (average gene copy number per gram of wet soil) for each of the Beltsville treatments???...???????????????????????..92 x Figure 3.3. Microbial Abundance Ratios in Minnesota Plots. Ratios of amoA AOB: 16S rRNA, nxrA:16S rRNA, nirK:16S rRNA, nirS:16S rRNA, nxrA: amoA AOB, and nirS:nirK gene abundances for each of the treatments at the Minnesota study sites.???????????95 Figure 3.4. Microbial Abundance Ratios in Beltsville Plots. Ratios of amoA AOB: 16S rRNA, nxrA:16S rRNA, nirK:16S rRNA, nirS:16S rRNA, nxrA: amoA AOB, and nirS:nirK gene abundances for the Beltsville treatments??...???????????????????96 Figure 3.5. PCoA of Microbial Community Composition. The figure illustrates the results of the Principal Coordinate Analysis conducted on the entire dataset to explore trends in the overall community composition of each soil sample. Shapes are used to differentiate the sample site location of each plot, and the points are shaded to indicate the combination of management strategies.???...??????????????????????????????99 xi Appendices: Table S1. One-Way ANOVA Table for 16S rRNA abundance??????????...115 Table S2. One-Way ANOVA Table for nxrA rRNA abundance??????????.116 Table S3. One-Way ANOVA Table for amoA AOB rRNA abundance?????..??.117 Table S4. One-Way ANOVA Table for nirS rRNA abundance????????..??.118 Table S5. One-Way ANOVA Table for nirK rRNA abundance????????..??.119 Table S6. One-Way ANOVA Table for amoA AOB:16S rRNA abundance??????120 Table S7. One-Way ANOVA Table for nirS:16S rRNA abundance?????...???.121 Table S8. One-Way ANOVA Table for nxrA:16S rRNA abundance???????..?.122 Table S9. One-Way ANOVA Table for nirK:16S rRNA abundance???...?????.123 Table S10. One-Way ANOVA Table for nirS:nirK abundance???????????.124 Table S1. One-Way ANOVA Table for nxrA:amoA AOB abundance?.???????.125 xii Chapter 1 - Introduction and Literature Review Nitrogen (N) cycling has been widely researched for decades as it is an important limiting nutrient in temperate ecosystems, is crucial for plant development, and is imperative for sustaining crop productivity (Hou et al., 2012; Leghari et al., 2016; Wagner et al., 2015). Early developments in the field of study led to the discovery of the Haber-Bosch process in the early 1900s, allowing for the successful synthesis of inorganic ammonia (NH3) (Rouwenhorst et al., 2021). This in turn revolutionized agricultural practices internationally, as N could now be easily supplemented in nutrient deficient fields. However, over time concerns of the environmental impacts of inorganic N additions and over-fertilization came into focus. By the mid-20th century, eutrophication was recognized as a common water pollution problem in North America and Europe (Rodhe, 1969). Additional studies of the impact of fertilization have indicated that additional N in agricultural fields is closely tied to approximately 75% of the United States? production of nitrous oxide (N2O), a powerful greenhouse gas with 300x the warming potential of carbon dioxide (CO2) (US EPA, 2021a). The research within this thesis provides a better understanding of nitrification and less researched N cycling pathways, and the impact that common agricultural management strategies (tillage and fertilization regimes) have on organic N pools and transformations. The first component of this research focuses on organic N held within and leached from conventionally tilled (CT) agricultural soils in Maryland, USA. Organic N refers to the fraction of compounds in soil organic matter (SOM) that contain N, and accounts for a small but active portion of agricultural soils (Chantigny, 2003; McGill et al., 1986). For decades, research into SOM focused on the study of humic substances: complex compounds thought to represent the largest (~60%) pool of SOM and to be the endpoint of SOM degradation (Adey & Loveland, 1 2007; Trevisan et al., 2010). These substances (fulvic acid, humic acid, and fulvin) were thought to control SOM cycling, improving soil fertility and structure, and affecting plant root structure and nutrient uptake (Trevisan et al., 2010). However, in 2015 a paper by Lehmann & Kleber challenged the common understanding of SOM cycling. They put forward that humic substances were much less prevalent in most soils, and that the majority of studied humic substances were a lab artifact resulting from the traditional required alkaline extraction process. Instead, SOM exists as progressively decomposing compounds within soil, a subset of which (such as free amino acids) can be utilized by plants and microorganisms as a source of C and N (Kleber & Johnson, 2010; Lehmann & Kleber, 2015). Furthermore, recent studies indicate that smaller, labile soil organic nitrogen (SON) may be an important source of N for plants regardless of inorganic N additions. The long-term understanding was that microbes and plants utilized inorganic over organic N sources, and that organic N was only important in in nutrient limited environments (Healey et al., 2004). Organic N dynamics in forests indicate that this might not always be the case, and microbes may utilize organic N more often than previous assumed (Kalbitz et al., 2011). Further research is needed to understand these dynamics and the role of organic N as a potential nutrient source. There are several common methods to characterize organic N in soils and water. Briefly: 1) Dissolved Organic Nitrogen (DON). This is the most common method of determining the quantity of small organic molecules present in leachate and water samples. It is calculated by first analyzing filtered samples (0.45um) for NO3-N and NH4-N (the sum of which represents the pool of dissolved inorganic N (DIN)) and the total concentration of N in the sample (TN). DON concentration is then determined as the difference between TN and DIN. The inorganic fractions are conventionally analyzed via flow injection analysis while 2 methods to determine TN vary. Common methods include UV oxidation, dialysis pretreatment, persulfate digestion, and catalytic thermal decomposition (Lee & Westerhoff, 2005; Tirendi et al., 2002; Shimadzu Corporation, 2010). DON may be responsible for up to 70% of soluble N in N-limited soils (Prendergast-Miller et al., 2015), and accounts for a significant portion of leached N despite its utilization by plants and microbes. There is a wide range of leached DON values in the literature and the amount of DON is strongly impacted by land use (i.e.: forest vs. pasture vs. agriculture) (Van Kessel et al., 2009). One study of Australian cotton fields reported that 40% of leached N from the soil was DON (MacDonald et al., 2017), while another study of Chilean forests indicated that leachate samples contained 94-96% leachate (Hedin, 1995). The conversion of land to agricultural use can also influence leached DON. A study of the Chesapeake Bay observed a significant positive relationship between the area of upstream cropland and organic N in stream water samples (Jordan et al., 2010). Additional studies indicate that leached agricultural urea and poultry litter runoff are both major contributors of N in local waterways, and agricultural watersheds can be considered as areas of DON production (Davis et al., 2016; Osburn et al., 2016) 2) Extractable Organic Nitrogen (EON) or Soil DON. These terms refer to the pool of N compounds that can be extracted from soils and is calculated in a similar manner to leachate or water DON. Soils are extracted with a given solution ? common extractants include KCl, K2SO4, CaCl2, and water ? and analyzed for NO3-N, NH4-N, and TN. Soil DON is then calculated as the difference between extractable TDN and DIN (Chantigny, 2003). Both storage and extraction can have a strong impact on a soil sample?s EON (Ros et al., 2010). Similar patterns have been observed between leached DON and EON under a variety of land 3 uses. As with leached DON, studies have reported higher concentrations of water extractable ON (WEON) in forest soils when compared to crop land. Agricultural WEON represents a small fraction of soil total N (25 ugL-1 ? 10mgL-1, or 0.1-3%) and can be strongly impacted by field management strategies (Ros et al., 2009). Studies have shown that WEON increases with the addition of N fertilizer (+118% in EON following fertilizer application), and the incorporation of crop residues can increase EON by 22% (Chantigny, 2003; Ros et al., 2009, 2010). 3) Potentially Mineralizable Nitrogen (PMN). This refers to the subset of organic N that is readily mineralized to plant available, inorganic N (NO3-N and NH4-N). PMN is commonly determined by incubating a soil sample in a microcosm and analyzing the change in NH4-N concentration before and after incubation. The additional NH4-N that is produced during incubation is attributed to the mineralization of OM and used to estimate the amount of readily mineralized N present in the SOM (Waring & Bremner, 1964). There are several methodologies to determine PMN, with varying incubation lengths (7, 28, and 56 days) and conditions (aerobic and anerobic). The 7-day anaerobic protocol is the most common as it is a simple methodology and takes the least amount of time to analyze. Studies of PMN indicate that it is impacted by agricultural management strategies. A 2018 meta-analysis of 43 independent studies indicated that additional N fertilizer can lead to an average increase in PMN; systems fertilized with inorganic N reported 22% higher PMN and systems fertilized with manure reported 34% higher PMN when compared with non-fertilized systems (Mahal et al., 2018). Conservation practices can also lead to an increase in PMN. No-till farming systems have reported an average 11% increase in PMN compared to conventionally tilled systems, while studies of leguminous cover crops reveal 211% greater PMN when compared 4 to systems without cover crops. Furthermore, studies of diverse rotations of three or more crops indicated 44% greater PMN compared to continuous cropping systems (Mahal et al., 2018). 4) Microbial Biomass C and N. This is the amount of C and N present within the soil microbial community, and can be used to assess the size of the soil microbial community (Lori et al., 2017). It is determined by calculating the difference between K2SO4 extractable total carbon (TC) and TN before and after chloroform fumigation (Vance et al., 1987). By fumigating a fresh soil sample, the microbes are killed and lysed. It is assumed that any net change in N or C can be attributed to the microbial community, but a conversion factor is used in final reporting of microbial C and N (DeLuca et al., 2019), because empirical evidence showed that not all cells are lysed and the extractability of microbial C and N in the fumigated samples varies among soil types (Dictor et al., 1997). Microbial biomass can account for 1-5% of SOM and is impacted by agricultural management strategies (e.g.: tillage and cover cropping) in agricultural systems (Cookson et al., 2008). A meta-analysis of 56 studies researching the impact of organic farming techniques indicates that organic systems had an average 41% greater microbial biomass C and 51% greater microbial biomass N compared to conventional systems (Lori et al., 2017). The second study in this thesis focuses on the composition of the N-cycling microbial community. N-cycling has traditionally been thought of as consisting of several microbially mediated, sequential transformations (Figure 1.1) (Kuypers et al., 2018). 5 Figure 1.1. Diagram of Agricultural N-cycling Dynamics Figure 1.1. The various microbially mediated N transformations that occur in agricultural fields. Emphasis is placed on potential N2O leaks. 6 Nitrification: Nitrification is the transformation of NH + - 4 to NO3 and is traditionally considered a two-step process mediated by different bacterial communities. 1) Ammonia Oxidation ? (NH +4 -? NO2 ). This step begins with NH +4 which can come from a variety of sources (decomposition and mineralization of plant residue, microbially mediated fixation of atmospheric N, or supplemental N fertilizer) (Kuypers et al., 2018). The subsequent transformation is mediated by bacteria (Nitrosomonas & Nitrosospira spp.) or archaea (Nitrososphaera & Nitrosoarchaeum spp.) with the ability to produce the enzyme ammonia monooxygenase (amoA), which transforms NH +4 into NH2OH. Nitrous oxide (N2O) can be produced as a by-product of this reaction if oxidation is incomplete. The bacteria or archaea also can produce the enzyme hydroxylamine oxidoreductase (hao) which converts the resulting NH2OH into nitrite (NO - 2 ) (Peng et al., 2021; Yin et al., 2018). 2) Nitrite Oxidation ? (NO - -2 ? NO3 ). This step is meditated by a different subset of bacteria (Nitrobacter & Nitrospira spp.) that can produce the nitrate oxidoreductase enzyme (nxr), which converts NO - 2 into plant available (nitrate) NO - 3 (Cabello et al., 2009). Denitrification: Denitrification refers to the transformation of NO -3 to N2 and is also microbially mediated. Denitrifiers generally contain all the enzymes necessary for complete denitrification and, though there is a wide variety of denitrifying genera, most denitrifying microbes fall into one of the following classes: Alphaproteobacteria (32%), Betaproteobacteria (28%), and 7 Gammaproteobacteria (28%) (Jones et al., 2008). There are several intermediate transformations: 1) Nitrate reduction ? (NO -3 ? NO -2 ). During this step, NO -3 is transformed into NO -2 via the enzyme nitrate reductase (nar) (Alvarez et al., 2014). This step requires anerobic conditions as enzyme synthesis is inhibited by the presence of oxygen. 2) Nitrite reduction ? (NO - 2 ? NO). During this step NO2 is converted to nitric oxide (NO) using the enzyme nitrite reductase (nir). There are two common forms of nir that are often targeted when studying the denitrifying community: nirS which encodes for a heme form of the enzyme, and nirK which encodes for the less common copper form of the enzyme (Alvarez et al., 2014). 3) Nitric oxide reduction (NO? N2O). During this step NO is converted to nitrous oxide (N2O) by nitric oxide reductase (nor). This step is responsible for many of the N-N bonds present in nature, and often occurs quickly after nitrite reduction as NO is toxic to cells (Alvarez et al., 2014). 4) Nitrous oxide reduction (N2O ? N2). During this step N2O is converted into dinitrogen (N2) via nitrous oxide reductase (nosZ). As with the other denitrification enzymes, this activity is strongly inhibited by the presence of O2. Given that this step transforms N2O, nosZ gene expression has been frequently studied in relation to N2O emissions, and lower rates of gene expression are correlated with increases in emission rates (Kong et al., 2021). Recent studies of Nitrospira sp. have redefined our understanding of nitrification. In 2015, unique samples of Nitrospira sp. with the ability to completely oxidize NH +4 to NO - 3 were isolated in in aquaculture filters (Van Kessel et al., 2015). In the years since its discovery, it has 8 been isolated from a plethora of systems (i.e.: wastewater treatment plants, freshwater ecosystems, and both agricultural and forest soils) (Xu et al., 2020). Comammox Nitrospira sp. cannot be classified based on 16S rRNA sequencing, instead functional gene markers need to be amplified and sequenced. Previous studies have identified two clades of comammox Nitrospira (Clade A and Clade B), with slight niche differences between the two clades (Koch et al., 2018; Xu et al., 2020). 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Ammonia-oxidizing archaea (AOA) play with ammonia- oxidizing bacteria (AOB) in nitrogen removal from wastewater. Archaea, 2018. https://doi.org/10.1155/2018/8429145 13 CHAPTER II Title: Dissolved organic matter and dissolved organic nitrogen composition shifts with sampling depth and management strategies in agricultural fields Author Names and Affiliations: Alyssa Wellman Houde a, Nicole Fiorellino b, Malak Tfaily c, and Stephanie Yarwood a a Department of Environmental Science and Technology, University of Maryland, College Park, Maryland, USA, b Department of Plant Science and Landscape Architecture, University of Maryland, College Park, Maryland, USA, c Department of Environmental Science, University of Arizona, Tucson, Arizona, USA Abstract: For decades, soil organic matter (SOM) research focused on the dynamics of complex organic acids (i.e.: fulvic and humic acids). Recent studies have shown, however, that these compounds are lab artifacts and that dissolved organic matter (DOM) exists as smaller, free molecules within the soil matrix. It also accounts for up to 70% of N in surface waters (Lehmann & Kleber, 2015; Osburn et al., 2016). Fourier transform ion cyclotron mass spectrometry (FT- ICR-MS) and traditional chemical N measures (NO3-N, NH4-N, total dissolved N (TN), total dissolved C (TC), microbial biomass C and N, dissolved organic N (DON), and potentially mineralizable N) were used to determine the chemical composition and amount of leached and extractable DOM and DON in agricultural fields. Using a series of Repeated Measure ANOVAs and Pearson correlations, we investigated the impact of three cover cropping strategies (cereal cover, a mixed cereal and leguminous cover, and fallow) and sampling date on the DOM and DON pools. We also studied the impact of sampling depth on the composition of leached DOM 14 and DON and found that the relative percentage of recalcitrant compounds and the photodegradability of DOM and DON increased with depth. Traditional chemical soil N measures (K2SO4 extractable TN (F=11.437, p=0.043), water extractable TN (F=10.676, p=0.04), and DON (F=10.901, p=0.046)) significantly increased with sampling date and, though not significant, the percentage of labile DOM declined slightly from June to late July. Though not statistically significant, plots with a mixed cover crop reporting slightly higher percentages of soil protein and leached free amino sugars compared to the other cover class treatments. Further study that includes later sampling dates are needed to determine the significance of temporal and cover-based trends, but that this line of research could have lasting impact on how we understand organic N cycling dynamics and could eventually be used to inform N fertilizer and cover- cropping recommendations. Introduction: Nitrogen (N) is frequently a limiting nutrient in plant growth, and as such is often supplemented in agricultural soils through inorganic (nitrate (NO -3 ) and ammonia (NH + 3 )) and organic (manure) fertilizer additions (Bundy & Meisinger, 1994). Early agricultural research focused on developing technologies and strategies for feeding the world?s ever-growing population. These successful efforts resulted in the increase in international food productivity during the second half of the 20th century, with an estimated 12-13% increase in the food supply of several African and East Asian nations between 1960 and 1990 (Pingali, 2012). However, over fertilization has also acidified agricultural soil (?urovec et al., 2021) and leached N into local waterways (Dybowski et al., 2020) causing unforeseen environmental issues. These developments have highlighted the need to better understand agricultural N dynamics and has led to an increase in studies of organic N dynamics and the role of organic 15 matter as underestimated source of N in agricultural fields. Research focused on organic nutrient availability has underscored the positive impact of cover-cropping and legumes on soil health and organic C and N accumulation (Ebelhar et al., 1984; Mitchell, 1977; Utomo et al., 2010; Wei et al., 2018). Other studies have investigated alternative tillage and no-till practices, noting substatntial increases in mineralizable N (Mahal et al., 2018) and microbial soil diversity under minimum tillage (Li et al., 2020). Despite the growing wealth of studies investigating the impacts of management on organic matter (OM) cycling, many of these studies rely on traditional chemical analysis (i.e.: extractable dissolved organic matter (DOM) and dissolved organic N (DON), potentially mineralizable N (PMN), and Bradford Protein Assays). While these measures provide insight into the size and reactivity of OM pools, they do not provide information on the chemical composition of OM or the mechanims of OM cycing. Advancements in NMR (nuclear mass resonance spectroscopy) and mass spectrometry provide a more complete picture of the composition of DOM and DON samples. Studies have also been published utilizing Fourier Transform Ion Cyclotron Mass Spectrometry (FT-ICR-MS) to characterize OM in wetlands, freshwater, and marine environments, and is a powerful tool for understanding the complexities of leached OM and soil OM (D?Andrilli et al., 2015; Tfaily et al., 2015). This form of analysis can capture thousands of compounds a given sample and can determine the molecular formula for each (Wilson & Tfaily, 2018). However, FT-ICR-MS data is qualitative in nature and, while it provides incredibly detailed insight into the composition of organic compounds within a sample, it cannot quantify the size of the DOM or DON pool. As such, FT-ICR-MS is often paired with traditional, quantitative DOM measures to contextualize the dataset. 16 Early FT-ICR-MS research began in 1973 and was primarily utilized to characterize complex organic mixtures with a focus on sources of fuel such as petroleum and pyrolysis biomass derived liquids (Marshall & Chen, 2015; Yan et al., 2016). In the decades since, FT- ICR-MS has been used to study OM in peatland and wetland soils as well as glacial, marine, and freshwater ecosystems (D?Andrilli et al., 2015; Hanson et al., 2018; Solihat et al., 2019; Wilson & Tfaily, 2018). Preliminary studies indicate that it has potential to be a powerful tool to understand agricultural organic N cycling and could be used to inform sustainable soil management techniques (Kwiatkowska-Malina, 2018). However, few studies have applied FT- ICR-MS analysis to explore agricultural N cycling. Our study was designed to investigate this knowledge gap and apply both traditional chemical N and OM measures with FT-ICR-MS techniques to investigate the chemical composition of soil organic N throughout the growing season in Maryland corn fields. We also examined the potential impact of cover cropping on the amount and composition of soil organic N and organic N leached from agricultural fields. We had several hypotheses: 1) The presence of N-fixing leguminous cover crops are known to increase available N (Kermah et al., 2018), and will increase soil organic N. This will subsequently lead to a higher proportion of labile N-containing compounds in leachate and soil samples from the plots with a mixed leguminous and cereal cover crop than under a cereal cover or left fallow. 2) The OM present in the soil would act as a source of nutrients for the growing corn and we will observe a decrease the relative proportion of labile N-containing compounds throughout the growing season. 17 3) Leaching has repeatedly been reported as a major pathway of DON loss in agricultural systems, and studies have shown that up to 61% of lost DON can be attributed to lignin, a highly photodegradable organic compounds (Benner & Kaiser, 2011; Li et al., 2018). As such, we hypothesize that leached DOM and DON would be primarily comprised of lignin and that deeper samples would have a higher proportion of recalcitrant, highly photodegradable, and lignin-like compounds. Experimental Procedures: Sample Site and Collection: In Summer of 2019, composite 0-20cm soil samples were collected from a series of 30x60 ft (9.14m x 18.28 m) research plots located at the Wye Research and Education Center (REC). This UMD Research Station was established in Queenstown, MD in 1982 and has had a long history of agricultural research on Maryland?s Eastern Shore (College of Agriculture and Natural Resources, 2021). The study site itself is level (0-2% slope) and soils are characterized as Mattapex-Butlertown (MqA) silt loam with a 0 ? 2% slope (Soil Survey Staff). A subset of six plots were selected from a subset of a plots on 6-acre long-term study established in 2019, all of which were under corn (Zea mays L.) production during the 2019 field season and soybeans (Glycine max L.) the previous year. All the plots were conventionally tilled (CT) and received inorganic fertilizer. On 3/18/2019 the 6-acre field received a broadcast application of 12000 lbs of high Calcium lime at a rate of 2000 lbs/ ac. Urea ammonium nitrate (UAN) fertilizer was applied twice: 15 gals (or 49lbs N) was applied during planting on 6/4/2019, and 240 gals (782 lbs N) was applied via sidedress application on 6/19/2019. The plots 18 differed in cover crop treatments: two received a mixed cover of crimson clover (Trifolium incarnatium L.) and rye (Secale cereale L.), two received a cereal rye cover, and two were left fallow over the winter. Soil samples were taken twice during the summer: shortly before planting (June 3rd) and when the corn had reached the V6 stage (July 18th). To collect each composite soil sample, the study plot was divided into a grid of nine 10ft x 20ft (3.04m x 6.09m) rectangles (Figure 2.1). From there, 3 individual 0-20cm cores were collected from each of the grid quadrants. This resulted in 27 cores per plot, which were combined and sieved (4mm) together while in the field. In total, 12 composite soil samples were collected over the growing season. Prior to planting, suction cup lysimeters were installed in the center of each of the plots at 3 different depths (30cm, 60cm, and 90cm) (Figure 2.2) and the resulting 18 lysimeters were sampled three times during the growing season (June 3rd, June 18th, and July 16th). Due to varied field conditions and precipitation, samples were not able to be collected from each lysimeter at every sampling date (Figure 2.3). Over the field season, 37 leachate samples were collected in total. All samples were returned to the lab and refrigerated at 4oC for later analysis. Chemical Analysis: Within 24 hours of sampling, 5g dry equivalent of each refrigerated, field moist soil sample was placed in 12, 50mL centrifuge tubes (Figure 2.1). Soils in three of those tubes were extracted with water by adding 50mL of deionized water to the samples and periodically shaken to resuspend the soil particles for 24 hours. The supernatant was collected via vacuum filtration through 40um filter paper, acidified to pH<2 using sulfuric acid, and stored at 4oC for later analysis. 19 Figure 2.1: Soil Sample Collection and Preparation for Analysis Figure 2.1. The soil sampling method and sample preparation for chemical analysis (water extractable NH4-N, NO3-N, TN & TC, KCl extractable NH4-N and NO3-N, K2SO4 extractable TN & TC, PMN, and Microbial Biomass C & N). Soil DON was calculated by subtracting the DIN from the total N concentration in the soil water extracts. PMN was calculated by determining the difference in NH4-N between the anaerobically incubated KCl extracts and fresh field moist soil samples. Soil microbial biomass C and N was determined by determining the average difference in C and N between the fumigated and fresh field moist K2SO4 extracts and correcting for extraction efficiency by dividing the value by 0.45. A portion of each composite soil sample was shipped to our collaborators in Arizona for FT-ICR-MS analysis. 20 Figure 2.2: Leachate Sample Collection and Preparation for Analysis Figure 2.2. The leachate sampling method and sample preparation for chemical analysis (NH4-N, NO3-N, TN & TC). A portion of each leachate sample was shipped to our collaborators in Arizona for FT-ICR-MS analysis. 21 Figure 2.3: Precipitation at the Wye REC (Summer 2019) Figure 2.3. Daily precipitation data gathered using a NOAH IV Precipitation Gauge at the Wye Research and Education center during the summer of 2019. The three sampling dates are noted on the graph with arrows, indicating the date as well as what types of samples (soil or leachate) were collected on the given date. 22 The soils in a second another set of three centrifuge tubes were extracted with 50mL of 2M KCl, by shaking for one hour with a fixed speed reciprocal shaker (280rpm) and collecting the supernatant via gravimetric filtration with Whatman 42 filter paper (Vance et al., 1987; Maynard & Kalra, 1993). A third set of 3 analytical soil replicates were incubated anaerobically at 40oC with 10mL of water for 7 days (Waring & Bremner, 1964). The samples were then shaken with 40mL of 2.5M KCl using a fixed speed reciprocal shaker (280 rpm) and the supernatant was collected via gravimetric filtration with Whatman 42 filter paper. A fourth set of 3 analytical replicates was extracted with 25mL of 0.5M K2SO4, shaken for one hour with a fixed speed reciprocal shaker (280rpm), and the supernatant was collected via gravimetric filtration with Whatman 42 filter paper. A final set of 3 analytical replicates (5g dry equivalent of field moist soil) was weighed into 50mL glass beakers and fumigated in a desiccator under a vacuum with chloroform for 48 hours to determine microbial biomass C and N. The fumigated soils were then shaken with a fixed speed reciprocal shaker (280rpm) with 0.5M K2SO4 for one hour, and the supernatant was collected by gravimetric filtration with Whatman 42 filter paper (Vance et al., 1987). All KCl and K2SO4 extracts were stored frozen at -20 oC for later analysis. The water extractions and KCl solutions were analyzed for NO 3 ? N and NH4 ? N using a Lachat Quickchem Flow Injection Analyzer (Harbridge, 2007a; Harbridge, 2007b), and all the solutions were analyzed for their total nitrogen (TN) and total carbon (TC) content using a Shimadzu TOC Analyzer (Shimadzu Corporation, 2010) (Figure 2.1). The leachate samples were filtered via vacuum filtration through 40um filter paper, acidified to pH<2 using sulfuric acid, and stored at 4oC until they analyzed for NO3-N, and NH4-N using a Lachat Quickchem Flow Injection Analyzer (Harbridge, 2007a; Harbridge, 2007b) and were analyzed for TN and TC with a Shimadzu TOC Analyzer (Shimadzu Corporation, 2010) (Figure 2.2). 23 Potential mineralizable N was determined by subtracting the average NH4-N concentration of the non-incubated KCl extractions from the average NH4-N concentration of the samples that were incubated for 7 days. The resulting change in NH4-N represents the pool of organic N in the soil that is readily converted into a plant available form (Waring & Bremner, 1964). Microbial biomass C and N was determined by comparing the average C and N values of non-fumigated 0.5M K2SO4 extractions with those of the samples that had been fumigated with chloroform and applying the conversion factors in equations 1 and 2 (Vance et al., 1987). Biomass C = ?C / 0.45 (1) Biomass N = ?N / 0.45 (2) FT-ICR-MS Analysis: To prepare the soil samples for FT-ICR-MS analysis, 100mg of soil was weighed into a 2mL capped centrifuge tube. Given the cost and time for FT-ICR-MS analysis, replicates were not used for this portion of the study. Each sample was initially extracted with 1mL of de-ionized water and shaken with an orbital sharker (20rpm) for 2 hours (Figure 2.1) (Tfaily et al., 2015). The solution was allowed to settle and then centrifuged for 10 minutes at 4430g to collect the supernatant. Organic compounds in the resulting solution (Figure 2.1) and the leachate (Figure 2.2) samples were isolated with solid phase extraction (SPE) (Dittmar et al., 2008). To improve ionization efficiency, the DOM isolates were mixed with MeOH, to create a resulting solution that is 33% sample and 66% MeOH. The soil fraction of the water extraction samples underwent a second extraction using 1mL of chloroform and shaken with the orbital shaker (20rpm) for 2 hours (Tfaily et al., 2015). 24 Figure 2.4: Compound Classification in a Van Krevelan Diagram Figure 2.4: Classification ranges depicted for each of the biogeochemical organic compound classes in a standard Van Krevelan diagram. 25 The resulting solution was centrifuged for 10 minutes at 4430g and injected directly into the FT- ICR-MS. By sequentially extracting the samples first with water and then with chloroform, we were able to capture both the pool of free organic compounds, as well as the semi-polar and non- polar compounds that are captured in a chloroform extraction (Tfaily et al., 2018). The soil water, chloroform, and leachate samples were ionized with electrospray ionization (ESI) and analyzed with a Bruker 9.4T FT-ICR. The resulting data was processed with Formularity Version 1.0. Using a compound identification algorithm and the FR-ICR-MS peaks, the database assigned a chemical formula and determined the number of C, H, O, N, S, and P atoms present with each compound (Toli? et al., 2017). These matches were used to calculate the O/C ratio and H/C for each of the compounds which, in turn, was used to broadly classify the compounds present into the categories in Table 1 and Figure 2.4 (Tfaily et al., 2015). Table 2.1. Adapted from AminiTabriz et al, (2020). The ranges were used to determine the biogeochemical classification of the DOM and DON compounds within the samples. Compound Class O/C range H/C Range Lipid 0.0 ? 0.3 1.5 ? 2.5 Unsaturated Hydrocarbon 0.0 ? 0.3 1.0 ? 1.6 Condensed Hydrocarbon 0.0 ? 0.4 0.2 ? 0.8 Lignin 0.29 ? 0.65 0.7 ? 1.5 Protein 0.3 ? 0.6 1.5 ? 2.3 Carbohydrate 0.5 ? 0.7 0.8 ? 2.5 Amino Sugar 0.5 ? 0.7 0.8 ? 2.5 Tannin 0.65 ?1.00 0.8 ? 1.5 The FT-ICR-MS peaks were also used to calculate each sample?s double bond equivalency (DBE) and aromaticity index (AI) (Equations 3&4) (Koch & Dittmar, 2006). DBE = 1 + ? (2C ? H + N + P) (3) AI = (1 + C ? O ? S ? 0.5H) / (C ? O ? S ? N ? P) (4) 26 The number of C atoms can be divided by the number of rings and double C bonds in a compound to determine the relative stability. High DBE/C ratios can indicate the presence of stable, aromatic structures (Koch & Dittmar, 2006). Similarly, the AI of a sample can indicate whether a compound is easily photodegradable; an AI ? 0.67 indicates the presence of more recalcitrant, aromatic structures. Conversely lability can be indicated by a compound?s H/C ratio; highly labile compounds are classified as having a H/C > 1.5 (D?Andrilli et al., 2015). Data Analysis: The FT-MS-R Exploratory Data Analysis Tool (FREDA) was used to visualize the spread of the data with Van Krevelan diagrams (Figure 2.4) and conduct Principal Coordinates Analysis (PCoA) on the FT-ICR-MS dataset. The statistical package JMP Pro 15 was used for multivariate analysis. Pearson Correlations, Repeated Measure ANOVAs, and Multiple Regression Analysis were performed to ascertain the impact of sampling date and cover- cropping treatments on the composition of DOM and DON pool. Similar analysis was used to assess the relationship between the FT-ICR-MS data, photodegradability metrics, and traditional N measure gathered through chemical analysis (microbial biomass C and N, PMN, DON, TDN, and KCl and water extractable NO3-N and NH4-N). Results: Leachate Chemical Analysis: Repeated Measure ANOVAs to investigate the impact of sampling date and cover cropping on traditional chemical N measures reveal little difference in DIN measures among the cover crop treatments (Table 2.2). Though not significant (p=0.05) there was an increase in NO3- N (F=3.010, p=0.087) and TN (F=3.619, p=0.058) with date (Figure 2.5a & c). 27 Table 2.2: The sample numb er, sampling depth, sampling date and cover cropping strategy along with several traditional chemical N measures for each of the leachate samples. Leachate Chemical Analysis NO3-N NH3-N TC TN DIN DON # Depth Date Cover (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) 15 1 Mixed 0.17 0.04 12.37 4.72 0.21 4.51 18 Cereal 0.05 0.005 18.82 1.20 0.05 1.15 20 Fallow 5.51 0.07 10.93 6.24 5.58 0.66 21 2 Mixed 1.71 -0.004 41.9 6.99 1.70 5.28 22 Fallow 1.51 0.009 14.27 2.63 1.51 1.11 25 30 Mixed 3.34 -0.003 10.92 4.34 3.34 1.00 28 Cereal 311.72 12.60 5.40 324.31 31 Mixed 16.35 0.02 6.44 19.27 16.37 2.90 32 3 Fallow 126.88 0.27 3.32 130.3 127.15 3.15 34 Cereal 103.27 0.11 5.53 103.38 37 Mixed 219.79 5.49 8.22 225.28 13 1 Cereal 0.62 -0.02 2.71 3.00 0.60 2.40 26 2 Mixed 0.09 0.007 11.82 1.57 0.10 1.47 29 60 Cereal 48.31 0.27 3.84 51.52 48.59 2.93 35 3 Cereal 13.65 0.35 7.99 14.34 14.00 0.34 38 Mixed 21.24 0.13 5.24 22.26 21.37 0.89 14 Cereal 0.22 -0.003 2.62 1.30 0.21 1.09 16 1 Fallow 2.67 -0.02 2.73 9.10 2.64 6.46 17 Cereal 1.75 0.04 2.13 5.34 1.80 3.54 19 Cereal 0.90 -0.005 12.79 2.83 0.90 1.93 23 Fallow 2.64 -0.03 2.08 2.78 2.61 0.17 2 24 90 Cereal 3.41 0.04 2.8 3.61 3.45 0.16 27 Mixed 3.94 0.08 6.81 4.21 4.02 0.19 30 Cereal 21.69 0.00 2.13 23.09 21.69 1.40 33 Fallow 7.30 -0.01 1.34 8.666 7.29 1.38 3 36 Cereal 219.93 6.46 6.72 226.39 39 Mixed 16.87 0.00 4.48 16.87 28 Table 2.3: The plot, sampling date, and cover cropping strategy along with several traditional chemical N measures for each of the soil samples. Soil Chemical Analysis Avg. KCl Avg. K2SO4 Microbial Avg. Water Extractable (mg/L) Plot Date Cover extractable (mg/L) extractable (mg/L) Biomass NO3-N NH4-N TC TN DIN DON NO3-N NH4-N PMN TC TN C N 0.17 +/- -0.02 3.32 +/- 5.44 +/- 0.17 +/- 0.54 +/- 42.84 0.98 +/- 1 Cereal 0.15 5.29 1.29 -69.63 4.93 0.01 +/- 0.01 0.19 0.06 0.07 0.01 +/- 3.37 0.73 0.28 +/- -0.03 3.65 +/- 6.35 +/- 0.36 +/- 0.53 40.71 1.66 +/- 2 Fallow 0.25 6.11 2.69 59.93 4.88 0.01 +/- 0.00 0.41 0.14 0.01 +/- 0.01 +/- 2.48 0.00 0.23 +/- -0.03 3.63 +/- 9.00 +/- 0.29 +/- 0.57 +/- 44.00 1.34 +/- 3 Mixed 0.20 8.80 2.39 -30.78 5.75 0.00 +/- 0.00 0.30 0.18 0.02 0.02 +/- 4.29 1.05 1 0.20 +/- -0.03 3.39 +/- 6.78 +/- 0.31 +/- 0.54 +/- 38.56 1.66 +/- 4 Fallow 0.17 6.61 1.95 152.96 5.60 0.01 +/- 0.00 0.14 0.06 0.02 0.02 +/- 1.31 0.15 0.18 +/- -0.03 3.51 +/- 6.25 +/- 0.23 +/- 0.54 +/- 42.53 1.59 +/- 5 Cereal 0.14 6.11 1.79 221.63 7.62 0.01 +/- 0.01 0.20 0.05 0.00 0.01 +/- 1.80 0.06 0.17 +/- -0.03 3.77 +/- 6.95 +/- 0.26 +/- 0.61 +/- 40.28 1.66 +/- 6 Mixed 0.14 6.80 1.60 290.89 7.20 0.02 +/- 0.00 0.21 0.13 0.01 0.02 +/- 1.83 0.04 0.46 +/- -0.03 3.39 +/- 10.69 0.63 +/- 0.55 +/- 20.10 2.59 +/- 1 Cereal 0.43 10.26 0.93 223.93 5.03 0.12 +/- 0.05 0.12 +/- 0.17 0.04 0.05 +/- 0.66 0.04 0.31 +/- -0.01 3.76 +/- 8.27 +/- 0.38 +/- 0.58 +/- 23.53 2.14 +/- 2 Fallow 0.30 7.97 1.54 195.78 5.09 0.01 +/- 0.00 0.55 0.80 0.01 0.09 +/- 0.70 0.06 0.95 +/- 0.00 +/- 3.44 +/- 8.42 +/- 1.12 +/- 0.50 +/- 22.47 3.22 +/- 3 Mixed 0.94 7.47 0.65 193.78 5.14 0.01 0.02 0.18 0.88 0.02 0.01 +/- 1.10 0.04 3 1.99 +/- 0.00 +/- 3.18 +/- 15.16 2.17 +/- 0.61 +/- 23.69 5.42 +/- 4 Fallow 1.99 13.17 0.71 243.70 5.86 0.05 0.00 0.24 +/- 2.12 0.05 0.01 +/- 0.26 0.05 1.41 +/- -0.01 2.71 +/- 12.45 1.57 +/- 0.57 +/- 21.52 4.18 +/- 5 Cereal 1.40 11.05 1.03 203.04 4.83 0.03 +/- 0.00 0.08 +/- 0.23 0.06 0.03 +/- 0.98 0.03 0.82 +/- -0.01 2.84 +/- 9.60 +/- 0.91 +/- 0.57 +/- 21.97 3.04 +/- 6 Mixed 0.81 8.79 0.75 -3.52 4.74 0.1 4 +/- 0.01 0.06 1.22 0.00 0.02 +/- 1.59 0.02 29 Figure 2.5: Leachate Chemical Analysis Interaction Plots TC Figure 2.5: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on NO3-N, NH4-N, TN, DON, and TC. 30 Figure 2.6: Soil Chemical Analysis Interaction Plots T C TC Figure 2.6: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on water extractable NO3-N, NH4-N, KCl extractable NO3-N, NH4-N, water extractable TN and DON, K2SO4 extractable TC and TN, water extractable TC, Microbial biomass C and N, and PMN. 31 Dissolved organic nitrogen significantly decreased throughout the sampling season (F=5.940, p=0.036) and ? though not significant- there was a minor interaction between cover cropping strategy and date (F=0.209, p=0.209) (Figure 2.5d). There was a wider range of DON values at the beginning of the sampling season (Table 2.2), with the highest DON reported in the fallow plots, followed by the mixed and cereal cover plots. Variability between the cover crops treatments decreased over time, and similar DON values were reported across all cover cropping treatments by the third sampling date (Figure 2.5d). Total carbon also varied with date (F=3.039, p=0.084), although there was not a consistent increase or decrease (Figure 2.5e). Soil Chemical Analysis: There was a significant increase in K2SO4 extractable TN (F=11.437, p=0.043), water extractable TN (F=10.676, p=0.04), and DON (F=10.901, p=0.046) over time (Table 2.3 and Figure 2.6 e, h, & f). Though not significant, there was also an observed increase in soil water extractable DIN measures over time (NO3-N, F= 5.5782, p=0.0992; NH3-N, F=10.021, p=0.0507) (Figure 2.6 a & b). Significant decreases were observed in the K2SO4 extractable TC pool (F=715.212, p=0.0001) and PMN (F=38.752, p=0.008) (Figure 2.6 g & l). Cover class did not influence the chemical N measures, and there was little change in either Microbial Biomass C or N (Table 2.3 and Figure 2.6 j & k). Leachate DOM: All the DOM samples were primarily composed of lignin (48 - 66% of compounds in the DOM pool). There was more variability in relative percentages of the other compounds present (Table 2.4). 32 Figure 2.7: Leachate DOM Van Krevelan Diagrams Sampling Depth Cover Cropping Strategy Figure 2.7. The figure is comprised of two sets of Van Krevelan diagrams illustrating the DOM composition of the leachate samples. One separates the DOM based on the sampling depth of the leachate, and one separates the samples based upon the cover cropping strategy of the given sample plot. The graphs are also color coded based upon the aromaticity of the compounds (red indicating highly labile and green indicating highly recalcitrant). 33 Table 2.4: The sample number, sampling date, cover cropping strategy, DOM FT-ICR-MS peaks (indicating the total number of unique compounds), the relative percentage of each compositional class (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amnio sugars, carbohydrates, lignin, tannins, and unclassifiable compounds), the average DBE/C ratio and the percentage of compounds with an AI> 0.67. Leachate DOM Composition Relative Percentage Unsat Con Amino Comp. % # Depth Date Cover Peaks Lip. Hydro Hydro Pro. Sugars Carb. Lig. Tan. NA DBE/C AI 15 1 Mixed 3979 1.0 0.7 4.8 4.9 2.2 4.1 48.4 7.2 26.8 0.5 +/- 0.2 1.0 18 Cereal 3687 1.0 0.8 2.7 4.8 1.8 3.0 52.8 7.3 25.9 0.5 +/- 0.1 0.7 20 Fallow 3574 1.0 0.6 5.2 3.0 1.2 2.8 53.5 9.8 23.0 0.5 +/- 0.1 0.7 21 2 Mixed 4012 0.7 0.6 4.8 3.6 2.6 3.9 49.0 7.8 27.1 0.5 +/- 0.2 0.9 22 Fallow 3866 0.6 0.6 4.5 3.9 1.4 3.5 50.6 9.2 25.8 0.5 +/- 0.2 0.9 30 25 Mixed 3899 0.6 0.6 2.3 5.5 1.8 2.2 49.3 7.8 29.8 0.5 +/- 0.1 0.7 28 Cereal 3386 1.2 0.6 1.5 8.6 1.8 0.5 57.9 6.5 21.4 0.5 +/- 0.1 0.2 31 Mixed 4347 0.7 0.5 4.5 4.0 2.0 3.5 48.5 7.3 29.1 0.5 +/- 0.2 0.8 32 3 Fallow 3494 0.9 0.5 3.7 4.7 1.4 2.5 55.3 7.4 23.6 0.5 +/- 0.1 0.7 34 Cereal 3573 0.6 0.5 3.0 6.0 1.3 1.9 57.6 8.5 20.7 0.5 +/- 0.1 0.4 37 Mixed 4342 1.1 0.7 2.5 6.3 2.5 1.8 50.6 7.0 27.5 0.5 +/- 0.1 0.6 13 1 Cereal 2993 1.9 1.2 0.8 8.5 1.3 1.0 56.6 4.7 24.1 0.4 +/- 0.1 0.2 26 2 Mixed 3540 1.1 0.7 0.9 6.3 2.1 1.5 52.5 8.3 26.7 0.4 +/- 0.1 0.3 29 60 Cereal 2874 1.2 1.3 0.4 8.4 1.1 0.7 61.9 3.6 21.6 0.4 +/- 0.1 0.1 35 3 Cereal 3248 1.5 0.9 3.2 4.2 1.3 2.9 53.6 8.3 24.1 0.5 +/- 0.1 0.6 38 Mixed 2386 1.6 0.9 0.5 5.7 0.9 0.6 63.4 7.3 19.3 0.5 +/- 0.1 0.1 14 Cereal 3560 2.0 1.0 1.7 9.3 1.7 1.0 53.8 4.9 24.6 0.4 +/- 0.1 0.3 16 1 Fallow 1255 3.9 0.6 0.3 12.0 1.9 1.5 52.4 1.8 25.7 0.4 +/- 0.1 0.1 17 Cereal 2767 1.4 1.1 0.6 8.8 1.2 1.3 59.4 4.5 21.8 0.4 +/- 0.2 1.1 19 Cereal 1650 1.3 0.6 0.4 6.2 0.4 0.2 66.2 4.0 20.7 0.4 +/- 0.1 0.2 23 Fallow 1189 3.6 0.6 0.3 4.8 1.2 0.9 61.1 2.1 25.3 0.4 +/- 0.1 0.1 2 24 90 Cereal 2675 1.6 0.5 0.6 11.0 2.0 0.9 55.6 6.0 21.9 0.4 +/- 0.1 0.3 27 Mixed 1935 2.9 0.8 0.6 7.2 1.3 1.2 59.8 2.0 24.1 0.4 +/- 0.1 0.3 30 Cereal 2879 2.1 0.9 1.3 7.4 0.9 0.5 58.3 4.4 24.3 0.4 +/- 0.1 0.1 33 Fallow 1352 3.1 0.7 0.2 4.7 0.4 0.7 59.6 2.5 28.0 0.4 +/- 0.1 0.0 3 36 Cereal 2541 1.5 1.1 0.6 6.9 0.7 0.6 64.7 3.7 20.3 0.4 +/- 0.1 0.2 39 Mixed 1816 3.0 0.8 0.8 6.7 1.5 1.0 56.0 1.4 28.8 0.4 +/- 0.1 0.3 34 Table 2.5: The results of Pearson Correlations comparing the sampling depth, the relative percent of highly photodegradable compounds (AI>0.67), the average DBE/C ratio, the amount of DON, TN, TC, NH4-N, and NO3-N, and the relative percentage of each compositional class (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amnio sugars, carbohydrates, lignin, tannins, and unclassifiable compounds) within the leachate DOM s amples. Bolded values within the chart indicate statistically significant correlations (p<0.5). Leachate DOM Pearson Correlations Avg NH4- NO3- Comp. Amino Con Unsat % AI DBE/C DON DIN TN TC N N NA Tan. Lig. Carb. Sugars Pro. Hydro. Hydro. Lip. Depth -0.62 -0.82 -0.27 -0.14 -0.15 -0.44 -0.10 -0.14 -0.22 -0.81 0.61 -0.72 -0.48 0.55 -0.80 0.37 0.76 Lip. -0.62 -0.84 -0.02 -0.21 -0.22 -0.46 -0.12 -0.21 0.09 -0.84 0.34 -0.52 -0.27 0.42 -0.64 0.13 Unsat Hydro -0.28 -0.25 -0.08 0.01 0.01 -0.27 0.04 0.01 -0.31 -0.33 0.41 -0.39 -0.35 0.28 -0.42 Con Hydro. 0.79 0.80 0.18 -0.02 0.00 0.54 -0.09 -0.02 0.31 0.76 -0.71 0.91 0.44 -0.66 Pro. -0.46 -0.72 0.21 0.11 0.10 -0.48 0.18 0.11 -0.30 -0.52 0.25 -0.61 0.06 Amino Sugars 0.50 0.14 0.41 0.05 0.04 0.44 0.09 0.05 0.50 0.36 -0.82 0.56 Carb. 0.85 0.63 0.30 -0.22 -0.20 0.61 -0.25 -0.22 0.47 0.65 -0.81 Lig. -0.67 -0.36 -0.31 0.17 0.17 -0.41 0.15 0.18 -0.72 -0.54 Tan. 0.61 0.87 -0.12 0.07 0.08 0.44 0.02 0.07 0.01 Comp. NA 0.33 -0.02 0.18 -0.29 -0.30 0.25 -0.25 -0.29 NO3-N -0.13 0.23 0.04 1.00 1.00 -0.18 0.92 NH4-N -0.16 0.13 0.02 0.92 0.88 -0.11 TC 0.49 0.42 0.19 -0.18 -0.18 TN -0.11 0.25 0.11 1.00 DIN -0.13 0.23 0.04 DON 0.27 -0.05 35 Figure 2.8: Leachate DOM Interaction Plots Figure 2.8: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, carbohydrates, lignin, proteins, tannins, unsaturated hydrocarbons, condensed hydrocarbons, and the relative percentage of N containing compounds) within the leachate DOM samples. 36 Protein and tannins comprised the next largest fraction of DOM in almost all the leachate samples; 89% of samples had protein as the 2nd or 3rd largest DOM fraction and 85% of samples had tannins as the 2nd or 3rd largest DOM fraction. In all samples there were compounds that could not be assigned a biochemical compound classification, accounting for 19-29% of compounds within the DOM samples. Van Krevelan diagrams indicate fewer unique compounds (represented as individual points on the diagrams) in 90cm leachate samples and illustrate a general change of DOM composition with increasing sampling depth (Figure 2.7). A higher proportion of compounds are plotted in the center rectangle (lignin) of the Van Krevelan diagram for the 90cm samples compared to the 60cm and 30cm diagrams. In comparison, fewer compounds were plotted in the two top right rectangles (amino sugars and carbohydrates) with increasing depth. Pearson correlations were conducted to determine the statistical significance of this shift in DOM composition (Table 2.5). There was a significant (p < 0.05) increase in the relative percentage of lipids (r=0.76), lignin (r= 0.61), and proteins (r=0.55) with depth, and a decrease in the relative percentage of amino sugars (r=-0.48), condensed hydrocarbons (r=-0.80), carbohydrates (r=- 0.72), and tannins (r=-0.88) with increasing sampling depth (Table 2.5). Depth also had a significant negative relationship with TC (r =-0.44), the relative percent of highly photodegradable compounds (AI> 0.67) (r=-0.62), and the average DBE/C of the sample (r=- 0.82), indicating that more recalcitrant compounds were observed in leachate samples collected from deeper in the soil profile. There was a slight shift in the composition of leached DOM throughout the growing season (Table 2.4 and Figure 2.8). Though not statistically significant, there was an observed decrease in the relative percentage of lipids (F=2.539, p=0.138), amino sugars (F=1.546, 37 p=0.249), proteins (F=3.773, p=0.053), and carbohydrates (F=3.377, p=0.084) (Figure 2.8 a, b, c, & e). There was a slight increase in the relative percentage of lignin (F=1.263, p=0.322) (Figure 2.8d). Van Krevelan diagrams comparing the composition of samples under the different cover cropping strategies appear very similar in spread (Figure 2.7), and Repeated Measure ANOVAs indicate that the values for most of the composition classes were similar between the cover cropping treatments, with exception to amino sugars, proteins, and lignin (Figure 2.8). Though not significant (F=2.758, p=0.125), amino sugars comprised a higher relative percentage of leached DOM in cover cropped plots with the highest values reported in the mixed cover plots (Figure 2.8b). Proteins comprised a higher relative percentage (F=3.362, p=0.106) of leached DOM in fallow plots at the beginning of the sampling season and quickly declined through time (cover*time, F=2.524, p=0.096) (Figure 2.8e). At all sampling dates, the cereal cover plots reported the highest relative percentage of lignin followed by fallow and mixed cover plots. Analysis of the photodegradability metrics also revealed a slight decrease in percentage of highly aromatic compounds over time (F=1.748, p=0.228) (Figure 2.9a). Data points clustered by sampling depth in PCoA; most of the 30cm samples are loosely clustered (Figure 2.10). Samples also clustered more based upon cover class at lower sampling depths, indicating that cover class may have more influence over composition of leached DOM at lower depths (Figure 2.10). Leachate DON: Fourteen to thirty one percent of identified compounds were present in the DON pool. As with the DOM pool, the majority of DON was comprised of lignin (70 - 91% of the N containing compounds across all the leachate samples) (Table 2.6). 38 Figure 2.9: Leachate Photodegradability Interaction Plots Figure 2.9: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage highly photodegradable compounds (AI>0.67) and the average DBE/C ratio within the leachate DOM and DON samples. 39 Figure 2.10: Leachate DOM PCoA Figure 2.10. The above figure illustrates the results of the Principal Coordinate Analysis conducted on the FT-ICR-MS dataset. The shapes of the data points indicate the cover cropping strategy of the sample plot, and the points are shaded based on the sampling depth of the leachate. 40 None of the DON samples contained carbohydrates or unsaturated hydrocarbons. There was variability in the other biochemical classes between the leachate samples (Table 2.6). Van Krevelan diagrams (Figure 2.12) and Pearson correlations (Table 2.7) reveal a similar relationship between sampling depth and the DON composition as was seen in the DOM pool. As sampling depth increased, fewer datapoints clustered in the amino sugar and carbohydrate regions of the Van Krevelan diagrams and a relatively higher proportion of datapoints clustered in the central, lignin region (Figure 2.12). Depth was significantly correlated (p<0.05) with a decrease in TC (r=-0.44), total peaks (r=-0.82), the relative percentage of N containing compounds (r=-0.80), and tannins (r=-0.87), as well as an increase in the relative percent of amino sugars (r=-0.46) (Table 2.7). There was also an increase in the relative percent of lipids (r=0.56), lignin (r=0.36), and proteins (r=0.48) and a decrease in the relative percent of condensed hydrocarbons (r=-0.77) with depth (Table 2.7). As with DOM, sampling depth was negatively correlated with photodegradability metrics (average DBEC, r=-0.82; percent of compounds with AI> 0.67, r=-0.62) (Table 2.7). There were non-significant decreases in the relative percent of lipids (F=1.821, p=0.206) and proteins (F=2.134, p=0.171) (Figure 2.11 a & d) in the N-containing DOM compounds, and an increase in the relative percent of lignin (F=1.599, p=0.249) (Figure 2.11c). Unlike the overall DOM pool, there was also a decrease in the percentage of condensed hydrocarbons (F=3.151, p=0.097) (Table 2.6 and Figure 2.11e). Cover cropping did not influence most of the compositional classes, but amino sugars comprised a higher percentage (F=3.246, p=0.083) of cover cropped plots (Mixed>Cereal>Fallow) (Figure 2.11b). The percentage of highly aromatic, recalcitrant N- containing compounds also decreased over time (F=1.748, p=0.228) (Figure 2.9b). 41 Table 2.6: The sample number, sampling date, cover cropping strategy, DOM FT-ICR-MS peaks (indicating the total number of unique compounds), the relative percentage of each compositional class (lignin, condensed hydrocarbons, unclassifiable compounds, lipids, tannins, proteins, and amino sugars), the average DBE/C ratio and the percentage of compounds with an AI> 0.67. Leachate DON Composition Relative Percentage Con. Amino % # Depth Date Cover Lig. Hydro. Other Lip. Tan. Pro. Sugars DBE/C AI 15 1 Mixed 74.0 10.3 0.3 0.7 5.8 4.7 4.3 0.5 +/- 0.2 2.5 18 Cereal 77.7 5.7 0.3 1.5 5.4 4.1 5.3 0.5 +/- 0.2 1.8 20 Fallow 77.3 7.5 0.0 1.3 8.0 3.1 2.9 0.5 +/- 0.2 1.8 21 2 Mixed 74.0 9.6 0.1 0.9 6.4 4.2 4.8 0.4 +/- 0.2 2.2 22 Fallow 73.2 9.1 0.5 0.6 7.6 4.5 4.4 0.4 +/- 0.2 2.3 25 30 Mixed 77.7 5.4 0.3 0.8 7.0 4.5 4.3 0.5 +/- 0.2 1.9 28 Cereal 81.1 3.6 0.5 1.0 7.3 4.8 1.7 0.5 +/- 0.1 0.5 31 Mixed 75.1 8.5 0.4 0.8 6.6 3.9 4.7 0.5 +/- 0.2 2.0 32 3 Fallow 80.5 6.7 0.5 1.2 5.5 3.6 2.1 0.5 +/- 0.2 1.9 34 Cereal 82.4 5.4 0.1 0.1 7.1 2.8 2.2 0.5 +/- 0.2 1.1 37 Mixed 77.5 6.1 0.3 1.5 6.5 4.3 3.8 0.5 +/- 0.2 1.5 13 1 Cereal 81.4 2.0 0.7 2.1 4.0 6.6 3.3 0.5 +/- 0.2 0.6 26 2 Mixed 78.0 2.5 0.8 2.3 7.4 3.9 5.2 0.5 +/- 0.2 0.8 29 60 Cereal 87.2 0.8 0.5 0.5 3.4 6.1 1.5 0.5 +/- 0.1 0.4 35 3 Cereal 77.6 6.6 0.4 1.8 5.4 5.2 3.1 0.5 +/- 0.2 1.7 38 Mixed 89.3 0.7 0.2 1.3 4.9 2.6 1.1 0.5 +/- 0.1 0.2 14 Cereal 80.2 2.7 0.7 1.5 3.7 7.1 4.1 0.5 +/- 0.2 0.9 16 1 Fallow 75.8 1.6 3.2 8.6 3.2 6.5 1.1 0.5 +/- 0.2 0.5 17 Cereal 85.2 2.0 0.5 1.0 3.8 5.9 1.8 0.5 +/- 0.2 0.5 19 Cereal 89.5 2.1 0.8 0.0 3.0 2.9 1.7 0.5 +/- 0.1 0.8 23 Fallow 81.1 1.3 3.1 6.1 3.1 4.8 0.4 0.5 +/- 0.2 0.4 2 24 90 Cereal 81.3 2.7 0.2 1.3 4.4 7.8 2.5 0.5 +/- 0.2 1.3 27 Mixed 75.0 2.6 2.0 4.9 2.3 9.1 4.2 0.4 +/- 0.2 0.9 30 3 Cereal 84.3 1.5 0.5 3.8 3.1 5.6 1.2 0.5 +/- 0.1 0.3 33 Fallow 83.5 1.3 2.5 6.8 3.4 2.5 0.0 0.5 +/- 0.2 0.0 36 Cereal 90.9 1.1 0.2 0.7 2.3 3.9 1.0 0.5 +/- 0.1 0.5 39 Mixed 69.8 4.2 2.3 7.6 2.3 9.1 4.9 0.4 +/- 0.2 1.0 42 Table 2.7: The results of Pearson Correlations comparing the sampling depth, the relative percent of N containing peaks, relative percent of highly photodegradable compounds (AI>0.67), the average DBE/C ratio, the amount of DON, TN, TC, NH4-N, and NO3-N, the relative percentage of each compositional class (tannins, lignin, amino sugars, proteins, condensed hydrocarbons, lipids) with the leached DON and the number of N peaks. Bolded values within the chart indicate statistically significant correlations (p<0.5). Leachate DON Pearson Correlations DON Avg NH4- NO3- Amino Con N % AI Frac. DBE/C DON DIN TN TC N N Tan. Lig. Sugars Pro. Hydro. Lip. peaks Depth -0.80 -0.62 -0.82 -0.27 -0.14 -0.15 -0.44 -0.10 -0.14 -0.87 0.36 -0.46 0.48 -0.77 0.56 -0.90 N peaks 0.88 0.70 0.85 0.14 0.31 0.32 0.45 0.24 0.31 0.83 -0.30 0.52 -0.37 0.76 -0.70 Lip. -0.68 -0.46 -0.72 0.09 -0.24 -0.25 -0.32 -0.18 -0.24 -0.52 -0.29 -0.22 0.41 -0.37 Con Hydro. 0.49 0.84 0.69 0.23 -0.03 -0.02 0.59 -0.07 -0.03 0.68 -0.67 0.63 -0.22 Pro. -0.47 -0.14 -0.70 -0.03 -0.19 -0.21 -0.25 -0.09 -0.20 -0.50 -0.36 0.24 Amino Sugars 0.14 0.57 0.22 0.00 -0.25 -0.26 0.52 -0.19 -0.25 0.42 -0.73 Lig. 0.02 -0.50 -0.04 -0.19 0.24 0.25 -0.34 0.18 0.25 -0.35 Tan. 0.74 0.57 0.80 -0.01 0.17 0.17 0.40 0.16 0.17 NO3-N 0.56 -0.13 0.23 0.04 1.00 1.00 -0.18 0.92 NH4-N 0.48 -0.16 0.13 0.02 0.92 0.88 -0.11 TC 0.26 0.49 0.42 0.19 -0.18 -0.18 TN 0.57 -0.11 0.25 0.11 1.00 DIN 0.56 -0.13 0.23 0.04 DON 0.08 0.27 -0.05 Avg DBEC 0.83 0.62 % AI 0.46 43 Figure 2.11: Leachate DON Interaction Plots Figure 2.11: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, lignin, proteins, condensed hydrocarbons and tannins) within the leachate DON samples. 44 Figure 2.12: Leachate DON Van Krevelan Diagrams Sampling Depth Cover Cropping Strategy Figure 2.12. The figure is comprised of two sets of Van Krevelan Diagrams illustrating the DON composition of the leachate samples. One separates the DON based on the sampling depth of the leachate, and one separates the samples based upon the cover cropping strategy of the given sample plot. The graphs are also color coded based upon the aromaticity of the compounds (red indicating highly labile and green indicating highly recalcitrant). 45 Analysis of the DON fraction reveals both a significant temporal effect (F=5.140, p=0.035) and sampling date and cover interaction (F=4.105, p=0.040) (Figure 2.8i). Soil DOM: There were differences in DOM composition between the two soil extractions techniques (Table 2.8). Of the classifiable compounds, water extracted DOM was primarily comprised of lignin (11 - 30%), followed by lipids (3-10%), proteins (5 - 22%), tannins (0.6 ? 4%), unsaturated hydrocarbons (0.8 ? 4%), condensed hydrocarbons (1 - 3%), amino sugars (1 ? 3%), and carbohydrates (0.4 ? 1%). In comparison, the chloroform extractable DOM was primarily lipids (6 ? 29%) followed by proteins (3 ? 11%), lignin (2? 9%), unsaturated hydrocarbons (0.3 ? 1%), condensed hydrocarbons (0.2 ? 2%), amino sugars (0.3 ? 1.5%), carbohydrates (0 ? 1.6%), and tannins (0 ? 1%). A large portion of all samples were not identified in either extraction (water extractable: 39 ? 70%; chloroform extractable: 56 ? 83%). Comparison via Van Krevelan diagrams visually confirms this difference in DOM composition: the compounds in the water extractable soil DOM are heavily clustered in the center region (lignin) of the plot while the compounds in the chloroform extractable soil DOM are clustered in the upper left region (lipids) of the plot (Figure 2.16). There was no correlation between microbial biomass N, but microbial biomass C was correlated with changes in DOM composition in both the water and chloroform extractions (Tables 2.9 and 2.10). In water extractable DOM, there was a positive correlation between proteins (r=0.67), amino sugars (r=0.65), and tannins (r=0.65) and a negative correlation with unsaturated hydrocarbons (r=-0.65), carbohydrates (r=-0.65), and photodegradability (% of AI>0.67, r=-0.65) (Table 2.9). 46 Table 2.8: The sample number, sampling date, plot number, DOM FT-ICR-MS peaks (indicating the total number of unique compounds), extraction method (water or chloroform), cover cropping strategy, the relative percentage of each compositional class (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amino sugars, carbohydrates, lignin, tannins, and unclassifiable compounds), the average DBE/C ratio and the percentage of compounds with an AI> 0.67. Soil DOM Composition Relative Percentage UnSat. Con. Amino Comp. % # Date Plot Peaks Extract. Cover Lip, Hydro. Hydro. Pro. Sugars Carb. Lig. Tan. NA DBE/C AI 40 1 4276 Cereal 9.7 3.7 1.9 5.4 0.9 1.2 12.5 0.6 64.1 0.3 +/- 0.2 3.1 41 2 4317 Fallow 7.2 3.7 2.9 5.1 1.0 1.2 14.3 1.5 63.3 0.4 +/- 0.2 4.7 42 3 4056 Mixed 5.4 2.3 2.7 4.7 1.2 1.2 11.0 1.5 70.0 0.4 +/- 0.2 5.0 1 43 4 3127 Fallow 6.5 1.2 1.9 21.5 2.9 0.4 24.9 3.8 37.1 0.4 +/- 0.2 0.4 44 5 3443 Cereal 5.6 0.8 2.3 19.6 2.6 0.6 25.4 4.4 38.8 0.4 +/- 0.2 0.1 45 6 3740 Mixed 5.7 1.8 1.7 19.6 2.9 0.8 23.0 4.0 40.6 0.4 +/- 0.2 0.4 Water 46 1 2771 Cereal 7.2 0.7 1.3 19.7 2.5 0.5 23.9 3.0 41.3 0.3 +/- 0.2 0.7 47 2 2979 Fallow 5.7 1.2 1.9 19.8 2.2 0.4 29.9 4.4 34.5 0.4 +/- 0.20 0.5 48 3 9750 Mixed 3.0 0.8 2.3 7.5 1.2 0.7 14.0 2.4 68.0 0.4 +/- 0.2 0.9 3 49 4 3121 Fallow 5.5 1.2 2.8 19.5 2.7 0.5 29.6 4.2 34.0 0.4 +/- 0.20 0.4 50 5 3228 Cereal 6.8 1.8 1.1 21.6 3.2 0.4 23.4 2.7 39.0 0.3 +/- 0.20 0.3 51 6 3739 Mixed 5.3 1.3 2.5 18.8 2.6 0.6 28.1 4.20 36.7 0.4 +/- 0.20 0.2 40 1 1944 Cereal 5.7 0.8 1.9 3.3 0.6 1.6 2.2 0.6 83.4 0.3 +/- 0.3 3.3 41 2 970 Fallow 23.5 1.1 0.2 5.7 0.3 0.5 5.1 0.1 63.5 0.2 +/- 0.2 0.3 42 3 2163 Mixed 18.9 0.9 1.0 10.2 1.5 0.4 6.0 0.8 60.3 0.2 +/- 0.2 1.2 1 43 4 1987 Fallow 19.8 0.4 0.5 7.6 0.9 0.3 8.0 0.7 62.0 0.2 +/- 0.2 0.8 44 5 1067 Cereal 29.0 0.5 0.3 8.4 1.2 0 3.6 0.4 56.7 0.2 +/- 0.1 0.7 45 6 2090 Mixed 22.9 0.4 1.4 6.3 0.7 0.2 5.6 0.7 62.0 0.2 +/- 0.2 1.7 Chlor. 46 1 758 Cereal 29.8 1.1 0.3 6.6 0.4 0.9 5.4 0 55.5 0.2 +/- 0.2 0.3 47 2 651 Fallow 19.4 0.8 0.2 3.1 0.3 0.3 2.6 0.2 73.3 0.2 +/- 0.2 0.6 48 3 1846 Mixed 18.3 0.3 1.8 5.5 0.7 0.2 5.6 1.1 66.4 0.3 +/- 0.2 2.7 3 49 4 1759 Fallow 20.0 0.4 1.3 7.3 0.9 0.4 5.3 0.6 63.9 0.2 +/- 0.2 2.0 50 5 1163 Cereal 17.1 0.3 1.7 4.8 0.7 0.4 4.7 1.0 69.2 0.3 +/- 0.2 3.3 51 6 1882 Mixed 16.7 1.6 1.5 11.1 1.3 0.5 8.7 0.4 58.2 0.3 +/- 0.2 1.0 47 Table 2.9: The results of Pearson Correlations comparing the sulfur containing compounds, the relative percentage of each compositional class in the soil water extractions (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amino sugars, carbohydrates, lignin, tannins, and unknown compounds), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5). Soil Water DOM Pearson Correlations KCl KCl Water Water Micro. Micro. K2SO4 K2SO4 NH4- NO3- Water Water NH4- NO3- % Bio. N Bio. C TN TC PMN N N DON DIN TN TC N N AI DBEC CHOS -0.30 -0.74 -0.49 0.59 0.59 -0.26 -0.39 -0.40 -0.36 -0.40 0.23 -0.39 -0.36 0.93 0.06 CHONS -0.29 -0.74 -0.51 0.59 0.58 -0.34 -0.40 -0.42 -0.38 -0.43 0.26 -0.41 -0.38 0.94 0.10 CHOSP -0.30 -0.78 -0.52 0.58 0.44 -0.35 -0.40 -0.47 -0.37 -0.46 0.18 -0.33 -0.37 0.85 0.02 CHONSP -0.22 -0.71 -0.57 0.58 0.58 -0.41 -0.47 -0.39 -0.46 -0.42 0.29 -0.57 -0.46 0.93 0.07 Lip. -0.24 -0.39 -0.35 0.33 0.23 -0.03 -0.31 -0.22 -0.29 -0.24 -0.09 -0.39 -0.29 0.32 -0.69 UnSat. Hydro. -0.26 -0.65 -0.43 0.57 0.52 -0.10 -0.33 -0.41 -0.30 -0.40 0.14 -0.29 -0.30 0.78 -0.08 Con. Hydro. 0.07 -0.37 0.01 0.30 0.30 -0.10 0.06 -0.08 0.08 -0.05 0.22 -0.01 0.09 0.47 0.81 Pro. 0.27 0.67 0.40 -0.44 -0.38 0.48 0.29 0.40 0.27 0.39 -0.31 0.19 0.27 -0.88 -0.46 Amino Sugars 0.33 0.65 0.43 -0.38 -0.35 0.53 0.34 0.45 0.32 0.43 -0.39 0.15 0.32 -0.82 -0.48 Carb. -0.08 -0.65 -0.50 0.62 0.49 -0.23 -0.38 -0.42 -0.37 -0.42 0.34 -0.42 -0.36 0.90 0.23 Lig. 0.18 0.57 0.47 -0.51 -0.40 0.52 0.35 0.44 0.35 0.43 -0.26 0.33 0.34 -0.82 -0.20 Tan. 0.45 0.66 0.35 -0.36 -0.29 0.48 0.23 0.28 0.22 0.28 -0.05 0.23 0.22 -0.82 0.02 Comp. NA -0.23 -0.58 -0.40 0.42 0.34 -0.54 -0.29 -0.40 -0.28 -0.38 0.29 -0.21 -0.28 0.82 0.39 DBEC -0.03 -0.12 0.14 -0.06 0.06 -0.21 0.15 -0.04 0.16 0.00 0.28 0.29 0.15 0.25 % AI -0.24 -0.69 -0.47 0.57 0.66 -0.26 -0.37 -0.30 -0.36 -0.32 0.35 -0.40 -0.35 Water NO3-N -0.24 0.30 0.98 -0.67 -0.65 0.30 1.00 0.84 1.00 0.90 -0.65 0.77 Water NH4-N -0.46 0.15 0.77 -0.76 -0.71 0.19 0.76 0.55 0.78 0.61 -0.50 Water TC 0.43 0.13 -0.62 0.52 0.60 -0.03 -0.64 -0.51 -0.65 -0.56 Water TN -0.22 0.33 0.92 -0.70 -0.57 0.48 0.90 0.99 0.89 DIN -0.25 0.29 0.98 -0.67 -0.66 0.30 1.00 0.84 DON -0.21 0.33 0.87 -0.68 -0.53 0.51 0.85 KCl NO3-N -0.24 0.33 0.99 -0.69 -0.66 0.29 KCl NH4-N 0.29 0.31 0.33 -0.13 -0.13 PMN 0.23 -0.29 -0.69 0.78 K2SO4 TC 0.50 -0.36 -0.76 K2SO4 TN -0.22 0.42 Micro. Bio. C 0.47 48 Table 2.10: The results of Pearson Correlations comparing the sulfur containing compounds, the relative percentage of each compositional class in the soil chloroform extractions (lipids, unsaturated hydrocarbons, condensed hydrocarbons, proteins, amino sugars, carbohydrates, lignin, tannins, and unknown compounds), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5). Soil Chloroform DOM Pearson Correlations Micro. Micro. K2SO4 K2SO4 KCl KCl Water Water Water Water Bio. N Bio. C TN TC PMN NH4-N NO3-N DON DIN TN TC NH4-N NO3-N % AI DBEC CHOS -0.59 -0.16 0.00 -0.21 -0.05 -0.56 0.05 -0.05 0.02 -0.04 -0.10 0.10 0.02 0.27 0.32 CHONS 0.34 -0.03 0.19 0.30 -0.14 0.22 0.31 0.12 0.33 0.16 -0.12 0.16 0.33 0.69 0.62 CHOSP -0.29 -0.49 -0.18 0.13 -0.31 -0.31 -0.08 -0.27 -0.05 -0.23 -0.21 0.12 -0.05 0.71 0.77 CHONSP -0.30 -0.13 0.19 -0.11 -0.44 -0.17 0.29 0.06 0.30 0.11 -0.34 0.38 0.30 0.93 0.84 Lip. 0.46 0.62 0.03 -0.08 0.19 0.05 -0.07 0.14 -0.12 0.09 0.32 -0.42 -0.11 -0.71 -0.87 UnSat. Hydro. -0.47 -0.63 -0.22 -0.08 0.10 -0.04 -0.25 -0.06 -0.23 -0.10 -0.11 -0.16 -0.23 -0.44 -0.04 Cond. Hydro. -0.17 -0.21 0.34 -0.16 -0.55 0.12 0.43 0.17 0.45 0.23 -0.52 0.51 0.45 0.89 0.89 Pro. 0.26 -0.21 0.04 0.11 0.08 0.13 0.03 0.18 0.02 0.15 -0.19 -0.23 0.03 -0.38 -0.10 Amino Sugars 0.36 -0.32 -0.02 0.27 0.05 0.09 0.02 0.08 0.04 0.07 -0.24 -0.11 0.04 0.01 0.17 Carb. -0.53 -0.58 -0.19 0.04 -0.16 -0.17 -0.13 -0.09 -0.11 -0.10 -0.20 -0.07 -0.11 0.33 0.53 Lig. -0.11 -0.06 0.19 -0.17 -0.07 0.05 0.17 0.20 0.15 0.20 -0.32 0.03 0.15 -0.26 0.08 Tan. 0.08 0.06 0.28 -0.01 -0.27 -0.06 0.37 0.11 0.38 0.17 -0.28 0.41 0.38 0.76 0.62 Comp. NA -0.38 -0.32 -0.10 0.07 -0.10 -0.09 -0.03 -0.23 0.01 -0.19 -0.03 0.35 0.00 0.64 0.56 DBEC -0.38 -0.49 0.14 0.00 -0.38 -0.09 0.25 0.00 0.28 0.06 -0.52 0.38 0.28 0.84 % AI -0.17 -0.08 0.32 -0.11 -0.48 0.00 0.42 0.13 0.44 0.20 -0.46 0.49 0.44 Water NO3-N -0.24 0.30 0.98 -0.67 -0.65 0.30 1.00 0.84 1.00 0.90 -0.65 0.77 Water NH4-N -0.46 0.15 0.77 -0.76 -0.71 0.19 0.76 0.55 0.78 0.61 -0.50 Water TC 0.43 0.13 -0.62 0.52 0.60 -0.03 -0.64 -0.51 -0.65 -0.56 Water TN -0.22 0.33 0.92 -0.70 -0.57 0.48 0.90 0.99 0.89 DIN -0.25 0.29 0.98 -0.67 -0.66 0.30 1.00 0.84 DON -0.21 0.33 0.87 -0.68 -0.53 0.51 0.85 KCl NO3-N -0.24 0.33 0.99 -0.69 -0.66 0.29 KCl NH4-N 0.29 0.31 0.33 -0.13 -0.13 PMN 0.23 -0.29 -0.69 0.78 K2SO4 TC 0.50 -0.36 -0.76 K2SO4 TN -0.22 0.42 Micro. Bio. C 0.47 49 Figure 2.13: Soil Water DOM Interaction Plots Figure 2.13: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, carbohydrates, lignin, proteins, tannins, unsaturated hydrocarbons, condensed hydrocarbons, and the relative percentage of N containing compounds) within the leachate DOM samples. 50 Figure 2.14: Soil Chloroform DOM Interaction Plots Figure 2.14: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, carbohydrates, lignin, proteins, tannins, unsaturated hydrocarbons, condensed hydrocarbons, and the relative percentage of N containing compounds) within the leachate DOM samples. 51 Figure 2.15: Soil DOM and DON Photodegradability Interaction Plots Soil Water Extractions Soil Chloroform Extractions c d e f g h Figure 2.15: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage highly photodegradable compounds (AI>0.67) and the average DBE/C ratio within the Soil Water extractable and Soil Chloroform extractable DOM and DON samples. 52 Figure 2.16: Soil Extractable Organic Matter Van Krevelan Diagrams (by Cover Cropping Strategy) Water Extractable Soil DOM Chloroform Extractable Soil DOM Figure 2.16. The above figure is comprised of two sets of Van Krevelan Diagrams illustrating the composition of the extractable soil organic matter composition of the leachate samples. One set represents the water extractable DOM pool while the other set represents the chloroform extractable DOM pool. The graphs are also color coded based upon the aromaticity of the compounds (red indicating highly labile and green indicating highly recalcitrant). 53 In the chloroform extractions a similar decrease in unsaturated hydrocarbons (r=-0.63) and carbohydrates (r=0.58) was reported, along with a positive correlation with the relative percentage of lipids (r=0.62) (Table 2.10). Potentially mineralizable N was associated with a shift in composition as well, significantly correlated with an increase in S containing compounds (CHOS, r=0.59; CHONS, r=0.60; CHONSP, r=0.58) as well as with an increase in photodegradability (relative percentage AI>0.67, r=0.66) (Table 2.10). There was no temporal change in the composition of soil DOM (Figures 2.13 and 2.14) . The only trend visible in the chloroform extractable DOM pool was an increase in the relative percentage of lignin in cover cropped plots, and a decrease in the lignin pool in the fallow plots (sampling date* cover, F=4.645, p=0.121) (Table 2.8 and Figure 2.14d). Though not significant, cover had a slight impact on the relative percentages of chloroform extractable lignin (F=1.940, p=0.288), condensed hydrocarbons (F=5.028, p=0.110), and proteins (F=1.840, p=0.301) (Figure 2.14 d, h, & e). In each of these examples, the cereal and fallow plots had similar values, while the mixed cover plots reported higher relative percentages for the given compound classes. Clearer trends were observed in the water extractable DOM pool (Table 2.8 and Figure 2.13). Though non-significant, there was a slight decrease in the relative percentage of water extractable lipids (F= 2.426, p=0.217), amino sugars (F=2.667, p=0.201), carbohydrates (F=3.282, p=0.168), unsaturated hydrocarbons (F=1.905, p=0.261) (Figure 2.13 a, b, c, & g) and the overall percentage of highly aromatic compounds (F=2.949, p=0.184) (Figure 2.15a). There was also a slight increase in lignin (F=4.741, p=0.118) and proteins (F=2.216, p=0.233) (Figure 2.15 d & e). Cover did not have much impact on the DOM composition, and Van Krevelan diagrams (Figure 2.16) of the DOM under the three cover classes reveal similar compositions among the treatments. 54 There was considerable variability in PCoA coordinates that represented the June water extractable DOM samples, but the July water extractable DOM coordinates were tightly clustered (Figure 2.17). The influence of the cover cropping on the DOM composition may have lessened throughout the growing season. There was evidence for more clustering by cover cropping in the PCoA of chloroform extractable DOM (Figure 2.18). There coordinates varied more between fallow and cereal covered plots, with less variability in the mixture. This trend is presumably due to the inclusion of legumes in the mixed cover crop. Soil DON: There were similar temporal trends in the DON composition as to the overall DOM pool (Table 2.11), but nothing of statistical significance in the chloroform extractable pool (Figure 2.20). In the water extractable pool there was a significant increase in the relative percentage of lignin (F=11.467, p=0.043) and a slight increase in tannins (F=7.345, p=0.073) over time (Figure 2.19 c & f). Though not significant, there was a decrease in lipids (F=4.362, p=0.128) (Figure 2.19a) and the percentage of aromatic compounds (F=3.420, p=0.162) (Figure 2.15b). Cover class had no clear impact on DON composition. There was no correlations between either water or chloroform extractable DON and either cover cropping strategy (Figures 2.19 and 2.20, Tables 2.12 and 2.13) but there was significantly more N peaks (p<0.05) from plots cover cropped with legumes (Figure 2.14i). There was also a significant relationship between microbial biomass C and water extractable DON composition (p<0.05): a decrease in condensed hydrocarbons (r=-0.59) and lipids (r=- 0.75), an increase in in proteins (r=0.70) and amino sugars (r=0.73) (Table 2.12). 55 Figure 2.17: Soil Water Extractable DOM PCoA Figure 2.17. The above figure illustrates the results of the Principal Coordinate Analysis conducted on the soil water extractable DOM FT-ICR-MS dataset. The shapes of the data points indicate the cover cropping strategy of the sample plot, and the points are shaded based on the sampling date. 56 Figure 2.18 Soil Chloroform Extractable DOM PCoA Figure 2.18. The above figure illustrates the results of the Principal Coordinate Analysis conducted on the soil chloroform extractable DOM FT-ICR-MS dataset. The shapes of the data points indicate the cover cropping strategy of the sample plot, and the points are shaded based on the sampling date. 57 Table 2.11: The sample number, sampling date, plot number, DOM FT-ICR-MS peaks (indicating the total number of unique compounds), extraction method (water or chloroform), cover cropping strategy, the relative percentage of each compositional class (lignin, condensed hydrocarbons, unclassifiable compounds, lipids, proteins, and amino sugars), the average DBE/ ration and the percentage of compounds with an AI> 0.67. Soil DON Composition Relative Percentage Con. Amino % # Date Plot Cover Extract. Lig. Hydro. Other Lip. Tan. Pro. Sugars DBE/C AI 40 1 Cereal 24.0 6.0 3.9 28.2 0.5 16.9 3.3 0.3 +/- 0.2 2.7 41 2 Fallow 32.2 11.4 3.7 18.9 1.5 11.8 3.0 0.4 +/- 0.2 6.7 42 3 Mixed 30.4 13.1 5.4 19.1 2.0 11.6 4.7 0.4 +/- 0.2 8.7 1 43 4 Fallow 36.9 3.0 1.0 6.2 2.1 32.9 10.8 0.4 +/- 0.2 1.1 44 5 Cereal 38.6 2.0 1.0 6.5 3.5 32.8 9.3 0.4 +/- 0.2 0.2 45 6 Mixed 33.8 2.5 0.8 5.4 4.7 32.0 9.5 0.4 +/- 0.2 1.1 Water 46 1 Cereal 38.7 3.9 1.1 8.9 1.7 30 9.35 0.4 +/- 0.2 1.3 47 2 Fallow 46.2 3.8 0.8 4.4 3.2 27.1 7.8 0.4 +/- 0.2 1.5 48 3 Mixed 45.1 8.4 1.0 7.8 5.1 16.8 6.7 0.4 +/- 0.2 2.0 3 49 4 Fallow 46.8 5.2 1.0 3.4 4.2 24.4 8.3 0.4 +/- 0.2 0.8 50 5 Cereal 35.1 2.2 0.5 5.6 3.1 32.4 11.4 0.4 +/- 0.2 0.5 51 6 Mixed 47.2 4.6 0.7 4.1 5.3 23.2 7.6 0.4 +/- 0.2 0.1 40 1 Cereal 6.8 18.8 3.4 22.2 9.4 18.0 6.8 0.3 +/- 0.3 3.3 41 2 Fallow 12.5 0 0 37.5 4.2 37.5 4.2 0.2 + /- 0.2 0.3 42 3 Mixed 22.0 7.6 3.8 33.3 9.9 9.1 7.6 0.2 +/- 0.2 1.2 1 43 4 Fallow 25.8 4.1 4.1 36.1 10.3 12.4 4.1 0.2 +/- 0.2 0.8 44 5 Cereal 22.6 0 3.2 45.2 9.7 16.1 3.2 0.2 +/- 0.1 0.7 45 6 Mixed 17.4 9.2 4.6 37.6 11.9 10.0 3.7 0.2 +/- 0.0 1.7 Chlor. 46 1 Cereal 10.0 0 0 30 0 45 0 0.2 +/- 0.2 0.3 47 2 Fallow 13.3 6.7 6.7 26.7 6.7 20 6.7 0.2 +/- 0.2 0.6 48 3 Mixed 23.4 12.3 6.1 26.3 14.9 11.4 3.5 0.3 +/- 0.2 2.7 3 49 4 Fallow 14.6 7.3 6.4 39.1 9.1 13.6 4.6 0.2 +/- 0.2 2.0 50 5 Cereal 21.6 9.5 8.1 28.4 13.5 10.8 2.7 0.3 +/- 0.2 3.3 51 6 Mixed 20.0 4.7 0 29.4 4.7 14.1 11.8 0.3 +/- 0.2 1.0 58 Table 2.12: The results of Pearson Correlations comparing the number of N peaks, the relative percentage of each compositional class (lignin, condensed hydrocarbons, lipids tannins, proteins, and amino sugars), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 extractable TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5). Soil Water DON Pearson Correlations KCl KCl Water Water DON Micro. Micro. K2SO4 K2SO4 NH4- NO3- Water Water NH4- NO3- % Avg. Frac. Bio. N Bio. C TN TC PMN N N DON DIN TN TC N N AI DBEC N Peaks -0.48 -0.20 -0.20 0.07 -0.07 -0.23 -0.52 0.13 -0.27 0.14 -0.19 -0.01 0.34 0.14 0.13 0.53 Lig. 0.19 -0.06 0.46 0.63 -0.74 -0.55 0.18 0.53 0.48 0.52 0.50 -0.20 0.63 0.52 -0.52 0.75 Cond. Hydro -0.32 -0.31 -0.59 -0.21 0.30 0.45 -0.29 -0.12 -0.11 -0.12 -0.11 0.30 -0.13 -0.12 0.92 0.38 Lip. -0.18 -0.25 -0.75 -0.58 0.60 0.44 -0.39 -0.46 -0.47 -0.44 -0.47 0.22 -0.42 -0.44 0.68 -0.38 Tan. 0.00 0.26 0.45 0.51 -0.46 -0.52 0.33 0.45 0.28 0.45 0.32 -0.20 0.53 0.44 -0.49 0.63 Pro. 0.22 0.41 0.70 0.19 -0.25 -0.29 0.32 0.09 0.18 0.07 0.16 -0.20 -0.01 0.07 -0.80 -0.38 Amino Sugar 0.07 0.31 0.73 0.40 -0.41 -0.37 0.31 0.31 0.39 0.28 0.38 -0.33 0.16 0.28 -0.72 -0.17 DBEC -0.02 -0.21 0.07 0.47 -0.46 -0.23 0.04 0.44 0.31 0.45 0.35 0.01 0.58 0.44 0.08 % AI -0.29 -0.17 -0.56 -0.42 0.51 0.71 -0.17 -0.34 -0.20 -0.34 -0.24 0.44 -0.38 -0.34 Water NO3-N 0.24 -0.24 0.30 0.98 -0.67 -0.65 0.30 1.00 0.84 1.00 0.90 -0.65 0.77 Water NH4-N 0.07 -0.46 0.15 0.77 -0.76 -0.71 0.19 0.76 0.55 0.78 0.61 -0.50 Water TC -0.37 0.43 0.13 -0.62 0.52 0.60 -0.03 -0.64 -0.51 -0.65 -0.56 Water TN 0.27 -0.22 0.33 0.92 -0.70 -0.57 0.48 0.90 0.99 0.89 DIN 0.24 -0.25 0.29 0.98 -0.67 -0.66 0.30 1.00 0.84 DON 0.27 -0.21 0.33 0.87 -0.68 -0.53 0.51 0.85 KCl NO3-N 0.21 -0.24 0.33 0.99 -0.69 -0.66 0.29 KCl NH4-N 0.48 0.29 0.31 0.33 -0.13 -0.13 PMN -0.05 0.23 -0.29 -0.69 0.78 K2SO4 TC -0.10 0.50 -0.36 -0.76 K2SO4 TN 0.25 -0.22 0.42 Micro. Bio. C -0.10 0.47 Micro. Bio. N -0.06 59 Table 2.13: The results of Pearson Correlations comparing the number of N peaks, the relative percentage of each compositional class (lignin, condensed hydrocarbons, lipids tannins, proteins, and amino sugars), the average DBE/C ratio, the relative percent of highly photodegradable compounds (AI>0.67), water extractable NO3-N, NH4-N, TC, TN, DIN, and DON, KCl extractable NO3-N, NH4-N, PMN, K2SO4 extractable TC and TN, and microbial biomass C and N. Bolded values within the chart indicate statistically significant correlations (p<0.5). Soil Chloroform DON Pearson Correlations KCl KCl Water Water DON Micro. Micro. K2SO4 K2SO4 NH4- NO3- Water Water NH4- NO3- Frac. Bio. N Bio. C TN TC PMN N N DON DIN TN TC N N % AI DBEC N Peaks 0.92 0.06 -0.29 0.09 0.22 -0.17 0.15 0.20 0.07 0.22 0.10 -0.17 0.21 0.22 0.62 0.76 Lig. 0.30 0.31 0.18 0.10 0.04 0.06 -0.14 0.10 0.00 0.08 0.02 -0.17 0.07 0.08 -0.01 -0.03 Cond. Hydro 0.73 -0.20 -0.29 0.01 0.05 -0.34 0.02 0.11 -0.10 0.14 -0.05 -0.14 0.42 0.14 0.87 0.83 Other 0.52 0.12 0.42 0.41 -0.21 -0.26 0.26 0.44 0.30 0.45 0.34 -0.09 0.58 0.44 0.59 0.21 Lip. -0.24 0.76 0.40 0.00 0.40 0.42 0.22 -0.03 0.00 -0.04 -0.01 0.26 -0.47 -0.04 -0.48 -0.57 Tan. 0.72 0.30 0.18 0.13 0.16 -0.10 -0.03 0.21 -0.09 0.22 -0.03 -0.08 0.31 0.21 0.69 0.45 Pro. -0.71 -0.32 0.02 -0.14 -0.14 0.14 -0.27 -0.19 -0.02 -0.22 -0.06 0.19 -0.36 -0.22 -0.52 -0.46 Amino Sugar 0.16 -0.24 -0.69 -0.12 0.08 0.02 0.18 -0.10 -0.12 -0.05 -0.11 -0.18 0.24 -0.06 0.01 0.36 DBEC 0.82 -0.38 -0.49 0.14 0.00 -0.38 -0.09 0.25 0.00 0.28 0.06 -0.52 0.38 0.28 0.84 % AI 0.82 -0.17 -0.08 0.32 -0.11 -0.48 0.00 0.42 0.13 0.44 0.20 -0.46 0.49 0.44 Water NO3-N 0.48 -0.24 0.30 0.98 -0.67 -0.65 0.30 1.00 0.84 1.00 0.90 -0.65 0.77 Water NH4-N 0.41 -0.46 0.15 0.77 -0.76 -0.71 0.19 0.76 0.55 0.78 0.61 -0.50 Water TC -0.42 0.43 0.13 -0.62 0.52 0.60 -0.03 -0.64 -0.51 -0.65 -0.56 Water TN 0.33 -0.22 0.33 0.92 -0.70 -0.57 0.48 0.90 0.99 0.89 DIN 0.48 -0.25 0.29 0.98 -0.67 -0.66 0.30 1.00 0.84 DON 0.29 -0.21 0.33 0.87 -0.68 -0.53 0.51 0.85 KCl NO3-N 0.46 -0.24 0.33 0.99 -0.69 -0.66 0.29 KCl NH4-N 0.15 0.29 0.31 0.33 -0.13 -0.13 PMN -0.34 0.23 -0.29 -0.69 0.78 K2SO4 TC 0.00 0.50 -0.36 -0.76 K2SO4 TN 0.35 -0.22 0.42 Micro. Bio. C -0.16 0.47 Micro. Bio. N -0.05 60 Figure 2.19: Soil Water DON Interaction Plots Table 2.19: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, lignin, proteins, condensed hydrocarbons, and tannins) within the soil water extractable DON samples. 61 Figure 2.20: So il Chloroform DON Interaction Plots Figure 2.20: Interaction plots illustrating the comparisons made with the Repeated Measure ANOVAs to investigate the impact of sampling date and cover class on the relative percentage of each compositional class (lipids, amino sugars, lignin, proteins, condensed hydrocarbons, and tannins) within the soil chloroform extractable DON samples. 62 Potentially mineralizable N was again correlated with an increase in the relative percent of recalcitrant compounds (r=0.71). Discussion: Leachate DOM and DON Composition: Although the composition of DOM and DON varied between samples, lignin represented the largest fraction of the organic pool in every leachate sample (Table 2.4). This is consistent with many previous studies of DOM (Heinz et al., 2015; Li et al., 2018; Wagner et al., 2015). A highly recalcitrant compound class, lignin is more resistant to microbial degradation than other DOM fractions and has also long been utilized as a tracer for terrestrial DOM (Nebbioso & Piccolo, 2013). Previous studies of porewater and soil-derived DOM report high relative percentages of aromatic lignin-like or lignin-derived compounds, and one study reporting that it accounted for 61% of DON in agricultural runoff (Jiang et al., 2017; Li et al., 2018; Nebbioso & Piccolo, 2013). It is therefore unsurprising that lignin accounted for at least 48.4% of leached DOM and 69.8% of leached DON in our study. Tannins also comprised large fractions of DOM in almost all of the leachate samples. Derived from vascular and non-vascular plants, tannins are another highly aromatic DOM fraction and, after lignin, are a major source of biologically produced polyphenols (Arbenz & Av?rous, 2015). Though tannins have been increasingly studied as an alternative source of macromolecular architectures for chemical development, they have been understudied in agricultural soils (Arbenz & Av?rous, 2015) . Though a large percentage of the leached DOM and DON were composed of recalcitrant compounds, proteins made up 12% of leached DOM compounds (Table 2.4). Proteins and other high H/C and low O/C compounds are considered more bioavailable and can serve as a readily 63 available nutrient pool for microbes and plants (D?Andrilli et al., 2015). The number of unique N containing compounds (r =-0.90) and the lability of the DOM decreased with sampling depth; the relative percentage of highly recalcitrant, lignin-like compounds had a significant (p<0.05) positive relationship with sampling depth (Table 2.5). This may indicate that more of the bioavailable DOM fraction does not leach through the soil profile and is retained in the soil closer to the surface. Additionally, this may indicate that labile DOM is being degraded at soil depths closer to the surface. Studies have shown that soil microbial activity decreases with increasing depth, with the majority of activity confined to the top few centimeters of the soil profile (Tate, 1979). This activity, in turn, in a known control on soil OM degradation and chemical composition, as soil microbes preferentially use bioavailable organic compounds in the soil solution as a source of N (Ros et al., 2010). Therefore, it may be that the labile fraction of DOM and DON present in our plots is being taken up by microbes before it has the opportunity to leach from the soil profile, leading to a higher relative proportion of recalcitrant compounds in the 90cm leachate samples. This finding supports one of our initial hypotheses: that leachate samples would be primarily composed of lignin, but that we would observe a shift in composition with increasing sampling depth. Although less abundant in the 90cm samples, labile DOM still accounted for an average of 17 ? 5% of leached organic compounds. Previous studies of leached OM have observed similar trends. In DOM from citrus grove runoff approximately 14% of DON compounds were biolabile (Li et al., 2018). While there is still need for further research into the impact of leached agricultural DON, studies of DON and non-purgeable organic carbon leached from landfills and soils amended from manure indicate that leached DON could contribute significant amounts of labile N to downstream aquatic systems, increase the risk of eutrophication in those waters 64 (Bolyard & Reinhart, 2017; Jensen et al., 2000). Studies show that photodegradation of more recalcitrant leached compounds may also have an important role to play in the transformation of DOM and the leaching of nutrients from agricultural fields (Li et al., 2018). In contrast, our dataset indicates <1% of DOM compounds in the leachate samples are highly photodegradable, and as depth increased, the percentage of highly photodegradable DOM decreased. Soil DOM and DON Composition: Though not one of our hypotheses, there was a distinct difference in DOM and DON composition between the two extraction methods. Lipids comprised the most classifiable compounds in all of the chloroform extracted samples. In comparison, the composition of the water extractable DOM was more similar to that of the leachate samples: with lignin as the most abundant compound in the water extract. Regardless of extraction methodology, protein was either the 2nd or 3rd largest functional group (Table 2.4). This difference is widely supported by past literature; studies have shown that extraction methodology and extraction solution can impact the organic compounds that one is able to capture during FT-ICR-MS analysis (Tfaily et al., 2015). Chloroform has been used in previous studies as a tool to investigate the lipid fraction of DOM, as it preferentially extracts non-polar compounds (McKee & Hatcher, 2015). In comparison, water extracts a wider range of compounds and is slightly selective for carbohydrates and compounds with high O/C ratios (Tfaily et al., 2015). Although not significant for all the compositional classes, sampling date had an impact on the overall composition of soil DOM. Over time, there was a slight decrease in the relative percentage of the labile, readily available classes (i.e.: proteins, amino sugars, lipids) and an increase in the relative percentage of more complex, recalcitrant compounds (i.e.: lignin). As 65 plant material decomposes, the more labile fraction is used preferentially by plants and microbes as a source of C and N (Healey et al., 2004; Lehmann & Kleber, 2015). Previous studies have correlated the abundance of lipids, and small, labile compounds with the increased microbial activity and the biodegradation of SOM (Hanson et al., 2018). It is likely that the smaller organic compounds in our plots were utilized during the growing season resulting in shifts in DOM composition. The shift may also explain the increase in highly photodegradable compounds, as recalcitrant compounds (such as lignin and both classes of hydrocarbons) are more susceptible to photodegradation (Li et al., 2018). As DOM composition shifted and the relative percentage of lignin increased, the percentage of highly photodegradable compounds increased as well. This indicates that we can accept our second hypothesis ? that the chemical composition of OM would shift throughout the growing as the corn plants utilized the labile N fraction as a source of N. However, the trends we observed were not statistically significant, and further sampling dates may provide more clarity on these trends. Previous seasonal studies of DOM observed temporal changes in arable soils, with the largest differences occurring between spring and fall (Rosa & Debska, 2018). The post-harvest sample set might have exhibited a more drastic, statistically significant DOM shift, but we were unable to process these data due to COVID restriction. Cover Cropping: Cover cropping did not have much impact on the composition of soil or leached DOM. We are therefore unable to accept our first hypothesis with any statistical significance: that the inclusion of a leguminous cover crop will increase the relative proportion of labile in DON and DON. The presence of legumes may partially be responsible for a subtle shift in DOM composition between the plots (Figures 2.10 and 2.14). The cereal and fallow plots had similar soil DOM compositions but differed from mixed cover plots. The mixed plots had more N peaks 66 (indicating a higher number of N containing compounds) and higher relative percentages of lignin, condensed hydrocarbons, and proteins. Legume cover crops have been shown to increase to overall and available C and N (Wu et al., 2017). The increase of N peaks may, therefore, be explained by the increase in SOM under leguminous cover (Wei et al., 2018). Though this does not account for the higher percentage of recalcitrant compounds, this would also explain the higher relative percentage of proteins in the plots. Additionally, leached DOM from plots that included a leguminous cover had a higher relative percentage of amino sugars. The inclusion of legumes in cover cropping strategies can lead to the enrichment of amino sugar and amino acid N in agricultural soils (Praveen-Kumar et al., 2002). Since the legumes in the mixed cover could lead to a higher concentration of amino sugars in the soil, leachate from the mixed cover plots would contain a higher relative percentage of amino sugars compared to the other cover treatments. However, the potential influence of legumes in our study was subtle. This could be due in part to the fertilization regime of our study. The entire field received a combined 831 lbs of N from UAN regardless of cover cropping treatment. Past studies have shown that additional N fertilizer can inhibit N fixation and nodulation in legumes (Tanner & Anderson, 1964), one study reporting a 23% decrease in N fixation in soybeans when in the presence of a 0.01M NO - 3 solution (Arrese-Igor et al., 1997). It is possible that the abundance of supplemental N sources in our plots reduced the potential effect of the leguminous cover crop. Additionally, it is worth noting that, though the plots used in our study were part of an intended long-term study, they were sampled in the first year the plots were established. While studies have noted positive short term effects of cover-cropping ? decreasing soil pH and slightly increasing soil organic C ? many studies indicate that it can take years of continued management to observe significant 67 effects of cover-cropping on soil fertility properties (Basche et al., 2016; Mukherjee & Lal, 2015). Therefore, it may be that we would have observed more distinct differences in DOM and DOM composition due to cover cropping if we were able to sample once the plots had been established for several more years. Conclusion: While FT-ICR-MS analysis revealed several trends in DOM and DON composition of our samples, we were not able to report any significant temporal shifts or any significant impact made from cover-cropping. Our first hypothesis postulated that the presence of N-fixing, leguminous cover crops would lead to a higher proportion of labile N ? containing compounds in the mixed cover crop plots. Our analysis indicates that the inclusion of red-clover in the cover crop had a slight effect on DOM composition, increasing the number of N peaks (indicating a higher proportion of N containing organic compounds within the samples) and increasing the relative percentages of lignin, condensed hydrocarbons, and proteins. However, this shift was not statistically significant and further study in is needed to determine if the effect would be more pronounced after multiple years of cover-cropping management. Our second hypothesis anticipated a temporal shift in the composition of soil OM within our plots. As a corn plant grows, it utilizes N from the surrounding soil to fulfill its nutrient requirements. We then hypothesized that the OM present in the soil would act as a source of nutrients, and that we would observe a decrease in the relative proportion of labile organic N throughout the growing season. This was somewhat supported by our study, as we observed a slight decrease in the relative percentage of proteins, amino sugars, and lipids, and a slight increase in the relative percentage of recalcitrant, lignin-like compounds. However, this shift was 68 not statistically significant, and additional sampling dates would be needed to determine the significance of the decrease in the percentage of labile DOM throughout the growing season. Our third and final hypothesis predicted that, as previous studies have indicated that lignin is a major component of leachate, leached DOM and DON will be primarily composed of lignin. Furthermore, we hypothesized that the composition of organic compounds within the leachate samples would shift with increasing depth, and that we would observe a higher proportion of recalcitrant, lignin-like compounds at lower sampling depths. This was supported by our study, as lignin accounted for at least 48.4% of leached DOM and 69.8% of leached DON in our study. We also observed the expected composition shift with increasing sampling depth, and the relative percentage of lignin (r=0.61) had a significant (p<0.05) positive correlation with increasing sampling depth. We are therefore able to accept our final hypothesis as sampling depth had a significant impact on the composition of leached DOM. Additional research is needed to confirm the trends we have observed, this study indicates that DOM and DON composition may vary over the growing season and with depth. This could have potential impacts on how we understand organic N cycling, and better inform fertilizer recommendations and field management practices. Current N fertilizer recommendations are often based on yield goals; until 2005 the commonly accepted method of estimating the fertilizer needed for a field was developed in 1973 and suggested that farmers apply 1.2lbs of fertilizer for every expected bushel of corn (Morris et al., 2018). Since then, additional methods have been developed: farmers utilize soil N tests to inform N recommendations, can use regression models to determine the maximum economic return of various N application rates, and can use plant measurements such as chlorophyll content and corn stalk nitrate content to inform fertilizer application. However, many of these metrics may be 69 underestimating the contribution of organic N as a source of nutrients in agricultural fields (Morris et al., 2018). While more research is needed, the slight temporal shifts in DOM and DON composition indicate that plants and microbes are utilizing this organic pool of nutrients throughout the growing season. Therefore, while we are not prepared to make any recommendations at this time, further investigation into the line of research may help to improve N fertilizer recommendations and prevent over-fertilization. 70 References ? Ch. 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Increasing soil pH reduces fertiliser derived N2O emissions in intensively managed temperate grassland. Agriculture, Ecosystems and Environment, 311. https://doi.org/10.1016/j.agee.2021.107319 75 Chapter III Title: Agricultural management impacts nitrification and denitrification gene abundance Author Names and Affiliations: Alyssa Wellman Houde a, Michel Cavigelli b, Rodney Venterea c, and Stephanie Yarwood a a Department of Environmental Science and Technology, University of Maryland, College Park, Maryland, USA, b Sustainable Agricultural Systems Laboratory, USDA Agricultural Research Service, Beltsville, Maryland 20705, USA, c USDA-ARS, Soil and Water Management Unit, St. Paul, Minnesota, USA Abstract: Nitrous oxide (N2O) is a powerful greenhouse gas, accounting for 7% of total emissions in the United States. Agricultural contributes the largest percentage, accounting for ~75% of emissions in the United States. These emissions are inextricably linked to soil N cycling as N2O is created as an intermediate in nitrification and denitrification. Past research has focused primarily on denitrification, as it was thought to be the major contributor of atmospheric N2O. However, recent studies indicate that nitrification might have a greater role to play in N2O emissions and indicate that there could be microbes present in agricultural fields that are capable of completely oxidizing ammonia (NH +4 ). This project quantified functional genes associated with nitrification (nxrA and amoA AOB) and dentification (nirS and nirK) to investigate impact of agricultural management on alternative N cycling pathways. Soil samples were collected from three of the University of Minnesota?s Research Farms and from the Farming System?s Project in Beltsville, MD. Samples were collected from experimental plots under a range of systems to compare the impact of tillage and fertilization (fertilized v. not fertilized, inorganic v. organic fertilizer, and tilled v. no-till plots). Comparison of the average gene abundance of 16S rRNA 76 and the selected functional genes revealed variation among the sites. Analysis of the nitrifier community indicates that management had limited impact. Nitrification gene abundance was lower in non-fertilized plots when compared to fertilized plots. Analysis of the denitrification community yielded mixed results. However, nirK and nirS abundances were higher in no-till plots than conventionally tilled plots, and the nirS:nirK ratio was higher in the Beltsville sites than in the Minnesota plots. Introduction: Nitrous oxide (N2O) is a powerful greenhouse gas, with 300x the warming potential of CO2 and accounts for approximately 7% of total greenhouse gas emissions in the United States (Billings & Tiemann, 2014; US EPA, 2021a). Additionally, stratospheric N2O leads to the production of nitrogen oxide which subsequently reacts with ozone, contributing to ozone depletion (Stolarski et al., 2015). There has been limited success in curbing N2O emissions; vehicle emission regulations and the Clean Air Act of 1990 led to a small decrease in atmospheric N2O from energy production. However, recent publications by the EPA show little change in total N2O emissions, reporting 466.81 MMT CO2 equivalent of N2O emission in 2019 (US EPA, 2021b). Nitrous oxide is a common by-product of wastewater treatment, fuel combustion, and plastic production. However, agriculture is responsible for the majority (75%) of modern N2O emissions, as N2O production is a key step in soil N cycling (US EPA, 2021a). N2O is created as an intermediate step in nitrification and denitrification and has repeatedly been shown to be influenced by a variety of biotic and abiotic environmental factors. Both nitrification and denitrification are microbially mediated processes and microbial abundance and community composition can have an impact on N2O emissions (?urovec et al., 2021). Abiotic factors such as 77 temperature, soil moisture, soil pH, and mineral N availability have been shown to influence microbial N-cycling and N2O emissions. Increases in temperature and soil moisture can lead to increases in soil organic N mineralization and nitrification which can lead to a subsequent increase in N2O (Aliyu et al., 2021; Daly & Hernandez-Ramirez, 2020). Results also indicate that soil moisture may be a strong determinant of the roles of nitrification and denitrification as N2O sources as nitrification requires an aerobic soil environment, while denitrification requires an anerobic soil environment. Studies have shown that in soils with greater than 60% of water filled pore spaces (WFPS) denitrification can account for up to 80% of N2O production, and pulse N2O emission events are often triggered by weather events in which soil moisture passes this 60% WFPS threshold (Aliyu et al., 2021). Lower soil pH has also been correlated with increased N2O emissions because pH shifts in the microbial community and hinders the synthesis of enzymes crucial to N2O reduction (?urovec et al., 2021). Additionally, the use of fertilizer has been repeatedly reported to increase N2O emissions (Daly & Hernandez-Ramirez, 2020; Ding et al., 2013). One study of long term (18 year) plots showed an 106% increase of background N2O emissions in fields with a history of compost application and a 46-76% increase of background N2O emissions in a history of inorganic fertilizer application when comparted to non-fertilized control plots (Ding et al., 2013). Another study indicated that by adding organic amendments to non-fertilized plots the activity of overall soil microbial community can increase from an initial 0.1-2% activity to 40% activity within minutes, leading to increases in N2O emissions from nitrification (Benckiser et al., 2015). However, it is worth noting that several studies have indicated wide variation in the spatial distribution of N-cycling microbial communities in arable and wetland soils, which can lead to location specific N2O fluxes (Correa-Galeote et al., 2013; Philippot et al., 2009). The uneven application of organic amendments, urea, and nitrification 78 inhibitors can lead to uneven nutrient availability and have been shown to enhance potential spot-wise nitrification and N2O emissions (Benckiser et al., 2015). Recent studies have begun to apply molecular techniques, quantifying microbial functional genes to gain a better understanding of dynamics within and the influence of abiotic factors on the nitrifying and denitrifying microbial communities (Garbeva et al., 2007). By targeting genes that encode for specific enzymes, researchers can better focus on pertinent members of the microbial community and investigate links between functional gene abundance, process rates, and N2O emissions. Most studies of agricultural N2O have focused on denitrification as it was thought to be the more important N2O production pathway compared to nitrification (Bakken et al., 2012). However, recent functional gene studies have suggested that nitrification could play an overlooked role in N2O emissions (Wei et al., 2014). One Minnesota study reported that the ratio of two nitrification genes nxrA and amoA AOB could explain 78% of variance in cumulative NO - 2 and 79% of the variance in N2O emissions (Breuillin-Sessoms et al., 2017). Other studies have investigated the impact of agricultural management strategies on microbial populations, several reporting that N additions can have a significant influence on microbial community composition. Inorganic N fertilizer has been shown to increase the abundance of both nitrifying (amoA AOB) and denitrifying (nirS, nirK, and nosZ) genes (Kong et al., 2021). Studies of organic N fertilizer have reported similar increases in denitrifying gene abundance (nosZ) but have also reported substantial decreases in potential ammonia oxidizing bacteria activity and decreases in N2O emissions up to 14% (Kong et al., 2021). Tillage has also been noted as a factor that can influence N-cycling microbes with no-till (NT) treatments shifting the dominant nitrite oxidizing bacteria (NOB) populations from nxrA (Nitrobacter sp.) to nxrB (Nitrospira sp.) (Breuillin- 79 Sessoms et al., 2017). Furthermore, studies of Nitrospira sp. (traditionally thought of as a nitrification species) have identified a novel subset of the genus capable of complete ammonia oxidation, harboring all the enzymes needed to convert ammonia (NH +4 ) to nitrite (NO - 2 ) and NO - -2 to nitrate (NO3 ) (Van Kessel et al., 2015). Originally isolated from aquaculture filters in 2015 (Van Kessel et al., 2015), comammox Nitrospira have been detected in soil. Nitrospira could play an important role in nutrient limited, oligotrophic soils (Kits et al., 2017; Li et al., 2019) but might be outcompeted by traditional nitrifying communities in nutrient rich environments (Xu et al., 2020). However, there is still much unknown about the role of Nitrospira and its potential impact on N2O emissions. Our study aims to target nitrification and denitrification functional genes to investigate the impact of management strategies on these novel N-cycling pathways. We had several hypotheses: 1) Previous studies have indicated that long-term management strategies (e.g. tillage and fertilization regime) (Breuillin-Sessoms et al., 2017; Kong et al., 2021) can impact the composition of the bacterial population in agricultural soils. We hypothesize that as organic, no-till systems often manipulate the landscape less than conventionally fertilized and tilled fields, the abundance of nitrite-oxidizing and denitrification genes will increase under no-till, organic management. 2) Comammaox Nitrospira has been increasingly observed in agricultural and forest soils, and we anticipate that comammox bacteria will be present in all the soil samples, though they will be in lower abundance compared to the traditional nitrifiers. 80 3) Fertilizer additions are known to increase nitrification activity within soils (Kong et al., 2021) and we hypothesize that the inclusion of either manure or inorganic fertilizer will increase the overall abundance of nitrification genes (nxrA and amoA AOB). Experimental Procedures: Study Site and Sample Collection: In collaboration with the USDA and the University of Minnesota, composite 0-20 cm soil samples were collected via push probe in late Fall 2019 from plots at the USDA?s Farming Systems Project in Beltsville, MD (sampled 10/29), the University of Minnesota?s Research and Outreach Centers in St. Paul (sampled 11/6) and Rosemount (sampled 10/28), MN, and the Sand Plain Research Farm in Becker, MN (sampled 11/1). All the plots were part of long-term agricultural research projects with histories of N2O research and all were under corn (Zea mays L.) production during the 2019 growing season. An additional archival sample from the Becker, MN site was included that was taken on 10/1/2018 in plots under potato production. There was variation in the management strategies at each site and among several of the Beltsville plots (Tables 3.1 and 3.2). The Rosemount plots were silt loam Waukegan (fine-silty over skeletal mixed, superactive, mesic Typic Hapludoll) and received 120 kg N ha-1 of urea-based fertilizer prior to the start of the growing season. A series of 6 plots ? 3 under no-till (NT) and 3 under conventional tillage (CT) ? were used for this study. The St. Paul site is also classified as Weaukegan silt loam soils (fine-silty over skeletal mixed, superactive, mesic Typic Hapludoll), and 3 CT, non-fertilized (NF) plots were used for the study. The soils at the Becker site differed from the other Minnesota site and are classified as loamy sand Hubbard soils (sandy, mixed, frigid, Entic Hapludolls). At the Becker site, a subset of NF, 81 CT plots were selected for our study (samples were taken from 4 plots in 2019 and 1 plot in 2018). The source plot for the 2018 Becker sample was tilled (on 5/4/2018) and had herbicide applied (on 5/23/2018) before planting. We were unable to obtain the records for the exact tillage, fertilizer application, and herbicide application dates at the Minnesota sites for 2019. The Beltsville plots were located on silt loam soils (Christiana (fine, kaolinitic, mesic Typic Paleudults), Matapeake (fine-silty, mixed, semiactive, mesic, Typic Hapludults), Keyport (fine, mixed, semiactive, mesic Aquic Hapludults), and Mattapex (fine-silty, mixed, active, mesic Aquic Hapludults)). A subset of 9 plots were selected for our study: 3 CT plots and 3 NT plots that received inorganic NPK fertilizer and 3 organic plots. For the context of this study, the organic plots were determined as soils that relied on organic fertilizers (manure) and utilized cultural weed management (instead of herbicides). We were unable to obtain the records for the exact tillage, fertilizer application, and herbicide application dates at the Beltsville sites for 2019. After collection, soils were immediately stored at -40oC for later analysis, with exception to the archival sample. The 2018 Becker sample was air dried, sieved, and stored before being shipped to University of Maryland in December of 2019 for further analysis. Sample Preparation: DNA was extracted from the soil samples using a Qiagen DNeasy PowerMax soil kit (Qiagen, Hilden, Germany). Following the manufacturer?s directions, 0.25g of defrosted soil was added to PowerBead tubes and shaken with a series of proprietary solutions using an MP FastPrep-24 bead beating grinder and lysis system for 45 seconds at 5.5 ms-1 and an Eppendorf Centrifuge 5430 at 10,000g for 3 minutes (in order to collect the supernatant after each solution 82 Table 3.1 . Study site information about the Maryland sampling site. Includes: treatment, location, soil type, number of plots sampled, crop rotation, tillage strategy, weed control strategy, and fertilizer applied in 2019. Maryland Study Site Information # of Primary Weed Treatment Location Soil Type plots Crop Rotation Tillage Control Fertilizer Source FSP ? NT Beltsville, Silt loam 3 Corn (Zea mays None Herbicides Inorganic (Cavigelli MD ? Christiana (fine, L.)/ Rye cover NPK et al., FSP ? CT kaolinitic, mesic 3 (Secale cereale Chisel Till 2008) Typic Paleudults) L.)/ Soybean ? Matapeake (fine- (Glycine max silty, mixed, L.) / Winter semiactive, mesic, Wheat Typic Hapludults) (Triticum ? Keyport (fine, aestivum L.)/ mixed, semiactive, Soybean mesic Aquic (Glycine max Hapludults) L.) FSP ? ? Mattapex (fine-silty, 3 Corn (Zea mays Disk/ Cultural Manure, Org3 mixed, active, mesic L.) / Rye cover Moldboard (Rotary Hoe/ K2SO4 Aquic Hapludults) (Secale cereale plow/ Between L.)/ Soybean Chisel Row (Glycine max plow Cultivator) L.) / Winter Wheat (Triticum aestivum L.)/Vetch cover (Vicia villosa L.) 83 Table 3.2. Study site information about the Minnesota sampling sites. Includes: treatment, location, soil type, number of plots sampled, crop rotation, tillage strategy, weed control strategy, and fertilizer applied in 2019. Minnesota Study Site Information # of Crop Primary Treatment Location Soil Type plots Rotation Tillage Weed Control Fertilizer Source Sand Plain Becker, Loamy sand 1 Rye (Secale Chisel & disc Herbicides None (Breuillin- ? 2018 MN ? Hubbard cereale L.)/ plow (0.5#/ac Sessoms Sand Plain (sandy, 4 Potato SencorDF + et al., ? 2019 mixed, (Solanum 1.0pt/ac Linex 2017) frigid, Entic tuberosum + 1.5pt/ac Hapludolls) L.)/Soybean Prowl H2O) (Glycine max (applied pre- L.) & Wheat planting) (Triticum aestivum L.) St. Paul St. Paul, Silt loam 3 Continuous Conventionally Herbicides None (Breuillin- MN ? Waukegan/ corn (Zea Tilled (Glyphosphate) Sessoms (fine-silty mays L.) (applied pre- et al., over sandy planting) 2017) or sandy- skeletal, mixed, superactive, mesic Typic Hapludolls ) Rosemount Rosemount, Silt loam 3 Corn (Zea None Herbicides Urea (120 kg N (Breuillin- ? NT MN ? Waukegan mays (Glyphosphate) ha-1 applied via Sessoms Rosemount (fine-silty 3 L.)/Soybean Moldboard (applied pre- sidedress et al., ? CT over skeletal (Glycine max (after corn), planting) application 2017; mixed, L.) chisel till (after when corn was Venterea superactive, soy) approx.. 20cm et al., mesic Typic high. 2016) Hapludoll) 84 Table 3.3. A series of commonly studie d functional genes associated with N cycling microbes. For each gene, the table lists the process associated with the gene, the enzyme the gene encodes, common microbes that harbor the gene, the corresponding primer set and sequence we used in this study, the amplicon size, the needed thermocycling conditions, and the source of information. Gene Process Enzyme Microbe Primer Sequence Amp Thermocycling Source size Conditions (bp) nxrA Nitrite Nitrite Nitrite F1norA / 5'-CAG ACC GAC GTG TGC GAA 322 Initialization: 94C (3 min.) (Breuillin- Oxidation Oxido- oxidizing R2norA AG-3' / 5' -TCC ACA AGG AAC Cycling: 94C (30s), 55C Sessoms et reductase bacteria GGA AGG TC-3' (45s), 72C (45s) for 35 cycles. al., 2017; alpha (Nitrobacter Elongation: 72C (5 min.) Wertz et al., subunit sp.) 2008) nxrB Nitrite nxrB169f / 5' - TAC ATG TGG TGG AAC A - 3' 485 Initialization: 95C (5 min.) (Breuillin- oxidizing nxrb638r / 5' - CGG TTC TGG TCR ATC A - Cycling: 95C (40s), 56.2C Sessoms et bacteria 3' (40s), 72C (90s) for 35 cycles. al., 2017; (Nitrospira Elongation: 72C (10 min.) Pester et al., sp.) 2014) nirK Nitrification Nitrite Nitrifying & nirK876 / 5' -ATY GGC GGV AYG GCG A - 3' 165 Initialization: 95C (5 min.) (Henry et al., & de- reductase denitrifying nirK1040 / 5'- GCC TCG ATC AGR TTR TGG Cycling: Two step 2004) nitrification (copper bacteria TT-3' touchdown. 95C (15s) form) annealing temperature started nirS Nitrite nirSCD3aF / 5' ? AAC GYS AAG GAR ACS GG - 400 at 63C (60s) and lowered 1C (Hristova & reductase nirSR3cd 3' / 5'- GAS TTC GGR TGS GTC each cycle for the first 6 Six, 2006) (heme TTS AYG AA- 3' cycles, 72C (30s). A total of form) 46 cycles. Elongation: 72C (10 min.) 16S Protein 30S Wide range of Eub 338F / 5' - ACT CCT ACG GGA GGC AGC 200 Initialization: 95C (5 min.) (Fierer et al., Synthesis ribosomal bacteria and Eub518R AG - 3' / 5' -ATT ACC GCG GCT Cycling: 95C (5s), 55C (15s), 2005) subunit archaea GCT GG - 3' 72C (10s) for 40 cycles. amoa Ammonia Ammonia Ammonia CrenAmoAQ- 5??GCA RGT MGG WAA RTT CTA 124 (Mincer et al., (AOA) Oxidation mono- oxidizing F / YAA - 3' / 5??AAG CGG CCA TCC 2007) oxygenase archaea CrenAmoMo ATC TGT A ? 3? dR amoa Ammonia amoA-1F / 5'-GGG GTT TCT ACT GGT GGT-3' 491 Initialization: 94C (5 min.) (Aoi et al., (AOB) oxidizing amoA-2R / 5'-CCC CTC KGS AAA GCC TTC Cycling: 60C (90s), 72C 2004) bacteria TTC-3' (90s), 94C (60s) for 42 cycles. Elongation: 72C (10 min.) amoa Complete Ammonia Comammox Comaa or Clade A: 5? -ATY AAY TGG GTS In4itialization: 94C (5 min.) (Klotz et al., (Coma Ammonia mono- Nitrospira Comab 244F / AAY TA -3?/ 5? ? ARA TCA TSG 15 Cycling: 94C (30s), 52C 2017; Lin et mmox) Oxidation oxygenase Clades 659R TGC TRT G ? 3? Clade B: 5? ? TAY (45s), 72C (60s) for 25 cycles. al., 2020) TTC TGG ACR TTY TA ? 3?/ 5? ? A & B Elongation: 72C (10 min.) ARA TCC ARA CDG TGT G ? 3? 85 addition). DNA was quantified using a Quibit 2.0 Flurometer and stored at -20oC for later analysis. Q-PCR: Functional genes related to N cycling were identified and selected for further analysis. These nitrification and denitrification genes, the enzymes they encode, and the primers and thermocycling conditions commonly used to amplify them, are listed in Table 3.3. Primers were tested for each functional genes by conducting a conventional PCR with a subset of the samples of extracted DNA: 10uL of GoTaq Green Master Mix 2x (Promega, Madison, WI, USA), 5uL of water, 1uL of forward primer, 1uL of reverse primer, 1uL of 1000ug uL-1 bovine serum albumin (BSA), and 2uL of extracted DNA into each well. Successful amplification was checked via gel electrophoresis using an Amersham Pharmacia Biotech Rig set at 120v, 70mA, and run for 40 minutes. Invitrogen SYBR Safe DNA gel stain (Invitrogen, Waltham, MA, USA) was used in conjunction with the Green Master Mix to stain the DNA during the gel electrophoresis, and UVP BioDoc-It Imaging System was used to take pictures of the resulting gels under UV light. Of the initial list of functional genes, nxrA, nirK, nirS, amoA AOB, and 16S rRNA were selected for further study. E. coli Q-PCR standards were created using a TOPO TA Cloning Kit (Invitrogen, Waltham, MA, USA) with a pCR 2.1-TOPO Vector following the manufacturer?s guidelines. Briefly, a subset of DNA extracts were PCR amplified using the following mix: 9.3uL water, 5uL of 5x Colorless GoTaq Reaction Buffer, 1.8uL of 25mM MgCl2, 1.3uL of Forward and Reverse Primer, 0.5uL of 2.5mM dNTP Mix, 4uL of 0.4% BSA, 0.1uL of Taq DNA Polymerase, and 2uL of 2.5ng uL-1 DNA in each well. Next, 4ulLof amplified PCR product for each of the 86 functional genes was added to a 0.65mL tube along with 1uL of salt solution and 1uL of TOPO Vector. The reaction was allowed to incubate at room temperature (22-23oC) for 5 minutes while a tube of OneShot TOP10 (Invitrogen, Waltham, MA, USA) chemically competent E.coli cells was allowed to thaw on ice. The ligation reaction (2uL) was added to the E.coli, flipped to mix, and incubated on ice for 5 additional minutes. The E. coli cells were then heat shocked in a 42oC water bath for 30 seconds, after which 250uL of room temperature SOC was added to the shocked cells. The resulting cell suspension was capped and incubated at 37oC for 1 hour while shaking. For each of the cell suspensions, 2 Luria broth plates with ampicillin were treated with 40ul of 40 mg mL-1 X-gal 1 hour before plating and allowed to warm at 37oC for 30 minutes before plating. The resulting transformed cells were plated on the prepared growth media; for each cell suspension 50uL the mixture was added to one plate while 100uL of the mixture was added to a second plate. All plates were incubated overnight at 37oC. The following day, the plates were checked for growth, and a subset of white, successfully transformed colonies were re-streaked onto new Luria broth plates with ampicillin. These were allowed to incubate overnight at 37oC. The resulting growth was tested for successful transformation by PCR amplification for each colony in question, using a standard reaction mix. A subset of the clones that had successfully amplified were regrown in tubes containing 6mL of Luria broth with ampicillin. These were incubated overnight at 37oC. The following day, 1mL of overnight culture was combined with 400uL of glycerol and preserved at -80oC in a cryotube. Plasmids were extracted from the remaining 5mL of overnight culture using the Qiagen QIAprep Spin Miniprep Kit (Qiagen, Hilden, Germany). The resulting plasmids were quantified using a Quibit 2.0 Flurometer, linearized using FastDigest Eco321(Thermoscientific, 87 Waltham, MA, USA), cleaned with a Qiagen QIAquick PCR Purification Kit (Qiagen, Hilden, Germany), and re-quantified using a Quibit 2.0 Flurometer. The resulting plasmids were diluted to 2.5ng ul-1, which was in turn used to create a serial dilution (2.5x10-1 ng ul-1, 2.5x10-2 ng ul-1, 2.5x10-3 ng ul-1, 2.5x10-4 ng ul-1, and 2.5x10-5 ng ul-1) that would serve as a potential standard set for the Q-PCR analysis. Each of the standards were tested and optimized for Q-PCR using a mastermix that contained: 10uL KicqStart SYBR Green qPCR ReadyMix with ROX (Sigma-Aldrich, St. Louis, MO, USA), 6uL of water, 1uL of each primer, and 2uL of sample. An Applied Biosystems StepOnePlus Real-Time PCR System was used for all Q-PCR assays and each sample was run in triplicate for genes: nxrA, nirK, nirS, amoA AOB, and 16S rRNA. Data Analysis: JMP Pro 15 was used for multivariate analysis (Pearson Correlations) and one-way ANOVAs to explore the impact of management strategy and sampling location on the abundance of functional genes. Analysis was conducted on the raw gene abundance data as well as on several ratios (nxrA:amoA AOB, nirS:nirK) to better understand the dynamics among the populations. The abundance of each functional gene was also divided by 16S rRNA gene abundance to determine the relative proportion of each functional gene to overall bacterial abundance, effectively normalizing the dataset and allowing comparison among sampling sites. For each functional gene or gene ratio, we conducted multiple One-Way ANOVAs to compare the abundances or ratios: 1) between the Maryland and Minnesota soils (MD v. MN) 2) among all the sampling sites (Site Diff (all)) 3) among the Minnesota sampling sites (Site Diff (MN)) 88 4) between all no-till (NT) and conventionally tilled (CT) plots across all the sampling sites (NT v. CT (all)) 5) between the NT and CT plots across all the Minnesota sampling sites (NT v. CT (MN)) 6) between the NT and CT plots at the Rosemount, MN site (NT v. CT (Rosemount)) 7) between the NT and CT plots at the Beltsville, MD site (NT v. CT (Beltsville)) 8) between the fertilized (F) and non-fertilized (NF) plots across all the sample sites (F v. NF (all)) 9) between the F and NF plots across the Minnesota sample sites (F v. NF (MN)) 10) between the organic and inorganically fertilized plots at the Beltsville, MD site (Organic v. Inorganic (Beltsville)). Pearson correlations were conducted to determine the significance of the shifting microbial community and any potential relationships between the nitrifiers and denitrifiers we were able to amplify, comparing the raw (16S, nxrA, amoA AOB, nirS, and nirK) and normalized (nxrA:16S, amoA AOB:16S, nirS:16S, and nirK:16S) gene abundances. For all the One-Way ANOVA and Pearson Correlation Analyses each data point represented the gene abundance or gene ratio for one of the sampled plots at a site (instead of a mean value across the treatments at a given site). Results: Microbial Abundance: Overall, 16S rRNA gene abundance was significantly lower in the Beltsville samples compared to the group of Minnesota samples (F= 16.375, p=0.0006), indicating a relatively 89 smaller bacterial community at the Maryland site (Table 3.4 & Figure 3.2a). 16S rRNA gene abundance varied across the Minnesota sites, but most sites had similar 16S rRNA gene quantities. One exception was the Becker 2018 sample, which was the lowest of the Minnesota samples and much lower than the Becker sample from the following year (Figure 3.1a). Though not significant, it is also worth noting that the no-till plots had a greater 16S rRNA gene abundance than the corresponding conventionally tilled plots at both the Rosemount and Beltsville sites (Figures 3.1a and 3.2a and Table 3.4) Nitrification Functional Genes: Although five different nitrification genes were planned for this study, we had difficulty cloning standards in order to quantify amoA AOA (ammonia oxidizing archaea) and nxrB (nitrite oxidizing Nitrospira sp.). Furthermore, we were unable to amplify Clades A&B of Comammox Nitrospira from the soil samples. Our analysis focuses on amoA AOB (ammonia oxidizing bacteria) and nxrB (nitrite oxidizing Nitrobacter sp). Site location had a significant effect on the abundance of nxrA and amoA AOB (nxrA, F=6.4514, p=0.0021; amoA AOB, F=4.6975, p=0.009) (Table 3.4). The Beltsville samples had significantly lower nxrA (F=15.261, p=0.0008) and amoA AOB (F=8.054, p=0.010) gene abundances than the Minnesota samples (Figures 3.1 b & c, 3.2 b & c, Table 3.4). When normalized to account for the 16S rRNA gene at the sites, there were no significant differences in nitrification genes by site or between the Maryland and Minnesota samples (Table 3.5). Although not significant (p<0.05), lower normalized nitrification gene counts were observed at the non-fertilized Minnesota sites when compared to fertilized plots (Figure 3.3 a & b). 90 Figure 3.1: Microbial Abundance in Minnesota Plots Figure 3.1. Average 16S rRNA gene, amoa AOB, nxrA, nirK, and nirS gene abundance (average gene copy number per gram of wet soil) for each of the treatments in the Minnesota sites. 91 Figure 3.2: Microbial Abundance in Beltsville Plots Figure 3.2. Average 16S rRNA, amoA AOB, nxrA, nirK, and nirS microbial abundances (average gene copy number per gram of wet soil) for each of the Beltsville treatments. 92 Table 3.4. Summary table of One-Way ANOVA values for 16S rRNA, nxrA, amoa AOB, nirS, and nirK gene abundance (average gene copy number per gram of wet soil). Bolded values indicate significant (p<0.05) differences. Functional Gene Abundance One-Way ANOVA Summary Table 16S nxrA AOB nirS nirK ndf, p- ndf, p- ndf, p- ndf, p- ndf, p- F ddf value F ddf value F ddf value F ddf value F ddf value MD v. MN 16.375 1, 21 0.001 15.261 1,21 0.001 8.054 1,21 0.010 0.001 1, 21 0.974 11.923 1,21 0.002 Site Diff. (all) 6.473 4, 18 0.002 6.451 4,18 0.002 4.698 4,18 0.009 8.037 4,18 0.001 7.328 3,19 0.002 Site Diff (MN) 1.118 2, 11 0.361 1.181 2,11 0.343 2.585 2,11 0.120 11.608 2,11 0.002 2.117 2,11 0.167 NT v. CT (all) 2.000 1, 21 0.172 0.884 1,21 0.358 0.203 1,21 0.657 1.889 1, 21 0.184 1.407 1,21 0.249 NT v. CT (MN) 2.239 1, 12 0.160 0.633 1,12 0.442 0.969 1,12 0.344 1.788 1, 12 0.206 1.817 1,12 0.203 NT v. CT (Rosemount) 10.298 1, 4 0.033 3.647 1,4 0.129 0.004 1,4 0.950 5.768 1,4 0.074 8.500 1,4 0.043 NT v. CT (Beltsville) 0.040 1, 7 0.848 0.075 1,7 0.792 0.007 1,7 0.937 0.209 1,7 0.661 2.983 1,7 0.128 F. v. NF (all) 5.562 1, 22 0.028 2.623 1,21 0.120 0.001 1,21 0.972 3.034 1, 21 0.096 2.328 1,21 0.142 F v. NF (MN) 0.358 1, 12 0.561 0.003 1,12 0.954 2.275 1,12 0.157 3.031 1, 12 0.107 0.000 1,12 0.987 Organic v. Inorganic (Beltsville) 2.760 1, 8 0.135 0.520 1,8 0.492 1.092 1,8 0.327 0.014 1,8 0.909 0.352 1,8 0.569 93 Although also not significant, higher nxrA:16S rRNA was observed in the CT plots when compared to the corresponding NT plots at Rosemount and Beltsville (Figures 3.3b and 3.4b). Regarding the effect of tillage on the nitrification community, there were no clear trends in the AOB:16S rRNA sample set. The Rosemount NT plots had a lower average AOB:16S rRNA that the Rosemount CT plots (Figure 3.3a), however the wide range of the Rosemount CT values led to a large SE for the sample plots and there was not statistically significant difference between the Beltsville NT and CT averages. Though not significant, largest treatment difference at the Beltsville site was between the NT and organic plots (Table 3.5, Figure 3.4); the organic plots had relatively higher average nxrA:16S rRNA and slightly lower average AOB:16S rRNA (Figure 3.4 a& b) that the NT plots. The nxrA:amoA AOB gene ratio was higher at the Beltsville sites (F=11.751, p=0.003) when compared to the Minnesota samples (Table 3.5). There was less variation in the ratio across Minnesota samples (0.6-9.0), compared to Beltsville (1.7 ? 51.8) (Figures 3.3e and 3.4e,). Denitrification Functional Genes: Similar to the 16S rRNA and other functional gene abundance, sampling site (F=6.059, p=0.003) and state (F=11.923, p=0.002) had a significant impact on the abundance of nirK gene copy numbers but did not impact nirS gene copy number (Table 3.4). Though not significant, the NT plots in both Beltsville and Rosemount had higher average nirS and nirK abundance than the corresponding CT plots at the respective sites (Table 3.4, Figures 3.1 d &e and 3.2 d & e). When normalized using 16S rRNA gene abundance additional trends emerged. There was significantly higher nirS:16S rRNA ratios (F=46.754, p=<0.0001) in Beltsville plots compared to the Minnesota samples. 94 Figure 3.3: Microbial Abundance Ratios in Minnesota Plots Figure 3.3. Ratios of amoA AOB: 16S rRNA, nxrA:16S rRNA, nirK:16S rRNA, nirS:16S rRNA, nxrA: amoA AOB, and nirS:nirK gene abundances for each of the treatments at the Minnesota study sites. 95 Figure 3.4: Mic robial Abundance Ratios in Beltsville Plots Figure 3.4. Ratios of amoA AOB: 16S rRNA, nxrA:16S rRNA, nirK:16S rRNA, nirS:16S rRNA, nxrA: amoA AOB, and nirS:nirK gene abundances for the Beltsville treatments. 96 Additionally, though not statically significant the Beltsville NT plots also had lower nirS:16S rRNA ratios averages than the CT plots (Figure 3.4d). The ratios of nirS:16S rRNA and nirK:16S rRNA were higher in organic plots compared to conventional and no-till (Table 3.5). The nirS: nirK ratio differed between sampling site (F=8.203, p=0.0006) and state (F=32.959, p= <0.0001), with the highest ratios observed in the Beltsville plots (Figure 3.4f). Though not significant, higher nirS:nirK values were observed in the NF Minnesota plots when compared to fertilized plots from the Minnesota sites (Figure 3.3f). A comparison of tillage treatments indicates conflicting trends, there was a slightly higher but not statistically significant difference in nirS: nirK ratio in NT plots at Beltsville (F=2.326, p=0.17), but a higher nirS:nirK ratio in the CT plots at Rosemount (F=19.291, p=0.012) (Figures 3.3f and 3.4f). Overall Microbial Community Composition: PCoA coordinates clustered based on sample site location (Figure 3.5), consistent with the One-Way ANOVAs on individual gene abundances (Table 3.4). There was also weak clustering based on management strategy: the non-fertilized plots loosely clustered in the center and top right quadrant and the no-till data points clustered loosely in the two left hand quadrants. However, sampling site seems to have the strongest influence on clustering, as the points are more strongly grouped by sampling location. Pearson correlations to investigate the composition of the bacterial populations show that 16S rRNA gene abundance was significantly correlated to increases in all the functional genes (p<0.05) (nxrA, r=0.91; nirS, r= 0.54; amoA AOB, r=0.55) (Table 3.6). The abundance of nitrification genes nxrA and amoA AOB were strongly correlated to one another (r=0.71, p=0.0001), while nxrA was weakly positively correlated to the abundance of nirS (r=0.42, p=0.05) (Table 3.6). When normalized with the 16S rRNA gene abundance, there no significant correlations between the functional genes. 97 Table 3.5. Summary table of One-Way ANOVA values for 16S rRNA normalized nxrA, amoa AOB, nirS, and nirK gene abundance as well as for the nxrA:amoA AOB and nirS:nirK ratios. Bolded values indicate significance (p<0.05). Normalized Functional Gene Abundance and Gene Ratios One-Way ANOVA Summary Table nxrA:16S AOB:16S nirS:16S nirK:16S ndf, p- ndf, p- ndf, ndf, p- F ddf value F ddf value F ddf p-value F ddf value MD v. MN 0.894 1,21 0.355 3.949 1,21 0.060 46.754 1,21 <0.0001 1.266 1,21 0.273 Site Diff. (all) 1.033 4,18 0.417 2.013 4,18 0.136 12.111 4,18 <0.0001 0.730 3,19 0.547 Site Diff (MN) 1.837 2,11 0.205 1.217 2,11 0.333 10.243 2,11 0.003 1.604 2,11 0.245 NT v. CT (all) 0.877 1,21 0.360 1.631 1,21 0.216 0.025 1,21 0.876 0.323 1,21 0.576 NT v. CT (MN) 3.332 1,12 0.093 3.476 1,12 0.087 0.325 1,12 0.579 0.050 1,12 0.826 NT v. CT (Rosemount) 0.440 1,4 0.543 0.455 1,4 0.537 1.060 1,4 0.362 1.950 1,4 0.235 NT v. CT (Beltsville) 0.118 1,7 0.741 0.053 1,7 0.824 0.147 1,7 0.713 0.279 1,7 0.614 F. v. NF (all) 2.300 1,21 0.144 0.294 1,12 0.593 3.474 1,21 0.076 1.025 1,21 0.323 F v. NF (MN) 1.729 1,12 0.213 2.653 1,12 0.129 1.573 1,12 0.234 0.683 1,12 0.425 Organic v. Inorganic (Beltsville) 4.149 1,8 0.076 1.263 1,8 0.294 1.071 1,8 0.331 1.813 1,8 0.215 nxrA:AOB nirS:nirK ndf, p- ndf, p- F ddf value F ddf value MD v. MN 1.266 2,21 0.243 32.959 1,21 <0.001 Site Diff. (all) 0.730 3,19 0.547 11.158 2,19 0.000 Site Diff (MN) 1.344 2,11 0.301 8.999 2,11 0.005 NT v. CT (all) 0.309 1,21 0.584 0.299 1,21 0.590 NT v. CT (MN) 0.088 1,12 0.771 0.325 1,12 0.579 NT v. CT (Rosemount) 0.389 1,4 0.567 19.291 1,4 0.012 NT v. CT (Beltsville) 1.046 1,7 0.341 2.326 1,7 0.171 F. v. NF (all) 1.547 1,21 0.227 2.584 1,21 0.123 F v. NF (MN) 3.010 1,12 0.108 3.425 1,12 0.089 Organic v. Inorganic (Beltsville) 0.340 1,8 0.576 0.132 1,8 0.726 98 Figure 3.5: PCoA of Microbial Community Composition Figure 3.5. The above figure illustrates the results of the Principal Coordinate Analysis conducted on the entire dataset to explore trends in the overall community composition of each soil sample. Shapes are used to differentiate the sample site location of each plot, and the points are shaded to indicate the combination of management strategies. 99 Table 3.6. Pearson Correlations comparing the 16S rRNA, nxrA, amoa AOB, nirS, and nirK gene abundance (average gene copy number per gram of wet soil) as well as 16S rRNA normalized nxrA, amoa AOB, nirS, and nirK gene abundances. Bolded values indicate significance (p<0.05) Functional Gene Pearson Correlations nirK: nirS: AOB: nxrA: 16S 16S 16S 16S nirK nirS AOB nxrA 16S -0.45 -0.54 0.20 -0.21 0.89 0.54 0.55 0.91 nxrA -0.50 -0.52 0.39 0.11 0.91 0.42 0.71 AOB -0.41 -0.41 0.90 0.28 0.65 0.21 nirS 0.29 0.26 -0.01 -0.12 0.51 nirK -0.51 -0.44 0.29 -0.02 nxrA:16S 0.02 0.29 0.34 AOB:16S -0.30 -0.30 nirS:16S 0.81 100 Table 3.7. Historical N2O emission data for the Minnesota sample sites, under different fertilization regimes. Data adapted from (Breuillin-Sessoms et al., 2017). Historical N2O Emissions (?g N g-1soil) in Minnesota Sample Sites Initial urea-N concentration (?g N g-1) 0 100 250 500 1000 Becker, MN 1.21 1.37 14.3 37.9 12.1 Rosemount - CT 1.05 2.23 2.48 2.49 13.2 Rosemount- NT 1.1 1.39 1.18 1.42 30.5 St. Paul 0.95 1.09 1.07 8.66 21.5 101 There was a weak relationship between nxrA:16S rRNA and the other normalized functional genes: nirS:16S (r=0.29 p= 0.18) and AOB:16S (r=0.34, p=0.11) (Table 3.6). Though not significant, nirS:16S was also weakly negatively correlated with AOB:16S (r=-0.30, p=0.17) (Table 3.6). Discussion: N2O Emissions: Though N2O data were not collected from all plots during the 2019 sampling season, all sampling sites have a long-term N2O emissions data. Values reported from the Minnesota sites in 2017 indicate a range of N2O emissions (Table 3.7) among the study sites and among the various management strategies (Breuillin-Sessoms et al., 2017). Generally, the historical data indicates that N2O emissions increased with higher N fertilizer application rates. At the Rosemount site N2O emissions were slightly higher in CT plots than NT plots when receiving 100-500 ?g N g -1. Nitrous oxide emissions also varied by site, the Becker site reported the highest N2O emissions at most fertilization levels, followed by the Rosemount and St. Paul sampling sites. Values reported from the Beltsville site show that the highest emissions were recorded in the organic plots followed by the chisel and no-till plot. Although this difference was statistically significant on some sampling days, cumulative emission records indicate no statistically significant differences in N2O emissions among the treatments (Djurickovic, 2010). Microbial Abundance: Abundance of 16S rRNA genes per gram of soil varied greatly between sampling sites and between the Maryland and Minnesota samples. This variation could be due to a wide variety 102 of environmental factors including: soil type, rainfall, temperature, and soil pH (Cao et al., 2016). The Becker 2018 sample had lower 16S rRNA and functional gene abundances, which is likely due to the sample having been previously sieved and dried, and storage conditions are known to effect microbial community analysis (Rubin et al., 2013). 16S abundance was also correlated to tillage regime, matching what has been observed in the literature and reporting higher relative abundances (RAs) in NT plots. Tillage is known to homogenize soils, and previous studies have indicated that long-term no-till management can increase fungal and bacterial abundance and bacterial richness (Sengupta & Dick, 2015; Sun et al., 2016). Though it was not observed in our study, organic farming practices have also been known to have a positive effect on soil microbial biomass (C and N) as well as increase total phospholipid fatty acids (PLFAs) (Lori et al., 2017). Nitrification Functional Genes: Though comammox Nitrospira have been observed in agricultural soil samples, we were not able to amplify the corresponding functional genes from any of our samples. We must therefore reject our second hypothesis: that we would observe comammox Nitrospira in all of the soil samples, though they would be in lower abundance than traditional nitrifiers. This could indicate that comammox Nitrospira was not present in any of our plots. However, this could also be due to our choice of primer sets. A recent study evaluated the effectiveness of the two most common primer sets used to amplify comammox Nitrospira and found that the set that we used (comA/B- 244f/659r) was effective in amplifying samples with high relative abundances but that it was not sensitive to clades with a lower RA (Lin et al., 2020). Previous studies that have successfully isolated comammox Nitrospira from agricultural soils have indicated lower RAs 103 compared to more traditional nitrifiers, and the comammox Nitrospira might be outcompeted in high nutrient (NH +3 ) environments (Xu et al., 2020). Analysis of the successfully amplified nitrification functional genes reveal some differences in the nitrifying community in relation to management strategy. These differences were not significant but are supported by similar findings from the literature. Long-term fertilization has repeatedly been shown to increase nitrifiers, especially ammonia-oxidizing bacteria (Aliyu et al., 2021; Sun et al., 2015). The non-fertilized plots in our study had slightly lower nxrA:16S rRNA ratio and much lower amoA AOB:16S rRNA ratio when compared to the other fertilized Minnesota plots. While not significant, this supports our third hypothesis: that fertilizer applications (either manure-based or inorganic fertilizer) will increase the overall abundance of nitrification genes (amoA AOB and nxrA). The potential impact of tillage was mixed, and conflicts with trends reported in previous studies. Though not significant, nxrA:16S rRNA was higher in the CT plots than in comparable NT plots at both Beltsville and Rosemount, indicating that tillage may have increased the relative proportion of nitrifiers (Figures 3.3 b &c, 3.4 b & c). Past studies have reported the opposite trend, and this does not support our initial hypothesis (that greater nxrA gene abundance would be observed in the no-till plots). One study of long-term experimental plots in France found that no-till soils had greater nxrA gene abundance and potential nitrite oxidation (Attard et al., 2010). Furthermore, the ratio between nxrA and amoA AOB has been used as an indicator for nitrification activity, one previous study reported that it explained 79% of variance in cumulative N2O emissions (Breuillin-Sessoms et al., 2017). Though not significant, no-till plots in Beltsville did have higher nxrA: amoA AOB than the Beltsville CT plots (Figure 3.4e). This indicates that there might be tighter coupling between the communities of ammonia oxidizers and nitrite 104 oxidizers. Nitrous oxide emissions due to nitrification in agricultural soils has often been referred to metaphorically as a leaky pipe. In this scenario total N2O emissions are proportional to N- cycling, and nitrification is mediated by two distinct microbial populations: ammonia oxidizers (amoA) and nitrite oxidizers (nxr) (Xing et al., 2011). Leaks (referring to additional N2O emissions) can occur when these two populations of nitrifiers within the soil system are not coupled (Breuillin-Sessoms et al., 2017). For example, if there are more ammonia oxidizers present in the soil than nitrite oxidizers, a leak is created and the imbalance of microbial activity can lead to a build-up of nitrite, and as a by-product of increased ammonia oxidation can increase N2O emissions. Our data therefore suggests that, while not statistically significant, the nitrifying populations are more tightly coupled at the Beltsville NT plots compared to the CT plots. This is also supported by historical N2O emission data from the Beltsville plots. While not statistically significant, the N2O emissions at these sample sites reported lower N2O emissions in the NT sites compared to the CT sites (Breuillin-Sessoms et al., 2017; Djurickovic, 2010). However, the same trend was not observed between the CT and NT treatments in Rosemount (Figure 3.3e). Further research is needed, but this indicates that while there may not have been a substantial difference in the size of the overall nitrifying community between tillage practices, no-till management may still increase potential nitrite oxidation and subsequently decrease overall N2O emissions. Denitrification Functional Genes: In our study, we measured nirS and nirK functional genes that each correspond to one of the two common forms of nitrate reductase within denitrifying communities. Unlike nitrification, the full pathway for denitrification can be found in one organism, with common denitrifiers 105 containing all the requisite enzymes for complete denitrification (Jones et al., 2008). As such, each sample?s nirS and nirK abundances can act as a proxy to represent the abundance of general denitrifiers with nirS and denitrifiers with nirK within each plot. There were no significant differences between the denitrification gene abundances. However, several of the trends that were observed are supported by past studies. The ratio of nirK:16S rRNA was consistently higher in no-till plots comparted to tilled plots at both Rosemount and Beltsville, and previous studies have repeatedly indicated that no-till management can increase soil denitrification activity and gene abundance (Wang & Zou, 2020). This in turn has been correlated with an increase in N2O emissions in multiple studies (Billings & Tiemann, 2014; Kong et al., 2021; Wang & Zou, 2020). Analysis of nirS gene abundance at our study sites, however, yielded contradictory results; CT plots had slightly higher nirS:16S rRNA abundance than NT plots at both Rosemount and Beltsville (Figures 3.3d and 3.4d). However, comparing the Beltsville samples to historical N2O data it is worth noting that the nirS:16S rRNA ratio follows the same trend as N2O past N2O emissions in the plot. Though not significantly different, the organic plots had the highest nirS:16S rRNA ratio, followed by the CT plots, and NT plots. Similarly, though not statistically significant, the historical N2O data indicates that the highest emissions were recorded in the organic plots followed by the CT and NT plots. Further investigation is needed to explore this relationship. Comparison of the two denitrifying genes, indicates much greater nirS abundance in comparison to nirK, and higher nirS: nirK in all the Beltsville plots when compared to the Minnesota plots. NirS encodes the more common heme form of nir (Alvarez et al., 2014); therefore, nirS would have a greater abundance in all our plots. The difference in the ratio of nirS: nirK could potentially be explained by differences in field conditions and environmental 106 factors. The niches of nirS and nirK have been studied, and both have been noted to be influenced differentially by soil pH, soil water content, total soil carbon, and soil development (Bowen et al., 2018; Herold et al., 2018; Szukics et al., 2010). The Beltsville and Minnesota sites have very different climates: Beltsville receives and average of 1110mm of precipitation and has an average temperature of 12.8oC, while the Minnesota sites are drier on average (879mm of precipitation) and colder (6.4oC). This difference can in turn influence soil formation, available water in the soil, and a wide range of other soil properties (Doula & Sarris, 2016). Additionally, this could indicate that the Minnesota sites have a lower pH, as loss of nirS abundance has been linked to decreases in pH and is more sensitive to pH change than nirK (Herold et al., 2018). Conclusion: The results of this study were mixed, and we are unable to accept our hypotheses with any statistical significance. Our first hypothesis postulated that, as previous studies have shown that long-term management can have impact on soil microbial populations, we would expect a greater abundance of nitrite oxidizing and denitrification genes in less manipulated (no-till and organic) plots. Comparison of the nxrA and amoA AOB gene abundance under different tillage regimes in our plots yielded mixed results. Treatment had no clear difference among the 16S rRNA normalized amoA AOB values in the Beltsville plots. Furthermore, while it is not statistically significant, the amoA AOB:16S rRNA at the Rosemount, MN site and the nxrA:16S rRNA ratios at both Beltsville and Rosemount were higher in conventionally tilled plots than non-tilled plots. This contradicts what have been observed in past literature. Additional research is needed to understand this relationship further. We also must reject our second hypothesis: that we would expect to observe comammox Nitrospira in all of our soil samples. This could indicate that it may not be as ubiquitous in soils 107 as other studies indicate, or that successful amplification requires alternative primers to amplify comammox bacteria in environments where it would have lower relative abundances. Further study in needed, as other studies have begun to isolate comammox Nitrospira in agricultural soils, and it may yet prove to have an important role to play in agricultural N cycling. Although we are unable to accept our third and final hypothesis with any statistical significance, the data from our study does support our prediction. As fertilizer application is known to increase nitrification activity within soils, we anticipated that additional N (from manure or inorganic fertilizer) would increase the abundance of nitrification genes (nxrA and amoA AOB). While not significant, the non-fertilized Minnesota plots reported both lower amoA AOB:16S rRNA and nxrA:16S rRNA ratios. While gene and microbial abundance is not a proxy for microbial activity, this would indicate smaller nitrifying communities in non-fertilized plots (compared to fertilized plots). Throughout our analysis, we found that sampling site had the largest impact on overall functional gene abundance. Comparing the Maryland samples to the Minnesota samples, there were statistically significant differences in 16S rRNA, nxrA, amoA AOB, and nirK abundance (Table 3.4). Comparing all 5 independent sites, there were significant differences in the abundance of 16S rRNA and all the studied functional genes (Table 3.4). While there were less significant differences among the sites once normalized with 16S rRNA abundance, there was still wide variation in the microbial community composition of the various sites. This indicates that, while more research is needed, the site-specific environmental factors may have more control over soil microbial communities and N2O emissions than management techniques. 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CT (MN) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F MD 1 9.76E+19 9.76E+19 16.375 0.0006 Till 1 1.91E+19 1.91E+19 2.240 0.1604 Error 21 1.25E+20 5.96E+180 Error 12 1.03E+20 8.54E+18 C. C. Total 22 2.23E+20 Total 13 1.22E+20 Site Diff. (all) NT v. CT (Rosemount) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F Site 4 1.31E+20 3.29E+19 6.473 0.002 Till 1 3.14E+19 3.14E+19 10.298 0.0326 Error 18 9.14E+19 5.18E+18 Error 4 1.22E+19 3.05E+18 C. C. Total 22 2.23E+20 Total 5 4.36E+19 Site Diff (MN) NT v. CT (Beltsville) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F Site 2 2.06E+19 1.03E+19 1.118 0.361 Till 1 1.98E+16 1.98E+16 0.040 0.8477 Error 11 1.01E+20 9.19E+18 Error 7 3.49E+18 4.99E+17 C. C. Total 13 1.22E+20 Total 8 3.51E+18 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob > Sum of Mean F Prob Source DF Squares Square Ratio F Source DF Squares Square Ratio > F Till 1 1.94E+19 1.94E+19 2.000 0.172 Fert 1 4.66E+19 4.66E+19 5.562 0.0281 Error 21 2.03E+20 9.69E+18 Error 21 1.76E+20 8.39E+18 C. C. Total 22 2.23E+20 Total 22 2.23E+20 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob > Sum of Mean F Prob Source DF Squares Square Ratio F Source DF Squares Square Ratio > F Fert 1 3.52E+18 3.52E+18 0.358 0.561 Org 1 2.80E+18 2.80E+18 2.760 0.1352 Error 12 1.18E+20 9.84E+18 Error 8 8.13E+18 1.02E+18 C. C. Total 13 1.22E+20 Total 9 1.09E+19 115 Table S2. One- Way ANOVA Table for nxrA gene abundance. MD v. MN NT v. CT (MN) Sum of F Sum of Mean F Prob > Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio F MD 1 2.28E+30 2.28E+30 15.261 0.0008 Till 1 1.54E+29 1.54E+29 0.6329 0.4418 Error 21 3.14E+30 1.49E+29 Error 12 2.92E+30 2.43E+29 C. C. Total 22 5.42E+30 Total 13 3.07E+30 Site Diff. (all) NT v. CT (Rosemount) Sum of F Sum of Mean F Prob > Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio F Site 4 3.19E+30 7.98E+29 6.4514 0.002 Till 1 6.28E+29 6.28E+29 3.6465 0.1288 Error 18 2.23E+30 1.24E+29 Error 4 6.89E+29 1.72E+29 C. C. Total 22 5.42E+30 Total 5 1.32E+30 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob > Sum of Mean F Prob > Source DF Squares Square Ratio F Source DF Squares Square Ratio F Site 2 5.43E+29 2.71E+29 1.181 0.343 Till 1 7.07E+26 7.07E+26 0.0751 0.7919 Error 11 2.53E+30 2.30E+29 Error 7 6.59E+28 9.41E+27 C. C. Total 13 3.07E+30 Total 8 6.66E+28 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob > Sum of Mean F Prob > Source DF Squares Square Ratio F Source DF Squares Square Ratio F Till 1 2.19E+29 2.19E+29 0.884 0.358 Fert 1 6.01E+29 6.01E+29 2.6234 0.1202 Error 21 5.20E+30 2.48E+29 Error 21 4.81E+30 2.29E+29 C. C. Total 22 5.42E+30 Total 22 5.42E+30 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob > Sum of Mean F Prob > Source DF Squares Square Ratio F Source DF Squares Square Ratio F Fert 1 8.80E+26 8.80E+26 0.003 0.954 Org 1 1.19E+28 1.20E+28 0.5196 0.4915 Error 12 3.07E+30 2.56E+29 Error 8 1.84E+29 2.30E+28 C. C. Total 13 3.07E+30 Total 9 1.96E+29 116 Table S3. One- Way ANOVA Table for amoA AOB gene abundance. MD v. MN NT v. CT (MN) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 1 1.28E+30 1.28E+30 8.054 0.010 Till 1 2.47E+29 2.47E+29 0.969 0.344 Error 21 3.33E+30 1.58E+29 Error 12 3.06E+30 2.55E+29 C. C. Total 22 4.60E+30 Total 13 3.31E+30 Site Diff. (all) NT v. CT (Rosemount) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F Site 4 2.35E+30 5.88E+29 4.698 0.009 Till 1 2.28E+27 2.28E+27 0.004 0.950 Error 18 2.25E+30 1.25E+29 Error 4 2.05E+30 5.13E+29 C. C. Total 22 4.60E+30 Total 5 2.05E+30 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Site 2 1.06E+30 5.29E+29 2.585 0.120 Till 1 2.01E+25 2.01E+25 0.007 0.937 Error 11 2.25E+30 2.04E+29 Error 7 2.07E+28 2.95E+27 C. C. Total 13 3.31E+30 Total 8 2.07E+28 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Till 1 4.40E+28 4.40E+28 0.203 0.657 Fert 1 2.68E+26 2.68E+26 0.001 0.972 Error 21 4.56E+30 2.17E+29 Error 21 4.60E+30 2.19E+29 C. C. Total 22 4.60E+30 Total 22 4.60E+30 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Fert 1 5.27E+29 5.27E+29 2.275 0.157 Org 1 1.50E+28 1.50E+28 1.092 0.327 Error 12 2.78E+30 2.32E+29 Error 8 1.10E+29 1.38E+28 C. C. Total 13 3.31E+30 Total 9 1.25E+29 117 Table S4. One- Way ANOVA Table for nirS gene abundance. MD v. MN NT v. CT (MN) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F MD 1 7.54E+14 7.54E+14 0.001 0.974 Till 1 1.61E+18 1.61E+18 1.788 0.206 Error 21 1.45E+19 6.92E+17 Error 12 1.08E+19 8.98E+17 C. C. Total 22 1.45E+19 Total 13 1.24E+19 Site Diff. (all) NT v. CT (Rosemount) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F Site 4 9.31E+18 2.24E+18 8.037 0.0007 Till 1 4.88E+17 4.88E+17 5.768 0.074 Error 18 5.21E+18 2.90E+17 Error 4 3.38E+17 8.46E+16 C. C. Total 22 1.45E+19 Total 5 8.26E+17 Site Diff (MN) NT v. CT (Beltsville) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F Site 2 8.40E+18 4.20E+18 11.608 0.002 Till 1 6.22E+16 6.22E+16 0.209 0.661 Error 11 3.98E+18 3.62E+17 Error 7 2.08E+18 2.97E+17 C. C. Total 13 1.24E+19 Total 8 2.14E+18 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob > Sum of Mean F Prob Source DF Squares Square Ratio F Source DF Squares Square Ratio > F Till 1 1.20E+18 1.20E+18 1.889 0.184 Fert 1 1.83E+18 1.83E+18 3.034 0.096 Error 21 1.33E+19 6.35E+17 Error 21 1.27E+19 6.05E+17 C. C. Total 22 1.45E+19 Total 22 1.45E+19 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob > Sum of Mean F Prob Source DF Squares Square Ratio F Source DF Squares Square Ratio > F Fert 1 2.50E+18 2.50E+18 3.031 0.107 Org 1 4.19E+15 4.19E+15 1.092 0.327 Error 12 9.89E+18 8.24E+17 Error 8 2.41E+18 3.02E+17 C. C. Total 13 1.24E+19 Total 9 2.42E+18 118 Table S5. One- Way ANOVA Table for nirK gene abundance. MD v. MN NT v. CT (MN) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 1 4.63E+13 4.63E+13 11.923 0.002 Till 1 1.05E+13 1.05E+13 1.817 0.203 Error 21 8.15E+13 3.88E+12 Error 12 6.96E+13 5.80E+12 C. C. Total 22 1.28E+14 Total 13 8.01E+13 Site Diff. (all) NT v. CT (Rosemount) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 3 6.85E+13 2.29E+13 7.328 0.002 Till 1 2.78E+13 2.78E+13 8.500 0.043 Error 19 5.92E+13 3.12E+12 Error 4 1.31E+13 3.27E+12 C. C. Total 22 1.28E+14 Total 5 4.09E+13 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Site 2 2.23E+13 1.11E+13 2.117 0.167 Till 1 4.17E+11 4.17E+11 2.983 0.128 Error 11 5.79E+13 5.26E+12 Error 7 9.78E+11 1.40E+11 C. C. Total 13 8.01E+13 Total 8 1.39E+12 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Till 1 8.03E+12 8.03E+12 1.407 0.249 Fert 1 1.28E+13 1.28E+13 2.328 0.142 Error 21 1.20E+14 5.70E+12 Error 21 1.15E+14 5.48E+12 C. C. Total 22 1.28E+14 Total 22 1.28E+14 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Fert 1 1.87E+09 1.87E+09 0.000 0.987 Org 1 1.86E+11 1.86E+11 0.352 0.569 Error 12 8.01E+13 6.68E+12 Error 8 4.23E+12 5.28E+11 C. C. Total 13 8.01E+13 Total 9 4.41E+12 119 Table S6. One- Way ANOVA Table for amoA AOB:16S rRNA MD v. MN NT v. CT (MN) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 1 2.72E+10 2.72E+10 3.949 0.060 Till 1 3.20E+10 3.20E+10 3.4755 0.0869 Error 21 1.45E+11 6.88E+09 Error 12 1.10E+11 9.20E+09 C. C. Total 22 1.78E+11 Total 13 1.42E+11 Site Diff. (all) NT v. CT (Rosemount) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F Site 4 5.31E+10 1.33E+10 2.013 0.136 Till 1 1.12E+10 1.12E+10 0.4549 0.537 Error 18 1.19E+11 6.59E+09 Error 4 9.83E+10 2.46E+10 C. C. Total 22 1.72E+11 Total 5 1.09E+11 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Site 2 2.58E+10 1.29E+10 1.217 0.333 Till 1 16004363 16004362 0.0532 0.8242 Error 11 1.17E+11 1.06E+10 Error 7 2.11E+09 3.01E+08 C. C. Total 13 1.42E+11 Total 8 2.12E+09 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Till 1 1.24E+10 1.24E+10 1.631 0.216 Fert 1 2.37E+09 2.37E+09 0.2941 0.5933 Error 21 1.59E+11 7.59E+09 Error 21 1.69E+11 8.06E+09 C. C. Total 22 1.72E+11 Total 22 1.72E+11 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Fert 1 2.58E+10 2.58E+10 2.653 0.129 Org 1 7.29E+08 7.29E+08 1.2626 0.2937 Error 12 1.17E+11 9.72E+09 Error 8 4.62E+09 5.77E+08 C. C. Total 13 1.42E+11 Total 9 5.35E+09 120 Table S7. One- Way ANOVA Table for nirS:16S rRNA.. MD v. MN NT v. CT (MN) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F MD 1 1.510362 1.51036 46.754 <0.0001 Till 1 0.002816 0.002816 0.324 0.579 Error 21 0.67839 0.0323 Error 12 0.104127 0.008677 C. C. Total 22 2.188752 Total 13 0.106943 Site Diff. (all) NT v. CT (Rosemount) Sum of F Sum of Mean F Prob Source DF Sq Mea Sq Ratio Prob>F Source DF Squares Square Ratio > F Site 4 1.60E+00 3.99E-01 12.111 <0.0001 Till 1 0.001621 0.001621 1.060 0.362 Error 18 5.93E-01 3.29E-02 Error 4 0.00612 0.00153 C. C. Total 22 2.19E+00 Total 5 0.007741 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob > Sum of Mean F Prob Source DF Squares Square Ratio F Source DF Squares Square Ratio > F Site 2 0.06958 0.03479 10.243 0.003 Till 1 0.011763 0.011763 0.147 0.713 Error 11 0.037363 0.003397 Error 7 0.559683 0.079955 C. C. Total 13 0.106943 Total 8 0.571446 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob > Sum of Mean F Prob Source DF Squares Square Ratio F Source DF Squares Square Ratio > F Till 1 0.00261 0.00261 0.025 0.876 Fert 1 0.310668 0.310668 3.474 0.076 Error 21 2.186141 0.104102 Error 21 1.878083 0.089433 C. C. Total 22 2.188752 Total 22 2.188752 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob > Sum of Mean F Prob Source DF Squares Square Ratio F Source DF Squares Square Ratio > F Fert 1 0.012394 0.012394 1.573 0.234 Till 1 0.101749 0.101749 1.071 0.331 Error 12 0.094549 0.007879 Error 8 0.760025 0.095003 C. C. Total 13 0.106943 Total 9 0.861774 121 Table S8. One- Way ANOVA Table for nxrA:16S rRNA.. MD v. MN NT v. CT (MN) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 1 2.01E+09 2.01E+09 0.894 0.355 Till 1 5.06E+09 5.06E+09 3.3321 0.0929 Error 21 4.72E+10 2.25E+09 Error 12 1.82E+10 1.52E+09 C. C. Total 22 4.92E+10 Total 13 2.33E+10 Site Diff. (all) NT v. CT (Rosemount) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F Site 4 9.18E+09 2.29E+09 1.033 0.417 Till 1 1.12E+09 1.12E+09 0.4399 0.5434 Error 18 4.00E+10 2.22E+09 Error 4 1.02E+10 2.54E+09 C. C. Total 22 4.92E+10 Total 5 1.13E+10 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Site 2 5.83E+09 2.91E+09 1.837 0.205 Till 1 3.96E+08 3.96E+08 0.1181 0.7412 Error 11 1.74E+10 1.59E+09 Error 7 2.35E+10 3.36E+09 C. C. Total 13 2.33E+10 Total 8 2.39E+10 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Till 1 1.97E+09 1.97E+09 0.877 0.356 Fert 1 4.85E+09 4.85E+09 2.2997 0.1443 Error 21 4.72E+10 2.25E+09 Error 21 4.43E+10 2.11E+09 C. C. Total 22 4.92E+10 Total 22 4.92E+10 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Fert 1 2.93E+09 2.93E+09 1.729 0.213 Org 1 8.45E+09 8.45E+09 4.1493 0.076 Error 12 2.03E+10 1.69E+09 Error 8 1.63E+10 2.04E+09 C. C. Total 13 2.33E+10 Total 9 2.47E+10 122 Table S9. One- Way ANOVA Table for nirK:16S rRNA.. MD v. MN NT v. CT (MN) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 1 1.09E-07 1.09E-07 1.266 0.273 Till 1 1.64E-09 1.64E-09 0.050 0.826 Error 21 1.80E-06 8.58E-08 Error 12 3.91E-07 3.26E-08 C. C. Total 22 1.91E-06 Total 13 3.93E-07 Site Diff. (all) NT v. CT (Rosemount) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F Site 3 1.97E-07 6.58E-08 0.730 0.547 Till 1 3.51E-08 3.51E-08 1.950 0.235 Error 19 1.71E-06 9.01E-08 Error 4 7.19E-08 1.80E-08 C. C. Total 22 1.91E-06 Total 5 1.07E-07 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Site 2 8.87E-08 4.44E-08 1.604 0.245 Till 1 5.40E-08 5.40E-08 0.279 0.614 Error 11 3.04E-07 2.77E-08 Error 7 1.35E-06 1.93E-07 C. C. Total 13 3.93E-07 Total 8 1.41E-06 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Till 1 2.89E-08 2.89E-08 0.323 0.576 Fert 1 8.89E-08 8.89E-08 1.025 0.323 Error 21 1.88E-06 8.96E-08 Error 21 1.82E-06 8.67E-08 C. C. Total 22 1.91E-06 Total 22 1.91E-06 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Fert 1 2.12E-08 2.12E-08 0.683 0.425 Org 1 2.64E-07 2.64E-07 1.813 0.215 Error 12 3.72E-07 3.10E-08 Error 8 1.17E-06 1.46E-07 C. C. Total 13 3.93E-07 Total 9 1.43E-06 123 Table S10. One- Way ANOVA Table for nirS:nirK rRNA.. MD v. MN NT v. CT (MN) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 1 2707746 2707746 32.959 <0.001 Till 1 5108.31 5108.3 0.325 0.579 Error 21 1725269 82156 Error 12 188413.2 15701.1 C. C. Total 22 4433015 Total 13 193521.5 Site Diff. (all) NT v. CT (Rosemount) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F Site 2 2827859 942620 11.158 0.0002 Till 1 12963.25 12963.2 19.290 0.012 Error 19 1605155 84482 Error 4 2687.986 672 C. C. Total 22 4433015 Total 5 15651.23 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Site 2 120113.6 60056.8 8.999 0.005 Till 1 382071.1 382071 2.326 0.171 Error 11 73407.85 6673.4 Error 7 1149676 164239 C. C. Total 13 193521.5 Total 8 1531748 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Till 1 62240 62240 0.299 0.590 Fert 1 485712.8 485713 2.584 0.123 Error 21 4370775 208132 Error 21 3947302 187967 C. C. Total 22 4433015 Total 22 4433015 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Fert 1 42969.49 42969.5 3.425 0.089 Org 1 33385.9 33386 0.132 0.726 Error 12 150552 12546 Error 8 2025867 253233 C. C. Total 13 193521.5 Total 9 2059252 124 Table S11. One- Way ANOVA Table for nxrA:amoA AOB rRNA.. MD v. MN NT v. CT (MN) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F MD 1 1.09E-07 1.09E-07 1.266 0.243 Till 1 0.49753 0.49753 0.088 0.771 Error 21 1.80E-06 8.58E-08 Error 12 67.59068 5.63256 C. C. Total 22 1.91E-06 Total 13 68.08821 Site Diff. (all) NT v. CT (Rosemount) Sum of F Prob> Sum of Mean F Prob Source DF Sq Mea Sq Ratio F Source DF Squares Square Ratio > F Site 3 1.97E-07 6.58E-08 0.730 0.547 Till 1 1.388016 1.38802 0.389 0.567 Error 19 1.71E-06 9.01E-08 Error 4 14.25986 3.56497 C. C. Total 22 1.91E-06 Total 5 15.64788 Site Diff (MN) NT v. CT (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Site 2 13.36908 6.68454 1.344 0.301 Till 1 254.3303 254.33 1.046 0.341 Error 11 54.71913 4.97447 Error 7 1702.53 243.219 C. C. Total 13 68.08821 Total 8 1956.86 NT v. CT (all) F. v. NF (all) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Till 1 45.8348 45.835 0.309 0.584 Fert 1 216.6422 216.642 1.547 0.227 Error 21 3112.172 148.199 Error 21 2941.364 140.065 C. C. Total 22 3158.006 Total 22 3158.006 F v. NF (MN) Organic v. Inorganic (Beltsville) Sum of Mean F Prob Sum of Mean F Prob Source DF Squares Square Ratio > F Source DF Squares Square Ratio > F Fert 1 13.65338 13.6534 3.010 0.108 Org 1 88.7565 88.756 0.340 0.576 Error 12 54.43483 4.5362 Error 8 2087.702 260.963 C. C. Total 13 68.08821 Total 9 2176.458 125 References: Adey, W. H., & Loveland, K. (2007). The Input of Organic Energy: Particulates and Feeding. Dynamic Aquaria, 93?100. https://doi.org/10.1016/B978-0-12-370641-6.50015-7 Aliyu, G., Luo, J., Di, H. J., Liu, D., Yuan, J., Chen, Z., He, T., & Ding, W. (2021). Yield-scaled nitrous oxide emissions from nitrogen-fertilized croplands in China: A meta-analysis of contrasting mitigation scenarios. Pedosphere, 31(2), 231?242. https://doi.org/10.1016/S1002-0160(20)60074-1 Alvarez, L., Bricio, C., Blesa, A., Hidalgo, A., & Berenguer, J. (2014). Transferable denitrification capability of thermus thermophilus. 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