Arts and Sciences, Irving K. Barber School of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Hans, Geetkamal Kaur
Master of Science - MSc
University of British Columbia
Nitrification is one of the processes responsible for the emissions of nitrous oxide (N₂O), an ozone-depleting greenhouse gas, from agricultural soil. In the first step of nitrification, catalyzed by the enzyme ammonia monooxygenase, ammonia is converted into hydroxylamine which further chemically decomposes into N₂O. The contribution of nitrifying Archaeal species to N₂O emissions in woody perennial cropping systems is not well understood. The first study reported here examined the effect of nitrogen application, mulch and soil depth on abundances of nitrifying Archaea and Bacteria, and total Archaea and Bacteria in an apple orchard and a grape vineyard. Soil samples were collected five times (2013-2015) and at three depths. Quantitative polymerase chain reaction was used to determine the abundances of total Archaeal (16SrRNAArch) and Bacterial (16SrRNABac) and nitrifying Archaeal (amoAArch) and Bacterial (amoABac) genes. Total Bacteria were more abundant than total Archaea and both decreased as depth increased. Nitrifying Archaeal abundance increased with depth. Abundance of nitrifying Bacteria was lower than that of nitrifying Archaea when compost was applied and there was no effect of mulch. The second study examined the effect of selective nitrification inhibitors, urea concentration, and soil depth on N₂O emissions in closed in vitro microcosms. Soil samples were collected from a grape vineyard in 2015 and 2016 at two depths (0-15, 15-30 cm). Increasing urea concentrations increased N₂O emissions. When urea was added, soils collected from 0-15 cm produced about 4 times more N₂O than soils collected from 15-30 cm soil depth. Addition of a Bacterial or an Archaeal inhibitor reduced N₂O fluxes from soils at 0-15 cm. These data show that nitrifying Archaea are present in these woody perennial soils, and in vitro studies suggest nitrifying Bacteria may be more important contributors to N₂O emissions than nitrifying Archaea at higher ammonia concentrations.
ABUNDANCE OF AMMONIA-OXIDIZING ARCHAEA AND BACTERIA AND THEIR CONTRIBUTION TO NITROUS OXIDE EMISSIONS IN WOODY PERENNIAL CROPPING SYSTEMS by GEETKAMAL KAUR HANS B.Sc. (Honours), The University of British Columbia, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biochemistry and Molecular Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) January 2019 © Geetkamal Kaur Hans, 2019 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: ABUNDANCE OF AMMONIA-OXIDIZING ARCHAEA AND BACTERIA AND THEIR CONTRIBUTION TO NITROUS OXIDE EMISSIONS IN WOODY PERENNIAL CROPPING SYSTEMS submitted by Geetkamal Kaur Hans in partial fulfillment of the requirements of the degree of Master of Science Dr. Louise Nelson, Biology Department, Irving K. Barber School of Arts and Sciences Supervisor Dr. Craig Nichol, Earth and Environmental Sciences Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Richard Plunkett, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. David Scott, Earth and Environmental Sciences Department, Irving K. Barber School of Arts and Sciences University Examiner iii Abstract Nitrification is one of the processes responsible for the emissions of nitrous oxide (N2O), an ozone-depleting greenhouse gas, from agricultural soil. In the first step of nitrification, catalyzed by the enzyme ammonia monooxygenase, ammonia is converted into hydroxylamine which further chemically decomposes into N2O. The contribution of nitrifying Archaeal species to N2O emissions in woody perennial cropping systems is not well understood. The first study reported here examined the effect of nitrogen application, mulch and soil depth on abundances of nitrifying Archaea and Bacteria, and total Archaea and Bacteria in an apple orchard and a grape vineyard. Soil samples were collected five times (2013-2015) and at three depths. Quantitative polymerase chain reaction was used to determine the abundances of total Archaeal (16SrRNAArch) and Bacterial (16SrRNABac) and nitrifying Archaeal (amoAArch) and Bacterial (amoABac) genes. Total Bacteria were more abundant than total Archaea and both decreased as depth increased. Nitrifying Archaeal abundance increased with depth. Abundance of nitrifying Bacteria was lower than that of nitrifying Archaea when compost was applied and there was no effect of mulch. The second study examined the effect of selective nitrification inhibitors, urea concentration, and soil depth on N2O emissions in closed in vitro microcosms. Soil samples were collected from a grape vineyard in 2015 and 2016 at two depths (0-15, 15-30 cm). Increasing urea concentrations increased N2O emissions. When urea was added, soils collected from 0-15 cm produced about 4 times more N2O than soils collected from 15-30 cm soil depth. Addition of a Bacterial or an Archaeal inhibitor reduced N2O fluxes from soils at 0-15 cm. These data show that nitrifying Archaea are present in these woody perennial soils, and in vitro studies suggest nitrifying Bacteria may be more important contributors to N2O emissions than nitrifying Archaea at higher ammonia concentrations. iv Lay Summary The tree of life consists of three domains: Archaea, Bacteria and Eukarya. In agricultural soil, the importance of Archaeal species in the production of the greenhouse gas, nitrous oxide (N2O), is unknown. Most research has considered Bacteria as the sole producers of N2O; therefore, mitigation strategies target Bacteria to reduce emissions of N2O. We examined the potential of Archaeal species in an apple orchard and grape vineyard to contribute to the production of N2O. Archaea were present in these soils. Archaeal and Bacterial abundance varied with nitrogen source, organic amendment and soil depth. N2O emissions under varying nitrogen concentrations and in the presence of domain-specific inhibitors were studied in the laboratory. N2O emissions were reduced in the presence of a Bacterial-specific inhibitor and high nitrogen concentration. This research furthers our understanding of the organisms and factors contributing to N2O production and potential means to reduce emissions into the atmosphere. v Preface This research was conducted under the supervision of Dr. Louise Nelson at The University of British Columbia. Nitrous oxide flux data collected from the field was provided by Dr. Mesfin Fentabil and Dr. Craig Nichol and was used in some parts of this thesis. All other research including development, methodology, data collection, analyses and interpretations was conducted by myself with the guidance from Dr. Louise Nelson. This thesis was written by myself, with continual support and review from my supervisor, Dr. Louise Nelson, and the supervisory committee, Dr. Craig Nichol, Dr. Richard Plunkett and Dr. Mary Forrest. vi Table of Contents Abstract .................................................................................................................................. iii Lay Summary ......................................................................................................................... iv Preface ..................................................................................................................................... v List of Tables .......................................................................................................................... ix List of Figures ...................................................................................................................... xvi Acknowledgments .............................................................................................................. xviii Dedication ............................................................................................................................. xix 1 Introduction and Literature Review ............................................................................. 1 1.1 Contribution of Agricultural Soils to Greenhouse Gases ................................... 1 1.2 The Nitrogen Cycle ................................................................................................. 3 1.3 Nitrification ............................................................................................................. 5 1.3.1 Ammonia-oxidizing Bacteria ............................................................................... 7 1.3.2 Ammonia-oxidizing Archaea ............................................................................... 9 1.4 Factors Affecting the Abundance of Nitrifying Populations and N2O Emissions ........................................................................................................................... 11 1.4.1 Substrate concentration ....................................................................................... 12 1.4.2 pH ....................................................................................................................... 13 1.4.3 Soil depth ............................................................................................................ 14 1.4.4 Temperature, oxygen availability and water-filled pore space ........................... 15 1.5 Effect of Nitrification Inhibitors ......................................................................... 16 1.6 Methods to Determine Abundance of Archaea in Soil Environments ............. 18 1.7 Project Significance .............................................................................................. 21 1.8 Objectives and Hypotheses .................................................................................. 23 2 Methodology .................................................................................................................. 29 2.1 Field Study ............................................................................................................. 29 vii 2.1.1 Site description ................................................................................................... 29 2.1.2 Sample collection ............................................................................................... 32 2.1.3 Gravimetric soil moisture determination ............................................................ 33 2.1.4 Soil analyses ....................................................................................................... 33 2.1.5 DNA extraction and concentration determination .............................................. 34 2.1.6 Quantitative real time PCR (qPCR) .................................................................... 35 2.1.6.1 Generation of standard curve ...................................................................... 35 2.1.6.2 Primers ........................................................................................................ 36 2.1.6.3 Master mixes ............................................................................................... 36 2.1.6.4 Preparation of qPCR plates ......................................................................... 37 2.1.6.5 qPCR program ............................................................................................ 37 2.1.6.6 Calculations to obtain copy number per g dry soil ..................................... 38 2.2 In Vitro Microcosm Studies ................................................................................. 38 2.2.1 Sample collection ............................................................................................... 38 2.2.2 Experimental design ........................................................................................... 39 2.2.3 Inhibitor and urea application ............................................................................. 39 2.2.3.1 Experiment 1- Varying urea concentration and soil depth ......................... 43 2.2.3.2 Experiment 2- Varying concentrations of ATU and PTIO inhibitors ........ 44 2.2.3.3 Experiment 3- Varying urea concentrations and soil depth with fixed concentrations of domain-specific inhibitors .............................................. 44 2.2.3.4 Gas Collection and Calculations performed ............................................... 45 2.3 Statistical Analysis ................................................................................................ 47 3 Results ............................................................................................................................ 49 3.1 Field Experiment .................................................................................................. 49 3.1.1 Apple orchard ..................................................................................................... 49 3.1.2 Grape vineyard ................................................................................................... 62 3.2 In vitro Microcosm Experiment ........................................................................... 74 3.2.1 Experiment 1- Varying urea concentration and soil depth ................................. 74 3.2.2 Experiment 2- Varying concentrations of ATU and PTIO inhibitors ................ 76 3.2.3 Experiment 3-Varying urea concentrations and soil depth with fixed concentrations of domain-specific inhibitors ...................................................... 83 viii 4 Discussion ...................................................................................................................... 89 4.1 Field Study ............................................................................................................. 89 4.1.1 Effect of mulch and carbon concentration .......................................................... 90 4.1.2 Effect of nitrogen source and concentration ....................................................... 92 4.1.3 Effect of soil depth ............................................................................................. 95 4.2 In Vitro Microcosm Experiments ........................................................................ 96 4.2.1 Effect of increasing urea concentration .............................................................. 96 4.2.2 Effect of soil depth ............................................................................................. 98 4.2.3 Effect of increasing inhibitor concentration ..................................................... 100 4.3 Relationship Between the Field and the In-Vitro Microcosm Study .............. 102 5 Conclusions .................................................................................................................. 104 Bibliography ........................................................................................................................ 107 Appendices .......................................................................................................................... 119 Appendix A: Plot Design ................................................................................................ 119 Appendix B: Creation of Standard Curves with Plasmid DNA Templates .............. 121 Appendix C: Additional Statistical Analysis for the Field Study ............................... 124 Appendix D: Additional Statistical Analysis for the In Vitro Microcosm Study ...... 154 ix List of Tables Table 1: Summary of the variety, rootstock, soil type, established year, fertilizer, mulch and irrigation applications of both apple and grape plots sampled (Hannam et al. 2016b). . 30 Table 2: Primer sequences used to quantify total and nitrifying Archaeal and Bacterial populations with qPCR. .................................................................................................. 36 Table 3: PCR program for qPCR amplification and determination of copy number of total and nitrifying Bacteria and Archaea. .............................................................................. 37 Table 4: Treatments used in experiment 1 .............................................................................. 40 Table 5: Treatments used in experiment 2 .............................................................................. 41 Table 6: Treatments used in experiment 3.. ............................................................................ 42 Table 7: Means over all treatments at each sampling time of total and nitrifying Archaeal and Bacterial abundance in an apple orchard. ....................................................................... 50 Table 8: Ratio between abundances of total and nitrifying Archaeal and Bacterial populations over all treatments at each sampling time in an apple orchard. ...................................... 50 Table 9: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on percent soil nitrogen in an apple orchard ............... 59 Table 10: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10cm, 10-20 cm, 20-30 cm) on percent soil carbon in an apple orchard ................... 59 Table 11: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil ammonium-nitrogen in an apple orchard ........ 60 Table 12: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil nitrate-nitrogen in an apple orchard. ............... 60 Table 13: Pearson correlations between nitrifying Bacterial and Archaeal abundance and different parameters in the apple orchard. ...................................................................... 61 Table 14: Pearson correlations between total Bacterial and Archaeal abundance and percent nitrogen and carbon in the apple orchard soil ................................................................. 61 Table 15: Mean of all treatments at each sampling time of total and nitrifying Archaeal and Bacterial abundance in a grape vineyard. ....................................................................... 62 Table 16: Ratios between abundances of total and nitrifying Archaeal and Bacterial populations at all sampling times in a grape vineyard. ................................................... 63 x Table 17: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on percent soil nitrogen in a grape vineyard ............... 71 Table 18: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on percent soil carbon in a grape vineyard .................. 71 Table 19: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil ammonium-nitrogen in a grape vineyard ........ 72 Table 20: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil nitrate-nitrogen in a grape vineyard ................ 72 Table 21: Pearson correlations between nitrifying Bacterial and Archaeal abundance in the grape vineyard with combined data from August and September 2013 and 2014. ........ 73 Table 22: Pearson correlations between total Bacterial and Archaeal abundance in the grape vineyard with combined data from August and September 2013 and 2014. .................. 73 Table 23: Effects of three urea concentrations (0, 20, 200 mg N/kg of soil) on N20 emissions in an in vitro experiment over a 14-day period. ............................................................. 75 Table A. 1: SPSS output between total and nitrifying populations in both apple and grape plot for August 2013 sampling. .................................................................................... 124 Table A. 2: SPSS output between total and nitrifying populations in both apple and grape plot for September 2013 sampling. ............................................................................... 124 Table A. 3: SPSS output between total and nitrifying populations in both apple and grape plot for August 2014 sampling. .................................................................................... 125 Table A. 4: SPSS output between total and nitrifying populations in both apple and grape plot for September 2014 sampling. ............................................................................... 125 Table A. 5: SPSS output between total and nitrifying populations in both apple and grape plot for June 2015 sampling in the apple orchard. ....................................................... 126 Table A. 6: SPSS output for determining variation in sampling dates for total bacterial abundance in the apple orchard. ................................................................................... 126 Table A. 7: SPSS output for determining variation in sampling dates for total archaeal abundance in the apple orchard. ................................................................................... 127 Table A. 8: SPSS output for determining variation in sampling dates for nitrifying bacterial abundance in the apple orchard. ................................................................................... 127 Table A. 9: SPSS output for determining variation in sampling dates for nitrifying archaeal abundance in the apple orchard. ................................................................................... 128 xi Table A. 10: ANOVA results for total Bacterial abundance in August 2013 samples in the apple orchard. ............................................................................................................... 128 Table A. 11: ANOVA results for total Bacterial abundance in September 2013 samples in the apple orchard. ............................................................................................................... 128 Table A. 12: ANOVA results for total Bacterial abundance in August 2014 samples in the apple orchard. ............................................................................................................... 129 Table A. 13: ANOVA results for total Bacterial abundance in September 2014 samples in the apple orchard. ............................................................................................................. 129 Table A. 14: ANOVA results for total Bacterial abundance in June 2015 samples in the apple orchard. ......................................................................................................................... 129 Table A. 15: ANOVA results for total archaeal abundance in August 2013 samples in the apple orchard. ............................................................................................................... 130 Table A. 16: ANOVA results for total archaeal abundance in September 2013 samples in the apple orchard. ............................................................................................................... 130 Table A. 17: ANOVA results for total archaeal abundance in August 2014 samples in the apple orchard. ............................................................................................................... 130 Table A. 18: ANOVA results for total archaeal abundance in September 2014 samples in the apple orchard. ............................................................................................................... 131 Table A. 19: ANOVA results for total archaeal abundance in June 2015 samples in the apple orchard. ......................................................................................................................... 131 Table A. 20: ANOVA results for nitrifying Bacterial abundance in August 2013 samples in the apple orchard. ......................................................................................................... 131 Table A. 21: ANOVA results for nitrifying Bacterial abundance in September 2013 samples in the apple orchard. ..................................................................................................... 132 Table A. 22: ANOVA results for nitrifying Bacterial abundance in August 2014 samples in the apple orchard. ......................................................................................................... 132 Table A. 23: ANOVA results for nitrifying Bacterial abundance in September 2014 samples in the apple orchard. ..................................................................................................... 132 Table A. 24: ANOVA results for nitrifying Bacterial abundance in June 2015 samples in the apple orchard. .............................................................................................................. 133 Table A. 25: ANOVA results for nitrifying Archaeal abundance in August 2013 samples in the apple orchard. ......................................................................................................... 133 xii Table A. 26: ANOVA results for nitrifying Archaeal abundance in September 2013 samples in the apple orchard. ..................................................................................................... 133 Table A. 27: ANOVA results for nitrifying Archaeal abundance in August 2014 samples in the apple orchard. ......................................................................................................... 134 Table A. 28: ANOVA results for nitrifying Archaeal abundance in September 2014 samples in the apple orchard. ..................................................................................................... 134 Table A. 29: ANOVA results for nitrifying Archaeal abundance in June 2015 samples in the apple orchard. ............................................................................................................... 134 Table A. 30: SPSS output for univariate analysis of variance in the apple orchard (August 2013). ............................................................................................................................ 135 Table A. 31: SPSS output for univariate analysis of variance in the apple orchard (September 2013). ............................................................................................................................ 136 Table A. 32: SPSS output for univariate analysis of variance in the apple orchard (August 2014). ............................................................................................................................ 137 Table A. 33: SPSS output for univariate analysis of variance in the apple orchard (September 2014). ............................................................................................................................ 138 Table A. 34: SPSS output for univariate analysis of variance in the apple orchard (June 2015). ............................................................................................................................ 139 Table A. 35: SPSS output for determining variation in sampling dates for total Bacterial abundance in the grape vineyard. ................................................................................. 140 Table A. 36: SPSS output for determining variation in sampling dates for total Archaeal abundance in the grape vineyard. ................................................................................. 140 Table A. 37: SPSS output for determining variation in sampling dates for nitrifying Bacterial abundance in the grape vineyard. ................................................................................. 141 Table A. 38: SPSS output for determining variation in sampling dates for nitrifying Archaeal abundance in the grape vineyard. ................................................................................. 141 Table A. 39: ANOVA results for total Bacterial abundance in August 2013 samples in the grape vineyard. ............................................................................................................. 142 Table A. 40: ANOVA results for total Bacterial abundance in September 2013 samples in the grape vineyard. ............................................................................................................. 142 Table A. 41: ANOVA results for total Bacterial abundance in August 2014 samples in the grape vineyard. ............................................................................................................. 142 xiii Table A. 42: ANOVA results for total Bacterial abundance in September 2014 samples in the grape vineyard. ............................................................................................................. 143 Table A. 43: ANOVA results for total Bacterial abundance in June 2015 samples in the grape vineyard. ....................................................................................................................... 143 Table A. 44: ANOVA results for total Archaeal abundance in August 2013 samples in the grape vineyard. ............................................................................................................. 143 Table A. 45: ANOVA results for total Archaeal abundance in September 2013 samples in the grape vineyard. ............................................................................................................. 144 Table A. 46: ANOVA results for total Archaeal abundance in August 2014 samples in the grape vineyard. ............................................................................................................. 144 Table A. 47: ANOVA results for total Archaeal abundance in September 2014 samples in the grape vineyard. ............................................................................................................. 144 Table A. 48: ANOVA results for total Archaeal abundance in June 2015 samples in the grape vineyard. ....................................................................................................................... 145 Table A. 49: ANOVA results for nitrifying Bacterial abundance in August 2013 samples in the grape vineyard. ....................................................................................................... 145 Table A. 50: ANOVA results for nitrifying Bacterial abundance in September 2013 samples in the grape vineyard. ................................................................................................... 145 Table A. 51: ANOVA results for nitrifying Bacterial abundance in August 2014 samples in the grape vineyard. ....................................................................................................... 146 Table A. 52: ANOVA results for nitrifying Bacterial abundance in September 2014 samples in the grape vineyard. ................................................................................................... 146 Table A. 53: ANOVA results for nitrifying Bacterial abundance in June 2015 samples in the grape vineyard. ............................................................................................................. 146 Table A. 54: ANOVA results for nitrifying Archaeal abundance in August 2013 samples in the grape vineyard. ....................................................................................................... 147 Table A. 55: ANOVA results for nitrifying Archaeal abundance in September 2013 samples in the grape vineyard. ................................................................................................... 147 Table A. 56: ANOVA results for nitrifying Archaeal abundance in August 2014 samples in the grape vineyard. ....................................................................................................... 147 Table A. 57: ANOVA results for nitrifying Archaeal abundance in September 2014 samples in the grape vineyard. ................................................................................................... 148 xiv Table A. 58: ANOVA results for nitrifying Archaeal abundance in June 2015 samples in the grape vineyard. ............................................................................................................. 148 Table A. 59: SPSS output for univariate analysis of variance in the grape vineyard (August 2013). ............................................................................................................................ 149 Table A. 60: SPSS output for univariate analysis of variance in the grape vineyard (September 2013). ........................................................................................................ 150 Table A. 61: SPSS output for univariate analysis of variance in the grape vineyard (August 2014). ............................................................................................................................ 151 Table A. 62: SPSS output for univariate analysis of variance in the grape vineyard (September 2014). ........................................................................................................ 152 Table A. 63: SPSS output for univariate analysis of variance in the grape vineyard (June 2015). ............................................................................................................................ 153 Table A. 64: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 1 of 14. .................................................................................... 154 Table A. 65: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 2 of 14. ................................................................................... 155 Table A. 66: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 3 of 14. .................................................................................... 156 Table A. 67: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 4 of 14. .................................................................................... 157 Table A. 68: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 5 of 14. .................................................................................... 158 Table A. 69: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 6 of 14. .................................................................................... 159 Table A. 70: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 7 of 14. .................................................................................... 160 Table A. 71: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 8 of 14. .................................................................................... 161 Table A. 72: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 9 of 14. .................................................................................... 162 Table A. 73: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 10 of 14. .................................................................................. 163 xv Table A. 74: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 11 of 14. .................................................................................. 164 Table A. 75: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 12 of 14. .................................................................................. 165 Table A. 76: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 13 of 14. .................................................................................. 166 Table A. 77: SPSS output for univariate analysis of variance for experiment 1 in an in vitro soil microcosm on Day 14 of 14. .................................................................................. 167 Table A. 78: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of bacterial inhibitor (ATU) concentration on N2O emissions. .................... 168 Table A. 79: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of Archaeal inhibitor (PTIO) concentration on N2O emissions. .................. 169 Table A. 80: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of bacterial inhibitor (ATU) concentration with the addition of urea. ......... 170 Table A. 81: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of Archaeal inhibitor (PTIO) concentration with the addition of urea. ........ 171 Table A. 82: SPSS output for univariate analysis of variance for Experiment 3, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm. ...................................................................... 172 xvi List of Figures Figure 1: Illustration of oxic and anoxic processes of the nitrogen cycle (Dodds et al. 2010). 4 Figure 2: Urea decomposition by urease into 2 molecules of NH3 and CO2 ............................ 6 Figure 3: The process of nitrification in Bacteria showing the oxidation of ammonia (NH3) to nitrate (NO3-). ................................................................................................................... 6 Figure 4: Treatment layout of the apple plot at Summerland Research and Development Centre (Summerland, BC, Canada). ............................................................................... 31 Figure 5: The abundance of total Bacterial populations in the apple orchard determined by qPCR ............................................................................................................................... 53 Figure 6: The abundance of total Bacterial (A), nitrifying Bacterial (B), total Archaeal (C) and nitrifying Archaeal (D) in the apple plot in June 2015. ........................................... 54 Figure 7: The abundance of nitrifying Bacteria in the apple orchard determined by qPCR. . 55 Figure 8:The abundance of total Archaeal populations in the apple orchard as determined by qPCR ............................................................................................................................... 56 Figure 9: The abundance of nitrifying Archaeal populations in the apple orchard determined by qPCR .......................................................................................................................... 57 Figure 10: The abundance of total Bacterial populations in the grape vineyard determined by qPCR ............................................................................................................................... 65 Figure 11: The abundance of total Bacterial (A), nitrifying Bacterial (B), total Archaeal (C) and nitrifying Archaeal (D) in the grape plot in June,2015. ........................................... 66 Figure 12: The abundance of total Archaeal populations in the grape vineyard determined by qPCR ............................................................................................................................... 67 Figure 13: The abundance of nitrifying Bacterial populations in the grape vineyard determined by qPCR. ...................................................................................................... 68 Figure 14: The abundance of nitrifying Archaeal populations in the grape vineyard determined by qPCR ....................................................................................................... 69 Figure 15: Effects of three urea concentrations (0, 20, 200 mg N/kg of soil) on Total N2O emissions in an in vitro experiment over a 14-day period. ............................................. 76 Figure 16: Effect of Bacterial inhibitor (ATU, Green) concentration, Archaeal inhibitor (PTIO, Blue) concentration and/or urea (200 mg N/kg of soil) on nitrous oxide .......... 79 xvii Figure 17A: Effect of Bacterial inhibitor (ATU, N2O collected on day 4) concentration; and B: Archaeal inhibitor (PTIO, N2O collected on day 6) concentration. ........................... 80 Figure 18: Effect of Bacterial inhibitor (ATU) concentration, Archaeal inhibitor (PTIO) concentration and/or urea addition on total nitrous oxide emissions ............................. 81 Figure 19: Effect of Bacterial inhibitor concentration, Archaeal inhibitor concentration and/or urea addition on nitrifying Bacterial and nitrifying Archaeal abundance .......... 82 Figure 20: Effect of urea concentration and soil depth on nitrous oxide emissions in an in vitro microcosm study .................................................................................................... 85 Figure 21: Effect of Bacterial inhibitor, Archaeal inhibitor concentration, urea concentration and soil depth on nitrous oxide emissions ...................................................................... 86 Figure 22: Effect of Bacterial inhibitor and Archaeal inhibitor concentration, urea concentration and soil depth in an in vitro microcosm study ......................................... 87 Figure 23: Effect of Bacterial inhibitor and Archaeal inhibitor, urea concentration and soil depth on nitrifying Bacterial and nitrifying Archaeal abundance ................................. 88 Figure A 1: Apple plot treatment layout (K. Hannam unpublished 2015) ........................... 119 Figure A 2: Grape plot treatment layout (K. Hannam unpublished 2015) ........................... 120 xviii Acknowledgments Firstly and foremost, I would like to thank my supervisor, Dr. Nelson for her continued support and motivation throughout my years from working as a laboratory assistant to being a graduate student. This process has been a learning curve and I could not have done it without the encouragement from Dr. Nelson. Secondly, I would also like to thank my committee member, Dr. Craig Nichol, for showing me how research is a learning process and some very important life lessons. Additionally, I thank him for all his help in the second part of my study from the bare basics of how to work with gases to analyzing the results of the experiment. I appreciate the valuable time of Mesfin Fentabil and providing me with the ins and outs of using the gas chromatograph. I am also very grateful to have the guidance and constructive comments of my additional committee members, Dr. Mary Forrest and Dr. Richard Plunkett. Additionally, I really appreciate SuRDC in Summerland, B.C for providing sampling sites and equipment, Dr. Mark Rheault for access to the Bio-Rad CFX-96 thermal cycler, and Dr. Sophie Wertz for provision of the amoA plasmid. I would like to thank Agriculture and Agri-Food Canada for providing the funding required in order to conduct this research and members of the Nelson lab, for their help in the lab. Lastly, I can’t thank you enough, my parents and my brother, for believing in my capabilities and the strength that they provided me every step of the way in all my years of study. I can’t appreciate my husband enough for his push throughout this seemingly big task and for his tremendous support. This accomplishment would not have been possible without my family by my side at all times. xix Dedication Mom and Dad, Thank you for always trusting me and picking me up when I needed it most. 1 1 Introduction and Literature Review 1.1 Contribution of Agricultural Soils to Greenhouse Gases Greenhouse gases are a significant cause of the increase in average annual temperature and warming of Earth’s atmosphere. Greenhouse gases, including carbon dioxide, methane, nitrous oxide (N2O), and water vapour trap the infrared radiation within the atmosphere. According to the Government of Canada, 81% of the total greenhouse gas emissions are due to the energy sector (includes transport and stationary combustion sources) and 8% of the total emissions are generated by agricultural processes and product use (Statistics Canada 2015). One of the major gases produced by agricultural soils is N2O, a consequence of the rising use of nitrogen fertilizer (Butterbach-Bahl et al. 2013). N2O is responsible for 5-7% of the observed greenhouse effect; it is accountable for increasing Earth’s temperature and might contribute significantly to global warming (Hatzenpichler 2012). Although, it only comprises 320 ppb of the earth's atmosphere, it has a global warming potential (GWP) approximately 300 times greater than that of carbon dioxide (molecule to molecule) (Smith 2010). GWP is determined by calculating the amount of heat trapped by a molecule of gas in a period of 100 years compared to that of carbon dioxide (United States Environmental Protection Agency n.d.). More importantly, due to N2O’s reaction with atomic oxygen, nitrous oxide has a destructive effect on the Earth’s ozone layer. In the twenty-first century, nitrous oxide may be the most probable cause of ozone-layer depletion (Smith 2017). About 70% of this gas is produced by fertilized agricultural and natural soils (Butterbach-Bahl et al. 2013, Statistics Canada 2015, Smith 2017). As fertilizer use in agricultural production increases with a growing population, it is a challenge 2 to develop mitigation strategies for N2O and to reduce its effects on the atmosphere and environment. Nitrogen fertilizer (for example, in the form of urea and calcium nitrate) contributes to about half of the worldwide anthropogenic N2O emissions from agricultural soils (summarized in Ruser and Schulz 2015). Fertilizer is applied to the surface or incorporated into agricultural soil to stimulate the growth of plants. It is generally beneficial in that it helps promote plant growth by providing a source of nitrogen which is an important component of proteins and nucleic acids of all organisms (Pajares and Bohannan 2016). However, fertilizer is often not administered appropriately and farmers may not have sufficient information to apply it sustainably (Walsh 2007). Negative impacts of N fertilizer include the possibility of leaching of nitrate to surface and groundwater or gaseous emissions in the form of N2O (Mosier et al. 2002). The entry of fertilizers into waterways is correlated with harmful microscopic algal blooms causing illness in humans, fish and other marine life and changes to the ecosystem (Walsh 2007, Statistics Canada 2015, Villacorte et al. 2015). As nitrogen fertilizer use for agricultural soils increases, the production of N2O will continue to rise (United States Environmental Protection Agency n.d.). Some nitrogen fertilizers contain or break down into the substrates ammonia (NH3) and/or nitrate (NO3-), which are the starting points of two major pathways leading to the production of N2O: nitrification and denitrification, respectively. Nitrification is the process by which ammonia is converted to nitrite by ammonia-oxidizing Bacteria (AOB) and Archaea (AOA) and then to nitrate by nitrite-oxidizing Bacteria. N2O is produced in a side reaction during ammonia oxidation. Nitrate is reduced to N2O and N2 by denitrifying Bacteria during denitrification. These organisms are thought to be the major contributors to global N2O emissions 3 (Hatzenpichler 2012). Losses of nitrate into the groundwater lead to reduction in the efficiency of ammonia-based fertilizers. Ammonium (NH4+) is primarily retained in the soil due to its positive charge but negatively-charged NO3- is easily leached, increasing groundwater pollution. Knowledge of the key organisms in nitrification and their contribution to N2O production is therefore essential for environmental and economic reasons (Tourna et al. 2008). New mitigation strategies need to be studied and developed to reduce the emission of N2O. Thus, a stronger understanding of the biological pathways and the organisms involved needs to be developed (Hink et al. 2017). Very little is known about the contribution of nitrifying Archaea to N2O emissions in agricultural systems (Prosser and Nicol 2008, Schauss et al. 2009, Gubry-Rangin et al. 2010, Di et al. 2010b, Taylor et al. 2010, Bollmann et al. 2011, Morimoto et al. 2011, Zhang et al. 2012, Hu et al. 2014, Podolyan et al. 2014). 1.2 The Nitrogen Cycle In the terrestrial system, one of the key nutrient cycles is the nitrogen cycle (Hayatsu et al. 2008). Nitrogen is required for the synthesis of nucleic acids and proteins and is the fifth most abundant element in our solar system. Dinitrogen gas is inert and makes up most of the nitrogen in the atmosphere and in natural waters (Hayatsu et al. 2008). Other gaseous forms of nitrogen occur at trace levels in the environment (Ward et al. 2011). Fixed nitrogen comprises a very small fraction of the total nitrogen inventory on Earth, but this form is the most important to biogeochemical processes and to the sustenance of life (Mao et al. 2011). Five major processes involved in the production and cycling of fixed nitrogen include: nitrogen fixation, immobilization, mineralization, nitrification and denitrification (Figure 1) 4 (Mao et al. 2011). Biological nitrogen fixation reduces atmospheric dinitrogen gas (N2) to NH3 that is available to plants and other organisms (Mao et al. 2011). This is an important natural input of available nitrogen in many terrestrial habitats (Mao et al. 2011). Immobilization is the uptake of dissolved inorganic N (NH3 and NO3-) by plants and microbes and conversion into complex N-containing organic compounds (Levy-Booth et al. 2014). Mineralization involves the breakdown of complex N-containing organic compounds, such as those in mulch, manure, and soil into inorganic compounds (Robertson and Vitousek 2009). Figure 1: Illustration of oxic and anoxic processes of the nitrogen cycle. Reprinted with permission of Elsevier. (Dodds et al. 2010). Nitrification is carried out by ammonia oxidizers in oxic conditions and at low moisture content (Hink et al. 2017). Both Bacteria and Archaea are able to nitrify. AOB oxidize NH4+ into NO3- in two major steps; in the first step NH3 is oxidized to nitrite (NO2-) and in the second step NO2- is oxidized to NO3- (Mao et al. 2011). In an intermediate step, 5 NH3 is oxidized to hydroxylamine (NH2OH) which can be further converted into nitrous oxide (N2O). Thus far, Archaea are known to oxidize NH3 into NH2OH, but the ability to carry out the other steps of this pathways is still under investigation (Hatzenpichler 2012). In low oxygen conditions and at high moisture content, denitrification is the primary contributor to greenhouse gas emissions (Figure 1) (Hatzenpichler 2012). Denitrification involves the conversion of NO3-, the product of nitrification, into NO2-, then into nitric oxide (NO) and further into N2O and N2 (Mao et al. 2011). Denitrification is predominantly carried out by facultative heterotrophic denitrifiers and leads to release of gaseous NO, N2O and N2 (Ward et al. 2011, Hink et al. 2017). Under limiting oxygen conditions, NO2- can be enzymatically reduced into NO and further reduced into N2O, in a process called nitrifier denitrification. This process is carried out by autotrophic ammonia oxidizers rather than heterotrophic denitrifiers (dentrification) (Kool et al. 2011). In soil, these pathways usually occur simultaneously and in close vicinity, but in different microenvironments (Canfield et al. 2010).The focus of this thesis is the role of nitrification in managed woody perennial cropping systems and its potential link to N2O emissions. 1.3 Nitrification Nitrification is mainly carried out by AOA and AOB. Depending on the source of N being applied to the soils, preliminary steps might be required before the initiation of the nitrification pathway. One form of nitrogen fertilizer is urea (CH4N2O), which is converted into two molecules of NH3 by the urease enzyme. This enzyme is known to be abundant in Bacteria and present in marine species of Thaumarchaea, the dominant taxon of the nitrifying Archaeal species (Witte 2011, Tourna et al. 2011, Santoro et al. 2017) (Figure 2). 6 !"#$%& + "%& ()*+,*-⎯⎯⎯/ 2$"2 + !&% The first-step of nitrification is the rate-limiting step, which is catalyzed by the membrane-bound enzyme, ammonia monoxygenase (AMO) (Ward et al. 2011) (Figure 3). $"2 345-⎯/ $"%&" 635-⎯/ $&% 758-⎯/ $&29 N2O In AOB, the second step is catalyzed by hydroxylamine oxidase (HAO), an enzyme that has not been confirmed in Archaea (Schleper and Nicol 2010, Levy-Booth et al. 2014). N2O can be produced from hydroxylamine (NH2OH) in a side reaction during nitrification (Figure 3). The oxidation of NH3 to NO2- results in the net release of two protons to the surrounding environment (Hatzenpichler 2012; Ward et al. 2011). Ammonia oxidation causes the acidification of the growth medium or environment and thereby shifts the NH4+/NH3 equilibrium towards NH4+, lowering the concentration of NH3 used by AMO for nitrification (Ward et al. 2011). The last step of nitrification is the conversion of NO2- into NO3- and this step is catalyzed by nitrite oxidoreductase (NOR) (Butterbach-Bahl et al. 2013; Levy-Booth et al. 2014). Consequently, nitrification determines the concentrations of two major inorganic nitrogen sources for plants, NH3 and NO3- (Butterbach-Bahl et al. 2013). The concentration Figure 3: The process of nitrification in Bacteria showing the oxidation of ammonia (NH3) to nitrate (NO3-). Ammonia monooxygenase (AMO), hydroxylamine oxidase (HAO) and nitric oxide reductase (NOR) are enzymes which catalyze this pathway. Figure 2: Urea decomposition by urease into 2 molecules of NH3 and CO2 7 of these inorganic compounds in soil has doubled since the beginning of the industrial age, mainly due to the increase in agricultural use of fertilizer (Conen and Neftel 2010) . AMO catalyzes the first step of the reaction in nitrification. This enzyme is a part of a more diverse family, called copper-containing, membrane-associated monoxygenases (CuMMOs) that engage in the oxidation of NH3, methane and butane. AMO is a heterotrimer and consists of three subunits: amoA, amoB, and amoC (Hatzenpichler 2012; Ward et al. 2011). In Bacteria, AMOs are commonly genetically encoded in the subunit order CAB, while in AOA the arrangement of these genes varies among different lineages. According to Hakemian and Rosenzweig (2007) , the inactivator acetylene binds to amoA (AMO subunit A), which may suggest that the A subunit of AMO contains the active site for NH3. The A subunit is also a highly conserved gene compared with the amoB and amoC subunits (Matsuno et al. 2013). Therefore, quantifying the amoA gene of both Archaea and Bacteria allows direct comparison between these domains. Despite the functional similarity of amoA between domains, the sequence is not conserved. Because of this difference, the potential contribution of N2O production from either species can be determined. 1.3.1 Ammonia-oxidizing Bacteria Ammonia-oxidizing Bacteria (AOB) were thought to be the only group to have the potential to carry out nitrification before the amoA gene was discovered in Archaea (Hayatsu et al. 2008; Leininger et al. 2006). AOB carry out nitrification in broadly-distributed environments including soil and fresh and marine waters. Nitrifying Bacteria are largely chemolithoautotrophs; they use NH3 oxidation for energy generation and for the fixation of carbon dioxide, their carbon source (Ward et al. 2011). Some can take up and incorporate 8 simple organic compounds in combination with another energy source under aerobic conditions; none is known to use organic compounds as a sole energy source (Ward et al. 2011). Nitrosomas europaea, the first discovered nitrifying bacterium, has the advantage of growing relatively rapidly for an AOB and being able to tolerate high concentrations of NH3 (up to 100 nM) and NO2- (accumulating up to 25 nM in batch cultures) (Ward et al. 2011). Most of the known characteristics of the nitrifying Bacterial populations are based on this bacterium. Studies of other AOB, including Nitrosomonas eutropha, Nitrosococcus oceani and Nitrosospira sp. strain NpAV, have added to our understanding of the process of nitrification and further production of N2O (Ward et al. 2011). The genomes of AOB encode ammonia monooxygenase (AMO) and/or hydroxylamine oxidoreductase (HAO) which perform the oxidation of NH3 (Ward et al. 2011). These enzymes control the concentration of NO2- and NH3 in the surrounding environment (Ward et al. 2011). NO2- and NH3 have been shown to have many effects on nitrifying Bacterial populations; NO2- inactivates AMO activity, altering the concentration of the substrate and end product of the pathway (Ward et al. 2011). Consequently, the accumulation of NO2- leads to a decrease in nitrification rates, but in low oxygen conditions it may lead to an increase in denitrification rates (Ward et al. 2011). Increasing concentrations of NH3 will lead to an increase in nitrification rates (Ward et al. 2011). An increase in nitrification rates through fertilizer application, can lead to an increase in N2O production by either Bacteria or Archaea. The relationship between nitrogen concentration and the ratio of N2O production by each community is unknown. 9 1.3.2 Ammonia-oxidizing Archaea Archaea are the third domain of living systems beside Bacteria and Eukarya. Only 38% of their genes appear similar to genes of fully-sequenced species in other domains (Redei 2008). Archaea’s overall cellular architecture is similar to that of Bacteria (Forterre 2013). For this reason, Archaea were first categorized as Archaebacteria in the Bacterial lineage (Forterre 2013). Both Bacteria and Archaea have circular DNA, are relatively similar in size, and have no nucleus or membrane bound organelles (Forterre 2013). The differences are in the functionality of Archaea and Bacteria. The cell wall of Archaea does not contain peptidoglycan unlike that of Bacteria (Forterre 2013). Additionally, cell membranes of some Archaeal species contain ether lipids instead of ester lipids which may allow the Archaeal membrane to be more durable in extreme environments (Forterre 2013). Multiple RNA polymerases have been found in Archaea whereas Bacteria only contain one type (Forterre 2013). RNA processing occurs in Eukarya but now also has been observed in some species of Archaea (Forterre 2013). This process allows for the joining of protein-coding nucleotide sequences (exons) together while removing the non-translated additional nucleotide sequences (introns) (Redei 2008). The sequence and protein components of the Archaeal ribosome are similar to that of eukaryotes (Redei 2008). According to genome analysis of species from all three domains, there seems to be more similarity between Archaea and Eukarya than between Archaea and Bacteria. Archaea were first discovered in extreme environments such as hot springs, deep-sea hydrothermal vents, highly saline seas (DeLong 1992). Archaea can be methanogens, extreme halophiles, sulfate-reducing archaea, and extreme thermophiles (DeLong 1992). The first reports of Archaea in soil, identified by molecular techniques, were published in 1997 in agricultural soils in Wisconsin (Bintrim et al. 1997), in boreal forest soil in Finland (Jurgens 10 et al. 1997), and in tropical forest soil in Brazil (Borneman and Triplett 1997). The Archaeal domain has been separated into five phyla: Crenarchaeota, Euryarchaeota, Korarchaeota, Thaumarchaeota and Nanoarchaeota. Most mesophilic Crenarchaeota found in soil environments belong to four distinct clades; Group I.1a, I.1b, I.1c and I.3. Group I.1b Crenarchaeota are the most abundant and are commonly found in terrestrial and fresh water environments (Schleper et al. 2005). In high organic and acidic soils, Group I.1c Crenarchaeota dominate. As per our knowledge today, Groups I.1a and I.3 Crenarchaeota are less common (Bomberg and Timonen 2009). Thaumarchaeota is a newer phylum which only consists of the chemolithoautotrophic ammonia-oxidizers (Bomberg and Timonen 2009). Most ammonia-oxidizing species that have the potential for nitrification were in the Crenarchaeota phylum and have recently been classified into the Thaumarchaeota phylum. Three of these species have been isolated from soil environments; MY1 strain of Nitrosopumilus maritimes (Jung et al. 2011), Nitrososphaera viennensis (Tourna et al. 2010), and Nitrosotalea devanaterr (Lehtovirta-Morley et al. 2011). The potential for nitrification is present in both terrestrial and aquatic habitats and is carried out by a wide distribution of species within the Group I clade now classified as Thaumarchaeota (Francis et al. 2005). The nitrification pathway in Archaea is not well understood and limited information exists on the enzymes and intermediate steps leading to the production of N2O. Archaea are known to produce N2O in pure and enrichment cultures in both marine and soil environments (Jung et al. 2011, Hink et al. 2017). There is evidence that N2O is derived from intermediates of the pathway including NH2OH, NO and/or NO2- (Vajrala et al. 2014, Martens-Habbena et al. 2015). NO is known be a key intermediate in the Archaeal pathway and is directly linked to the production of N2O (Martens-Habbena et al. 2015). 11 Recent environmental metagenomic analyses revealed that ammonia monooxygenase A subunit (amoA) genes are also present in Archaea (AOA) and have been shown to be widespread in many environments such as soils and marine waters (Mao et al. 2011). Many reports have suggested that AOA are numerically dominant over AOB, even 3000 times more abundant, except under long-term fertilization (Mao et al. 2011; Morimoto et al. 2011; Shen et al. 2013). The amoA sequence is more variable in the Archaea than in the Bacteria but the function of this enzyme is similar among all species (Tourna et al. 2008). Archaeal amoA genes appear to be ubiquitously distributed in soils, and quantification of Bacterial and Archaeal amoA genes in terrestrial environments further supports this statement (Tourna et al. 2008). It is often speculated that Archaea may be more significant contributors to nitrification than the better-described Bacterial NH3 oxidizers but it has yet to be tested (Leininger et al. 2006). There are many factors in soil systems that may alter the ratio between nitrifying Archaeal and Bacterial populations, such as oxygen concentration, water availability, nutrient composition and pH of the soil. 1.4 Factors Affecting the Abundance of Nitrifying Populations and N2O Emissions N2O emissions in agricultural soils are highly influenced by many environmental and management factors including substrate concentration, pH, depth, oxygen availability, soil moisture and temperature, fertilizer and irrigation use (Swarts et al. 2016). However, very little is known about the abundance of the nitrifying communities under different management practices. 12 1.4.1 Substrate concentration A major factor affecting the relative abundance of AOA and AOB is substrate concentration. In contrast to AOB populations, AOA have been shown to display high affinities and low thresholds to NH3 (Hatzenpichler 2012). Hence, Archaea are able to nitrify even at extremely low concentrations of NH3 (Hatzenpichler 2012). The differences in the affinity for the substrate will lead to differential growth of AOA and AOB. As low concentrations of NH3 are found in open waters, a higher abundance of nitrifying Archaea has been observed and is correlated to nitrification rates in these environments (Di et al. 2010a, Verhamme et al. 2011). This trend was also observed in two isolates of soil AOA where no growth occurred at 20 nM NH3 in pure cultures of both isolates but growth occurred at lower concentrations of NH3 (Tourna et al. 2011). On the other hand, when high concentrations of NH3, in the form of ammonium sulfate, were applied to soil AOB abundance increased and was linked to higher nitrification rates (Verhamme et al. 2011). Another study suggested that AOB abundance increased at high NH3 concentrations and AOA populations were not affected by substrate concentration (Di et al. 2010a). These results suggest that AOB may dominate at high NH3 concentrations, AOA at low NH3 substrate concentrations, and that AOA and AOB may persist in different niches (Di et al. 2010a). Long-term fertilization altered the abundance and diversity of AOB rather than those of AOA (Shen et al. 2013). This may be a consequence of high NH3 concentration which leads to high growth rates and diversity of AOB in agricultural soils (Shen et al. 2013). After fertilizer amendment, an increase in AOB abundance was directly correlated with nitrification activity, whereas AOA abundance seemed to be unchanged (Di et al. 2009, 2010a). In a microcosm study, high NH3 concentrations inhibited NO2- conversion into NO3-, leading to the accumulation of NO2- and therefore an increase in N2O emissions (He et al. 13 2016). Thus far, the relationship between N2O production and nitrification rates of AOA in soil environments is not clearly understood. As the level of fertilizer being applied to the soil increases, it will alter the potential contribution of AOB and AOA to nitrification (Mao et al. 2011). Nitrification rates may further change the ratio of N2O production from each community. 1.4.2 pH Soil pH is important in determining the availability, chemical form and the concentration of chemical species, such as NH3. (He et al. 2016). The preference of AOA for low NH3 concentrations is in agreement with other studies that reported high numbers of AOA in acidic soils (Di et al. 2009, Verhamme et al. 2011, Zhang et al. 2012, Hatzenpichler 2012). Acidic pH conditions decrease the availability of NH3 and increase the availability of NH4+. Two studies testing acidic Chinese soils have established higher AOA abundance than AOB abundance in a microcosm (Zhang et al. 2012) as well as in a field study (Shen et al. 2013). These studies further concluded that there was a significant relationship between abundance of AOA and nitrification rates. Zhang et al. (2012) also studied the potential for assimilation of inorganic carbon by AOA species. Not surprisingly, AOA were found to assimilate inorganic carbon and accumulate NO3-, providing evidence of autotrophic nitrification. As compared to AOB, AOA were more transcriptionally active in acidic soils although both microbial groups harboured phylotypes that are specifically adapted to low pH conditions (Hatzenpichler 2012). This difference may lead to niche separation where N2O emissions in acidic soils may 14 be emitted predominantly by nitrifying Archaea and in neutral pH soils by nitrifying Bacteria. Biochar can be used to improve acidic soils. Biochar is formed from the thermochemical decomposition of organic material (He et al. 2016). Biochar application is a way to increase pH of acidic soils and decrease nutrient loss. Biochar-amended soils showed higher AOB abundance and lower AOA abundance compared to the control and this might be related to the increase in pH (He et al. 2016). 1.4.3 Soil depth The potential for N2O emissions by certain microorganisms at a particular soil depth depends on the nutrient availability. Since fertilizer and compost may be applied to the soil surface, they will contribute to increased concentrations of NH3 closer to the application site with slow transport of this substrate into the lower soil layers (Hatzenpichler 2012). The transport of fertilizer is dependent on the location of the irrigation source and frequency of water distribution (Hatzenpichler 2012). As the water content increases in the soil it will increase the water filled pore space (WFPS), forming anaerobic pockets in the soil and therefore promoting denitrification over nitrification (Hatzenpichler 2012). Di et al. (2010a) observed a significant decline in abundance of Bacterial amoA genes with depth in nonfertilized sites while Archaeal amoA abundance remained high, resulting in a maximal AOA to AOB ratio of 3,000:1. Subsequently, nitrification rates were much higher in surface soils than in the subsoils (Di et al. 2010a). Only rarely have studies quantified the abundance of AOA while measuring nitrification, which makes it harder to determine the relative importance of AOA in soil nitrification (Hatzenpichler 2012). In a study by Podolyan 15 et al. (2014), after 30 days of urea application, Bacterial amoA genes were most abundant at a depth of 2.5 cm and decreased with depth to 10 cm, but the opposite was true for Archaeal amoA gene abundance. 1.4.4 Temperature, oxygen availability and water-filled pore space Temperature and water availability may alter community composition or nitrification potential. Warming of the environment may either increase or decrease Bacterial or Archaeal abundance with no consensus provided in the literature (Jung et al. 2011). Tourna et al. (2011) observed changes in two AOA soil isolates due to alterations in temperature: N. viennesis produced NO3- at temperatures higher than 28 ᵒC, whereas the other isolate produced NO3- at temperatures higher than 37 ᵒC (Tourna et al. 2011). These changes in the nitrifying community could be due to selective growth of certain species (Tourna et al. 2008). Water content and oxygen levels in the soil have an opposing relationship. In well-aerated soils, nitrification is the main contributor to N2O, but in anaerobic pockets, denitrification is the main contributor to N2O emissions (Khalil et al. 2004). Water content of the soil can be expressed as the WFPS, which reports the fraction of total pore space occupied by water. If the water content of the soil is higher than 70%, denitrification is the main contributing factor to N2O production but if the water content is 35 % - 60%, nitrification is the main contributor to N2O flux (Bateman and Baggs 2005). Nitrifying Archaea and Bacteria also respond differently to water availability. In marine environments, Archaeal nitrifiers had much higher affinity for ammonia than Bacterial nitrifiers (Martens-Habbena et al. 2009). In Gleeson et al.’s (2010) study, water availability in soil environments 16 affected the Bacterial nitrifier abundance more than the Archaeal nitrifier abundance. In semiarid soil microcosms, increasing soil moisture increased AOA abundance but did not significantly change AOB abundance (Adair and Schwartz 2008, Bustamante et al. 2012).Water is not directly related to oxygen in soil but rather alters the diffusion coefficient of the soil. 1.5 Effect of Nitrification Inhibitors Selective inhibitors can be used to determine the potential contribution of nitrifiers and denitrifiers as well as Archaeal and Bacterial populations to N2O production. Some inhibitors are available commercially for agricultural use to reduce nitrification rates and to increase N fertilizer use efficiency, hence reducing N2O production and nitrate leaching from agricultural systems (Ruser and Schulz 2015). Inhibitors that affect the AMO enzyme directly inhibit nitrification rates and decrease NO3- concentrations (Ruser and Schulz 2015). Some nitrification inhibitors (NI) used in cattle slurry have been directly linked to reduction in N2O emissions but this has not been shown in agricultural soils (Ruser and Schulz 2015). The use of NI does show great potential for increasing the effectiveness of fertilizer application and reducing greenhouse gas emissions. NI such as acetylene (C2H2), dicyandiamide (DCD), carboxyphenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (carboxyl-PTIO, or PTIO), sulfathiazole (ST), l-octyne and allythiourea (ATU) are most frequently used in a laboratory setting. Acetylene inhibits the first step of nitrification and the formation of N2 gas from N2O in denitrification (Butterbach-Bahl et al. 2013). Acetylene significantly inhibits the nitrification pathway in 17 both Archaea (specifically N. viennesis) and Bacteria (Offre et al. 2009, Taylor et al. 2010, Tourna et al. 2011). DCD is a commercially available compound which binds to the active site of the AMO enzyme and inhibits its activity. It has been tested on acidic soils where the nitrifying Archaeal populations were high (Shen et al. 2013). In these soils DCD significantly reduced the AOA population but not the AOB population. In contrast, Di et al. (2009) demonstrated a decrease in AOB population in highly fertilized soils after the application of DCD. This inhibitor is a copper-chelator and therefore decreases the availability of copper which is a co-factor required by AMO to catalyze its reactions (Ruser and Schulz 2015). PTIO was assessed for effectiveness against a nitrifying Archaeal species and a Bacterial species and it showed inhibition of the AOA species at low concentrations while the Bacterial species was unaffected (Shen et al. 2013). Martens-Habbena et al. (2015) also showed a direct link between PTIO inhibition and AOA, while AOB were insensitive to this inhibitor. As PTIO is a NO scavenger, this suggests that NO may be a direct intermediate or an activator of nitrification in the Archaeal pathway and a potential nitrifying inhibitor for Archaeal populations (Martens-Habbena et al. 2015). ST is an antibiotic capable of interfering with folic acid biosynthesis. This agent has been tested in environmental settings and demonstrated little effect on AOA while significantly inhibiting Bacterial populations (Schauss et al. 2009). L-octyne is a selective Bacterial inhibitor and has been tested on three AOA isolates which displayed minimal inhibition in pure cultures (Taylor et al. 2013). Lastly, ATU is a copper-chelating inhibitor which reduces the capability of AMO’s active site to bind substrate. Furthermore, ATU has been shown to be a very strong inhibitor 18 of AOB but at the same concentration AOA species were not inhibited (Shen et al. 2013). Jung et al. (2014) used ATU to inhibit the growth of AOB to obtain pure cultures of AOA isolates from soil. These data suggest there may be differences in the active site of the enzyme in Archaeal and Bacterial species (Shen et al. 2013). Hink et al. (2017) were the first to distinguish between N2O production from AOA and AOB with the use of l-octyne as a selective Bacterial inhibitor and acetylene as a nitrification inhibitor (inhibiting both populations). The results of this study showed the domination of AOB in NH3-amended microcosms and their high contribution to N2O production in the same microcosms (Hink et al. 2017). AOA’s overall contribution to N2O production was low but higher when NH3 availability was limited compared to when NH3 was abundant, while AOB produced N2O contributing to higher emissions (Hink et al. 2017). Additionally, Hink et al. (2017) suggested a mitigation strategy to push the equilibrium toward the nitrifying Archaeal population rather than the nitrifying Bacterial population. One way to achieve this would be the application of organic-N rather than inorganic N fertilizer. Zhou et al. (2015) found evidence suggesting a higher abundance of AOA and lower N2O emissions in plots amended with organic N compared to inorganic N. 1.6 Methods to Determine Abundance of Archaea in Soil Environments Nitrifier abundance is traditionally determined by the most probable number (MPN) method. This method involves a serial dilution of the original sample with many replicates. The replicates are tested by colorimetric analysis to detect the presence of nitrite or nitrate, the end products of nitrification (Nicol and Prosser 2011). The amount the original sample is diluted depends on the abundance of the nitrifiers in the original soil sample (Nicol and 19 Prosser 2011). A higher dilution factor will be required if the original soil sample is highly concentrated, whereas a lower dilution factor will be required for less concentrated soil sample. The abundance of nitrifying species also can be estimated by measuring the nitrification potential, which is determined by analyzing the production of nitrate from soil samples (Nicol and Prosser 2011). The nitrification potential technique assumes that information on the ‘optimal’ growth conditions is available (Nicol and Prosser 2011). These methods are limited as they are culture-based techniques and it is difficult to distinguish between Bacterial and Archaeal ammonia oxidizers (Nicol and Prosser 2011). By understanding the ‘optimal’ growth conditions of both the Bacteria and Archaea and the nitrogen is labeled at the start of the experiment, this method can be used to distinguish the nitrification potential from either domain. A better estimate of ammonia oxidizer abundance is provided by quantification of specific 16SrRNA and amoA genes (Nicol and Prosser 2011). Specific primers for total and nitrifying Archaea and Bacteria can be used to distinguish and quantify the different domains and groups (Nicol and Prosser 2011). Quantitative polymerase chain reaction (qPCR) is the most common approach to determine the contribution and the abundance of Archaea in these agricultural systems (Nicol and Prosser 2011). Two types of qPCR can be used with different outcomes: relative qPCR or absolute qPCR. Relative qPCR is used to compare one sample to another sample and does not require a standard with known gene abundance (Nicol and Prosser 2011). Absolute quantification requires a standard and provides the number of gene copies in the sample (Nicol and Prosser 2011). I will focus on absolute qPCR for the remainder of this section. 20 Quantification of gene abundance enables the direct determination of population size in the soil sample (Nicol and Prosser 2011). Quantification of a gene is enabled by adding a standard to each qPCR assay (Nicol and Prosser 2011). The standard is a serial dilution of one sample for which the number of copies of the gene is known (Nicol and Prosser 2011). Selection of specific primers is critical for qPCR as they should be an exact match to the standard (Nicol and Prosser 2011). The best approach to generate a standard is to clone PCR product from the primers of choice into a plasmid and transform this plasmid into a suitable host in order to obtain multiple copies of the plasmid (Nicol and Prosser 2011). These plasmids can now be extracted and quantified for use as a standard in qPCR (Nicol and Prosser 2011). The standard curve will not only provide the gene abundance in the sample but also information about the efficiency of the assay and specificity of the primers. High PCR efficiency is required for precise and robust assays. In a qPCR assay, 100% efficiency means the amount of product is doubling in each cycle (Bustin et al. 2009). If a standard curve is established, the efficiency is calculated from the slope of the standard curve (Bustin et al. 2009). The melt curve of each qPCR can provide evidence for the specificity of each set of primers (Bustin et al. 2009). Depending on the size of the product, different products will melt (become single stranded) at different temperatures (Bustin et al. 2009). The melt curve will provide evidence that one product has been formed from one set of primers (Bustin et al. 2009). Due to these different reasons absolute qPCR is an effective method for direct quantification of gene abundance in soil samples. Quantitative PCR is the most convenient and reliable tool to analyze gene abundance in soil environments but still has some limitations. The major concern is the presence of impurities in the DNA samples including humic acids or urea that may hinder the PCR 21 amplification (Kim et al. 2013). These impurities can be left behind by the DNA extraction protocols. These inhibitors can be mitigated by altering the DNA polymerase or chemically washing the samples before use (Kim et al. 2013). Secondly, no PCR technique, including qPCR, cannot distinguish between viable or dead cells since DNA persists after the death of the organism (Kim et al. 2013). Therefore, an overestimation of the abundance of a community may result (Kim et al. 2013). Thirdly, if the concentration of the target sequence is low, qPCR may not be sensitive enough to quantify that gene (Bustin et al. 2009). Other more sensitive techniques can be used to obtain accurate quantification results, including droplet digital PCR (ddPCR) (Bustin et al. 2009). Regardless of these limitations qPCR is widely used to quantify the gene abundances in soil environments. 1.7 Project Significance Apple orchards cover about 15,000 ha of land in Canada and 3,921 ha in British Columbia (Statistics Canada 2015, Fentabil et al. 2016a) Vineyards cover about 7.1 million ha worldwide and roughly 3,906 ha in British Columbia (Fentabil et al. 2016a). Both these agricultural systems contribute to most of the agricultural land in the Okanagan region; therefore, these agricultural plots are ideal for the development of mitigation strategies (Canada 2015). Most research to date has considered Bacteria as the sole producers of N2O in agricultural soils and they are usually the domain targeted to reduce emissions of N2O into the atmosphere in agricultural cropping systems. The production of N2O has been confirmed in pure cultures of five nitrifying Archaeal species but their significance in soil environments is not well understood (Jung et al. 2014). The research reported in this thesis examines the 22 potential of Archaeal species in an agricultural woody perennial soil environment to contribute to the production of N2O. The effects of different agricultural practices and soil depth on the abundance of AOA in these cropping systems were studied. However, quantifying the abundance of genes does not confirm that they have been transcribed into RNA and further translated into a functional enzyme. Laboratory microcosms were used to study the relationship between N2O emissions and gene abundance under varying substrate concentrations and in the presence of nitrification inhibitors. In soil environments, the importance and potential of Archaeal species in the production of this long-lived greenhouse gas, N2O, has not been confirmed (Konneke et al. 2005, Francis et al. 2005, Leininger et al. 2006, Jung et al. 2014). The use of selective inhibitors allowed me to determine the relative contribution of Archaea and Bacteria to N2O emissions under laboratory conditions. This research furthers our understanding of the organisms and factors contributing to N2O production in woody perennial systems and potential means to reduce its production. This thesis examines the effect of different management practices on the abundance of nitrifying Bacterial and Archaeal communities in woody perennial cropping systems and the use of differential inhibitors to distinguish N2O emissions from each domain under controlled conditions. In the first study the abundance of Archaeal and Bacterial nitrifying communities was determined in soil samples collected at five times from an apple orchard and a grape vineyard at the Summerland Research and Development Centre (SuRDC) of Agriculture and Agri-Food Canada located in the Okanagan Valley, British Columbia, Canada. The orchard and vineyard were subjected to differing mulch and nitrogen applications and samples were collected at three depths. In the second study, in vitro soil microcosms were used to assess 23 N2O production by nitrifying Archaea and Bacteria under differing nitrogen concentrations, and in the presence of selective inhibitors of each microbial community using soils from different depths from the field plots. 1.8 Objectives and Hypotheses This thesis is divided into 2 separate studies: the field study and the in-vitro microcosm study. The main objectives for these studies are: 1. Field study 1.1. Determine the effect of mulch on the abundance of total and nitrifying Archaea and Bacteria in apple and grape plots. 1.2. Determine the effect of two nitrogen application rates on the abundance of total and nitrifying Archaea and Bacteria in the apple plot. 1.3. Determine the effect of nitrogen application either as compost or as urea via fertigation on the abundance of total and nitrifying Archaea and Bacteria in the grape plot. 1.4. Determine the effect of soil depth on the abundance of total and nitrifying Archaea and Bacteria in both plots. 2. In vitro microcosm study 2.1. Determine the effect of urea concentration on N2O production and the abundance of nitrifying Archaea and Bacteria. 2.2. Determine the effect of soil depth on N2O production and the abundance of nitrifying Archaea and Bacteria. 24 2.3. Determine the effect of an Archaeal and/or a Bacterial nitrifying inhibitor on N2O production and nitrifying Archaeal and Bacterial abundance. The Hypotheses are: 1. Field study 1.1 AOA and AOB abundance will decrease in the presence of mulch at the surface of the soil. Total Archaeal and Bacterial populations will increase in the presence of mulch. Rationale: It was expected that the presence of mulch would decrease the oxygen concentration at the soil surface therefore decreasing nitrifying Archaeal and Bacterial abundance. Mulch also provides soil with a source of carbon and other nutrients leading to increased abundance of total Archaeal and Bacterial populations. 1.2 Nitrifying Bacteria and Archaea will not be affected by the concentrations of applied nitrogen in the apple orchard. Rationale: Ca(NO3)2 was the nitrogen source for the apple orchard soil and both AOA and AOB use ammonia for nitrification. The concentration of nitrate would not affect the abundance and nitrification rates of nitrifying populations. 1.3 Abundance of nitrifying Bacteria will be higher with urea application via fertigation than with compost application in the grape plot, but the opposite will be true for nitrifying Archaea. Rationale: Urea application provides nitrifiers with a readily available source of ammonia in a soluble form; therefore, this may result in an increase in the abundance 25 of nitrifying Bacteria. A high concentration of this readily available source does not affect Archaeal abundance (Hatzenpichler 2012). Compost is applied to the soil surface and slow diffusion of this substrate over a longer period of time will limit the availability of ammonia to nitrifying Archaea and Bacteria (Hatzenpichler 2012). Therefore, compost treatments will have lower concentrations of available N compared to urea-fertigated treatments, and abundance of nitrifying Archaea will increase in compost plots compared to abundance of nitrifying Bacteria. 1.4 AOA abundance will increase as depth increases but AOB abundance will decrease as depth increases. The ratio between AOA and AOB will increase with depth. Total populations will decrease as soil depth increases. Rationale: Di and Cameron (2016) observed increase in the AOA: AOB ratio as soil depth increased in grasslands. In contrast to AOB populations, AOA have been shown to display high affinities and low thresholds to NH3 (Hatzenpichler 2012). The differences in the affinity for the substrate will lead to differential growth of AOA and AOB. Total populations will decrease since the nutrients available for growth decrease with depth. 2. In vitro microcosm study 2.1 N2O production will increase as urea concentration increases. Abundance of nitrifying Archaea will be higher than that of nitrifying Bacteria with no addition of nitrogen and abundance of nitrifying Bacteria will increase with increasing concentrations of nitrogen. 26 Rationale: Application of higher concentrations of the substrate urea will trigger an increase in nitrification and therefore, an increase in N2O production. Data from aquatic and soil environments suggest that AOB may dominate at high NH3 concentrations and AOA at low NH3 substrate concentrations (Di et al. 2010a). 2.2 AOA abundance will be higher than AOB abundance in soils collected at lower depths but AOB abundance will be higher in soils collected at the surface. Rationale: Di and Cameron (2016) observed increase in the AOA: AOB ratio as soil depth increased in grasslands. The increase in nitrifying Bacterial abundance in the surface soils leads to higher nitrous oxide emissions (Di and Cameron 2016). In contrast to AOB populations, AOA have been shown to display high affinities and low thresholds to NH3 (Hatzenpichler 2012) and they may be the main contributor of nitrous oxide at lower depths. The differences in the affinity for the substrate and the replication rates of both species will lead to differential growth of AOA and AOB. 2.3 The surface soil will produce more N2O than soil collected at lower depths. The majority of the nitrous oxide produced in the surface soils will be from nitrifying Bacteria rather than nitrifying Archaea. The majority of the nitrous oxide produced in the deeper soils will be from nitrifying Archaea rather than nitrifying Bacteria. Rationale: The different species that exist at different soil depths and their potential to produce nitrous oxide might be the cause of the difference in nitrous oxide production with soil depth (Xiang et al. 2017). Species of Bacteria and Archaea in surface soils might be more adapted to high N concentration and their nitrification rate might be higher than that of species at deeper soils. Nitrifying Bacteria prefer a 27 high N environment and Nitrifying Archaea prefer a low N environment (Mushinski et al. 2017). Since, all fertilizers were applied on the surface, we assume that surface soils have a higher concentration of most nutrients. 2.4 N2O production will be highest in the absence of inhibitor compared to that with the addition of one or both inhibitors. Rationale: Without inhibitor addition N2O production will occur from both Bacteria and Archaea therefore resulting in the highest emissions of N2O compared to when either of the inhibitors is applied. 2.5 With applications of high concentrations of urea, N2O emissions from the nitrifying Bacteria will be higher than those from the nitrifying Archaea, in the presence of Archaeal and Bacterial inhibitors, respectively. With applications of low concentrations of urea, N2O emissions from the nitrifying Archaea will be higher than those from the nitrifying Bacteria, in the presence of Bacterial and Archaeal inhibitors, respectively. Rationale: At high concentrations of urea, if the Archaeal population is inhibited, then the Bacterial population will produce more N2O compared to low concentrations of urea. Conversely, with high urea concentrations, if the Bacterial population is inhibited then the Archaeal population will produce less N2O compared to applications of low concentrations of urea. AOA have been shown to display high affinities and low thresholds to NH3 (Hatzenpichler 2012). 28 2.6 The abundance of nitrifying populations will be unchanged with the addition of either inhibitor. Rationale: The inhibitors target the activity of the enzyme that produced N2O; therefore, the abundance of these communities will not be affected (Martens-Habbena et al. 2009). 29 2 Methodology This section of my thesis is divided into the field study and then followed by the in vitro microcosm experiment. 2.1 Field Study This study investigated the effect of: 1) different nitrogen application rates; and 2) the presence or absence of mulch on abundance of total and nitrifying Bacteria and Archaea. Soils under two different management regimes in an apple orchard and a grape vineyard were sampled five times in total over three consecutive years. 2.1.1 Site description Soil samples were collected at two sites from the Summerland Research and Development Centre (SRDC) of Agriculture and Agri-Food Canada (Latitude. 49° 33'59''N and Longitude 119° 38'12''W) (Fentabil et al. 2016b). These sites are in the heart of the Okanagan valley and display an average maximum daily temperature of 28.4°C (in July) and an average minimum daily temperature of -1.5°C (Fentabil et al. 2016b) with an overall daily average temperature of 9.6 (± 2.4) °C. Additionally, this area has a low precipitation rate of 346 (±25) mm per year (average over 30 years, 1981-2010) (Fentabil et al. 2016b). The soils in the apple orchard display a low water-holding capacity (Fentabil et al. 2016b). These soils have a cation exchange capacity (CEC) of 7.9 meq / 100 g, a C:N ratio of 7.9 and a pH of 6.6 before treatment application (Fentabil et al. 2016b). The grape vineyard soils are of fluvio-glacial origin with a CEC of 10.6 meq/ 100 g, C:N ratio of 8.5 and a pH of 7.4 before treatment application. Apple (1,569 ac of 3,677 ac total area of fruits, berries and nuts) and 30 Grape (946 ac of 3,677 ac total area of fruits, berries and nuts) contribute to the majority of the agricultural land in the Central Okanagan and agricultural soil is the major contributor of anthropogenic nitrous oxide emissions (Statistics Canada 2015). Further characteristics specific to the apple and grape plots are displayed in Table 1. Table 1: Summary of the variety, rootstock, soil type, established year, fertilizer, mulch and irrigation applications of both apple and grape plots sampled (Hannam et al. 2016b). Parameter Apple Grape Variety/rootstock Ambrosia/M9 Merlot/SO4 Soil texture Loamy sand Sandy loam Established 2003 May 2011 Fertilizer 20 g or 40 g Ca(NO3)2/ tree 15 g N / year as available N from Compost or 15 g N as urea/vine by fertigation * Soil Cover Presence or absence of pine bark mulch Presence or absence of pine bark mulch Irrigation Two times a day via drip Four times a day via drip *In 2015, the urea and compost application in the grape plot was reduced to 7.5 g N/vine. The apple plot consisted of 8 rows which were 53.1 m long. The apple trees were planted every 0.9 m within each row and the spacing between the rows was 3.5 m. The outer rows were guard rows and did not have any treatment applied. Each row in the apple plot was divided into 12 sub-plots and each sub-plot consisted of 2 outer guard trees and 3 experimental trees (Figure 4) (Hannam et al. 2016b). All treatments were applied in 2012 randomly in a split-plot design (Appendix A; Hannam et al. 2016). This experiment was set up as a randomized complete block design with combinations replicated in six blocks, but only three of the six blocks were sampled for this study (Fentabil et al. 2016a). 31 The water output delivered via a drip irrigation system was equal to the amount of water lost by evapotranspiration (Fentabil et al. 2016a). These emitters were located 0.3 m on either side of the apple tree and the trees were irrigated morning and afternoon every day (Fentabil et al. 2016a). Some trees were irrigated morning and afternoon every other day; these treatments were not a part of this study. The apple orchard was irrigated from May to October each year and fertigated with calcium nitrate (20 g or 40 g Ca(NO3)2/tree) from mid-May to the end of June (6 weeks); these rates were defined as low (L) and high (H), respectively. During the irrigation period both the L and H plots received the same quantity of water. Six treatments were studied: two application rates of fertilizer (20 g N/tree or 40 g N/tree as calcium nitrate [Ca(NO3)2]) were applied, with or without bark mulch (shredded bark and wood) (Hannam et al. 2016). Some treatments also involved the use of black plastic which were not a part of this study. The bark mulch was reapplied in May of every second year (2014, 2016). It was spread on top of the previous mulch layer, to a depth of roughly 10 cm in a 2 m wide strip (Fentabil et al. 2016a; Hannam et al. 2016b). The mulch was generated from a local sawmill and consisted of shredded bark and wood-chips (mainly Pinus contorta var latifolia and Picea glauca) (Fentabil et al. 2016a). The grape plot consisted of Merlot vines (Vitis vinifera) on SO4 rootstock. In May 2011, these vines were planted in 14 rows with 2 outer rows as ‘guard’ rows (Fentabil et al. 2016b, Hannam et al. 2016a). Each row in the grape plot was divided into 6 sub-plots and 1A 1B 1C 1D 1E 1F 1G 1H 1I 1J 2A 2B 2C 2D 2E 2F 2G 2H 2I 2J 3A 3B 3C 3D 3E 3F 3G 3H 3I 3J 4A 4B 4C 4D 4E 4F 4G 4H 4I 4J1T 1S 1R 1Q 1P 1O 1N 1M 1L 1K 2T 2S 2R 2Q 2P 2O 2N 2M 2L 2K 3T 3S 3R 3Q 3P 3O 3N 3M 3L 3K 4T 4S 4R 4Q 4P 4O 4N 4M 4L 4K24A 24B 24C 24D 24E 24F 24G 24H 24I 24J 23A 23B 23C 23D 23E 23F 23G 23H 23I 23J 22A 22B 22C 22D 22E 22F 22G 22H 22I 22J 21A 21B 21C 21D 21E 21F 21G 21H 21I 21J24T 24S 24R 24Q 24P 24O 24N 24M 24L 24K 23T 23S 23R 23Q 23P 23O 23N 23M 23L 23K 22T 22S 22R 22Q 22P 22O 22N 22M 22L 22K 21T 21S 21R 21Q 21P 21O 21N 21M 21L 21K25A 25B 25C 25D 25E 25F 25G 25H 25I 25J 26A 26B 26C 26D 26E 26F 26G 26H 26I 26J 27A 27B 27C 27D 27E 27F 27G 27H 27I 27J 28A 28B 28C 28D 28E 28F 28G 28H 28I 28J25T 25S 25R 25Q 25P 25O 25N 25M 25L 25K 26T 26S 26R 26Q 26P 26O 26N 26M 26L 26K 27T 27S 27R 27Q 27P 27O 27N 27M 27L 27K 28T 28S 28R 28Q 28P 28O 28N 28M 28L 28K48A 48B 48C 48D 48E 48F 48G 48H 48I 48J 47A 47B 47C 47D 47E 47F 47G 47H 47I 47J 46A 46B 46C 46D 46E 46F 46G 46H 46I 46J 45A 45B 45C 45D 45E 45F 45G 45H 45I 45J48T 48S 48R 48Q 48P 48O 48N 48M 48L 48K 47T 47S 47R 47Q 47P 47O 47N 47M 47L 47K 46T 46S 46R 46Q 46P 46O 46N 46M 46L 46K 45T 45S 45R 45Q 45P 45O 45N 45M 45L 45K49A 49B 49C 49D 49E 49F 49G 49H 49I 49J 50A 50B 50C 50D 50E 50F 50G 50H 50I 50J 51A 51B 51C 51D 51E 51F 51G 51H 51I 51J 52A 52B 52C 52D 52E 52F 52G 52H 52I 52J49T 49S 49R 49Q 49P 49O 49N 49M 49L 49K 50T 50S 50R 50Q 50P 50O 50N 50M 50L 50K 51T 51S 51R 51Q 51P 51O 51N 51M 51L 51K 52T 52S 52R 52Q 52P 52O 52N 52M 52L 52K72A 72B 72C 72D 72E 72F 72G 72H 72I 72J 71A 71B 71C 71D 71E 71F 71G 71H 71I 71J 70A 70B 70C 70D 70E 70F 70G 70H 70I 70J 69A 69B 69C 69D 69E 69F 69G 69H 69I 69J72T 72S 72R 72Q 72P 72O 72N 72M 72L 72K 71T 71S 71R 71Q 71P 71O 71N 71M 71L 71K 70T 70S 70R 70Q 70P 70O 70N 70M 70L 70K 69T 69S 69R 69Q 69P 69O 69N 69M 69L 69KIrrig + N Irrig N mulch 100% 2x day 20g N/tree none100% every 2 days 20g N/tree black plastic100% 2x day 40gN/tree bark mulch100% every 2 days 40gN/treeRep 5Rep 6Guard rowWEST SIDEEAST SIDEGuard rowRep 1Rep 2Rep 3Rep 4G 1 3 G2GGGGGGGGGG GG G G12111098765469 68 67 66 65 64 63 62 6172 71 70GGGGG G G G76 77 78 79 80 81 82 83 8473 74 75G 144 142 G143 GGGGG G133134135136137138139140141G 145 147 G146 GGGGG G156155154153152151150149148GGGGG G G G213 212 211 210 209 208 207 206 205216 215 214G G G GG GGGGG GGGGGGGGGGG G G GG GGGGG GGGGGGGGGGFigure 4: Treatment layout of the apple plot at Summerland Research and Development Centre (Summerland, BC, Canada). Each treatment is represented by 5 adjacent apple trees and the outside 2 trees are guard trees (G) and the inside 3 trees are the treatments trees (numbered). Either side of the plot consists of a row of guard trees. 32 each sub-plot consisted of 2 outer guard vines and 3 experimental vines (Hannam et al. 2016). The spacing between vines was 1.2 m and 3.0 m between rows with an overall vine density of 2,778 vines/ha. All treatments were applied randomly in a factorial treatment design. Two different low-pressure irrigation systems were installed: Micro-sprinkler and Drip. The Micro-sprinkler system supplied 20 L/ h mid-way between the two adjacent vines. The micro-sprinkler treatments were not studied. A drip irrigation system was used to supply 4 L ha-1/vine 0.3 m on either side of the Merlot vine from May through to October in all sampling years. Urea was fertigated from mid-May to mid-June for about 4 weeks in 2013, but from late-May to late-June in 2014 and 2015. In the grape experiment, 15 g N/vine (2013 and 2014) or 7.5 g N/vine (2015) were applied either as compost or urea via fertigation (Hannam et al. 2016). The compost was applied yearly in May to the plots that were not fertigated with urea. The compost was manually incorporated, after application, into the top 5 cm of the soil. The compost composition was 40% cow manure, 25% shredded bark, 20% straw and 15% grape pomace (Fentabil et al. 2016; Hannam et al. 2016). The bark mulch was reapplied to a depth of 10 cm every second year (2013, 2015) (Fentabil et al. 2016; Hannam et al. 2016). 2.1.2 Sample collection Soil samples were collected during irrigation (August 6th, 2013 and August 12th, 2014) and before harvest (September 23rd, 2013 and September 22nd, 2014) and at the end of fertigation (June 23rd, 2015). Soil was collected from 12 apple trees and 12 grape vines (4 treatments, 3 replicate vines) using a 30-cm long soil probe. In the apple plot, the samples were collected 20 cm away from the tree and 20 cm away from the irrigation source. In the 33 grape plot, the samples were collected 30 cm from the vine and 30 cm from the dripper. In mulched plots, the mulch was removed from the surface of the soil before a 30-cm sample was collected. The soil core was divided into 3 parts according to depth (0-10 cm, 10-20 cm and 20-30 cm) and stored in 3 separate freezer bags. Between each sample collection, the probe and the tools were cleaned with 70% ethanol. The soil samples were stored on ice until frozen at -20°C. 2.1.3 Gravimetric soil moisture determination On the same day as sample collection, 1 g of the soil was used to determine the moisture content of each sample. Wet soil was added to aluminum tins and the weight was recorded. These tins were placed in a drying oven at 105˚C for 48 h. The dry soil weights were recorded and the gravimetric moisture content for each sample was calculated using equation 1. :;<=>?@A;>B % ?D>EAF;@ = (I<EE J@A ED>K)9(I<EE M;N ED>K)(I<EE M;N ED>K) ∗ PQQ Eq.1 Where gravimetric percent moisture is percent water content of the soil; Mass of wet soil was weighed when soil was wet and was recently collected from the field (g); Mass of dry soil was the mass of soil after drying in the drying oven (g). 2.1.4 Soil analyses The soil samples were transferred into labelled drying boats and dried at room temperature for 48 h. The soil was ground using a mortar and pestle and sieved through a 2- 34 mm grid directly into a labelled plastic bag. The sieve, mortar and pestle were washed with water and dried between each sample. The soil was sent to the B.C. Ministry of Environment in Victoria, B.C. and analyzed for total nitrogen (% N), total carbon (% C), available ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) according to standard practices. To obtain the concentration of % N and % C, the B.C. Ministry of Environment took a small amount of soil and combusted in a pure oxygen atmosphere to decompose the elements into oxide gases (NOx and CO2, respectively). The gases were either determined by infrared absorption or thermal conductivity and the concentrations in the original samples calculated (Carter and Gregorich 2008). Available NH4-N and NO3-N were measured calorimetrically using an Alpkem Flow System IV analyzer (Carter and Gregorich 2008). 2.1.5 DNA extraction and concentration determination Three hundred milligrams of each wet soil sample were incubated at 37°C for 5 min in Powersoil bead tubes (MoBio, Carlsbad, CA) before isolation. This step was added to defrost the frozen soil sample in the Powersoil bead tube. DNA was extracted using the MoBio Powersoil DNA isolation kit following the company protocol. DNA was eluted with 100 µl of DNase-free water and kept on ice to prevent degradation. The concentration and purity of the isolated DNA were determined using a nanodrop spectrophotometer (Thermo Scientific, Wilmington, DE). The samples were stored in a -80˚C freezer until qPCR was performed. 35 2.1.6 Quantitative real time PCR (qPCR) 2.1.6.1 Generation of standard curve A plasmid containing genes for Bacterial and Archaeal 16SrRNA (pJ201-16S) transformed into Escherichia coli-BL21was synthesized by DNA 2.0 Inc. (Menlo Park, CA). Strains harboring plasmids coding for Bacterial (pCR2.1-amoA) and Archaeal (pGEM-amoA) amoA genes were obtained from Kari Dunfield, University of Guelph, and Sophie Wertz, Agriculture and Agri food Canada, Fredericton, respectively. The cell stocks were thawed and a loopful was streaked onto Luria Bertani (LB) agar (Bertani 1951) with 50 mg/ml kanamycin as a selection for pJ201-16S and pCR2.1-amoA, and 100 mg/ml ampicillin for pGEM-amoA, respectively. The plates were incubated at 37 ˚C for 16 h. Single colonies were picked and inoculated into liquid LB medium [(10 g tryptone, 5 g yeast extract and 10 g sodium chloride) L-1] with the appropriate antibiotic as described above. Cultures were grown at 37˚C, and shaken at 200 rpm for 16 h and the plasmids were isolated using the Omega plasmid kit 1 (E.Z.N.A, Norcross, GA). The concentration of the plasmids was determined by nanodrop spectrophotometry. A digest using 20 U of EcoRI (NEB Biolabs, Ipswich, MA) for pJ201-16S and 10 U of NcoI (NEB Biolabs, Ipwich, MA) was performed to lineralize the plasmids. Four microlitres of the appropriate enzyme buffers and sterile water were added to a total volume of 40 µL. All tubes were incubated at 37˚C for 1 h. To inactivate the EcoRI enzyme, the solution was incubated at 65˚C for 20 min, and the reaction with NcoI was incubated at 80˚C for 20 min. The product was seen under UV on a 1% agarose gel (with SYBR staining) to confirm linearization. The remainder of the linearized DNA was purified using Invitrogen DNA clean and concentrate kit (Zymo, Irvine, CA) using the company protocol. The nanodrop 36 spectrophotometer readings were taken in triplicate and the average was used to calculate the amount of solution containing 106 copies (Appendix A). The volume calculated for 106 copies of the gene was pipetted into labelled cryovials and frozen at -80˚C until needed. In the biological safety hood, the appropriate plasmid was thawed on ice and 10-fold dilutions of the standard curve with nuclease-free water were prepared. Six dilutions of the standard curve with 106, 105, 104, 103, 102 and 10 copies of the gene were established. 2.1.6.2 Primers To amplify a specific gene, each gene requires a specific primer sequence which binds to the DNA and provides the enzyme a 3’ end to start amplification. Primers used are listed in Table 2. Table 2: Primer sequences used to quantify total and nitrifying Archaeal and Bacterial populations with qPCR. Gene Primers Sequences References Bacterial 16S rRNA BACT1369F PROK1492R 5’-CGG TGA ATA CGT TCY CGG- 3’ 5’-GGW TAC CTT GTT ACG ACT T- 3’ (Suzuki et al. 2000) Archaeal 16S rRNA A364aF A934Br 5’-CGG GGY GCA SCA GGC GCG AA- 3’ 5’-GTG CTC CCC CGC CAA TTC CT- 3’ (Burggraf et al. 1997) Bacterial amoA amoA-1F amoA-2R 5’-GGG GTT TCT ACT GGT GGT- 3’ 5’-CCC CTC KGS AAA GCC TTC TTC- 3’ (Mao et al. 2011) Archaeal amoA A23F A616r 5’-ATG GTC TGG CTW AGA CG- 3’ 5’-GCC ATC CAB CKR TAN GTC CA- 3’ (Tourna et al. 2008) 2.1.6.3 Master mixes Two master mixes were prepared; one for the standard curve and the other for the samples. For each standard well (MM1): 10 µL of SsoFast™ EvaGreen® Supermix (Biorad, Hercules, CA), 1.6 µL of 0.1 μM of each of the specific primers and 1.8 µL of water were combined and mixed well on ice. For each sample well (MM2): 10 µL of SsoFast™ EvaGreen® Supermix, 1.6 µL of each of the specific primers, 1.5 µL of T4 gene 32 protein 37 (NEB Biolabs, Ipswich, MA) and 3.3 µL of water were combined and mixed well on ice. The T4 had a final concentration of 75 µg/µL and was used to improve yield of long DNA product (Schwarz et al. 1990). The final concentration of each primer was 400 nM. 2.1.6.4 Preparation of qPCR plates For the 16S Bacteria and Archaea plates the DNA samples were diluted 1:100 with RNase-and DNase-free water. On ice, 15 µL of MM1 were added to the wells of the 96 well plate (Cat #: MLL9651, Biorad, Hercules, CA) for the standard and the negative control in triplicate. Then, 18 µL of MM2 were added to wells for the DNA samples and negative control. Five microliters of each standard dilution and 2 µL of each DNA sample were added and mixed into the well. The plate was sealed using a microseal ‘B’ PCR plate sealing film (Biorad, Hercules, CA) and briefly centrifuged at 2500 rpm. 2.1.6.5 qPCR program The PCR program for each primer set was optimized previously. Table 3 displays the program setting for Biorad CFX96 to determine the copy number of each gene region in all DNA samples. Table 3: PCR program for qPCR amplification and determination of copy number of total and nitrifying Bacteria and Archaea. Gene Program Bacterial 16S rRNA 95°C for 2 min, 40 cycles of 95°C for 30s and 56°C for 30s, 65°C for 5s and 95°C for 50s Archaeal 16S rRNA 95°C for 2 min, 40 cycles of 95°C for 30s and 61.5°C for 30s, 65°C for 5s and 95°C for 50s Bacterial amoA 95°C for 2 min, 40 cycles of 95°C for 30 s and 57°C for 1 min, 65°C for 5s and 95°C for 50s Archaeal amoA 95°C for 2 min, 40 cycles of 95°C for 30s and 56.9°C for 30s, 65°C for 5s and 95°C for 50s 38 2.1.6.6 Calculations to obtain copy number per g dry soil Equation 2 below was used to calculate the amount of dry soil used for each extraction. R STU VWXY = R Z[\ VWXY ∗ (P −^_`) Eq. 2 Where M dry soil is the mass of dry soil (grams), M wet soil is the mass of wet soil (grams) and WCG is the gravimetric water content (unitless). The copy number for each gene per gram of dry soil was determined as follows (Eq 3): BDaN #/: Dd M;N ED>K = ef ?@<g ∗ @KFA>Dg d<BAD; ∗ M>KFA>Dg d<BAD;hK in fklmI M;N ED>K Eq. 3 Where copy #/ g of dry soil is the copy number of each gene in 1 gram of dry soil, starting quantity mean (Sq mean) is the mean copy number of genes detected by qPCR, elution factor is the factor by which the sample of DNA was eluted in, dilution factor is the dilution done to the sample before qPCR was performed 2.2 In Vitro Microcosm Studies 2.2.1 Sample collection Soil was collected in June 2015 or 2016 from a grape vineyard at SuRDC, Summerland, B.C, fertigated with 15 (2015) or 7.5 (2016) g N/vine in the form of urea without mulch application (Table 1). Experiments 1 and 2 were performed with soil collected in 2015, fertigated at 15 g N/vine (discussed in 2.2.3.1 and 2.2.3.2, respectively). Experiment 3 was performed with soil collected in 2016, fertigated at 7.5 g N/vine (discussed in 2.2.3.3). Soil (0-30 cm) was collected from three vines of different sub-plots and mixed to create two 39 composite soil samples (0-15 and 15-30 cm soil depth). Soil cores were collected from three vines using a 30 cm soil probe. Each soil core was divided into two equal parts and stored separately in labelled (0-15 and 15-30 cm depth) freezer bags. Soil samples were collected from treatments where mulch and compost were not applied. Soil from three vines from different sub-plots was thoroughly mixed and stored on ice for transport. 2.2.2 Experimental design The gravimetric soil moisture was determined on the same day of soil collection. The details of this procedure are explained in section 3.1.3. Soil was air dried at room temperature for 3 days. The samples were ground using a mortar and pestle and sieved through a 2 mm grid directly into a labelled plastic bag. The sieve, mortar and pestle were dusted and washed with water between each sample that was collected from different depths. Five grams of dried and sieved soil were added into sterilized 60-ml glass test tubes (VWR, Edmonton, AB) and Suba Seals (Fisher Scientific, Ottawa, ON) were used to create a closed system in the tube. 2.2.3 Inhibitor and urea application In 25-ml Falcon tubes (Corning Incorporated, Tewksbury, MA), a liquid mixture of urea and/or inhibitor was made for each treatment. This amount of mixture applied to the soil depended on the water loss from the soil in the drying process. After the treatments were applied, they were allowed to calibrate for 1 h before being sealed with suba seals. Experiment 1 was performed using concentrations from the literature for both inhibitors. Urea concentration was varied in this experiment. Vineyard soil used for this experiment was collected in June 2015 at two specific soil depths (0-15 cm and 15-30 cm). After performing and analyzing the results of experiment 1, experiment 2 was performed to determine the 40 appropriate concentration of both the Bacterial and Archaeal inhibitors. Two (high and low level) urea concentrations were chosen for this experiment. Soil used for experiment 2 was the same as in experiment 1 but the soil from 0-15 cm and 15-30 cm soil depths was mixed to create one uniform soil sample (0-30 cm). Using the inhibitor concentrations obtained in experiment 2, experiment 3 was conducted to analyze the N2O emissions and abundance of nitrifying populations as soil urea concentrations and soil depth were varied. Table 4: Treatments used in Experiment 1, which was designed to test the effect of nitrification inhibitors, ATU (10 uM) and PTIO (100 uM) and three concentrations of nitrogen in the form of urea (0, 20, 200 mg N/kg of soil) on soil collected at two depths (0-15 and 15-30 cm) from a grape vineyard in June 2015 from Summerland Research and Development Centre in the Okanagan Valley. There were three replicates of each treatment and the target water content was similar to that found in the vineyard. Treatment number Soil depth (cm) Nitrogen concentration (mg N/kg soil) Inhibitor M01 0-15 0 none M02 0-15 0 PTIO M03 0-15 0 ATU M04 0-15 0 both M05 0-15 20 none M06 0-15 20 PTIO M07 0-15 20 ATU M08 0-15 20 both M09 0-15 200 none M10 0-15 200 PTIO M11 0-15 200 ATU M12 0-15 200 both M13 15-30 0 none M14 15-30 0 PTIO M15 15-30 0 ATU M16 15-30 0 both M17 15-30 20 none M18 15-30 20 PTIO M19 15-30 20 ATU M20 15-30 20 both M21 15-30 200 none M22 15-30 200 PTIO M23 15-30 200 ATU M24 15-30 200 both 41 Table 5: Treatments used in experiment 2, which was designed to test the effect of varying ATU (10, 50, 100, 200 uM) and PTIO (100, 200, 300, 400 uM) concentrations and two concentrations of nitrogen in the form of urea (0 and 200 mg N/kg of soil) on soil collected from a grape vineyard (0-30 cm) in June 2015 from Summerland Research and Development Centre in the Okanagan Valley. Pure water was added as a control. There were three replicates of each treatment and the target water content was similar to that found in the vineyard. Treatment Inhibitor Inhibitor concentration (µM) Nitrogen concentration (mg N/kg soil M1 Water 0 0 M2 ATU 10 0 M3 ATU 50 0 M4 ATU 100 0 M5 ATU 200 0 M6 ATU 0 200 M7 ATU 10 200 M8 ATU 50 200 M9 ATU 100 200 M10 ATU 200 200 M11 No Water 0 0 M12 PTIO 100 0 M13 PTIO 200 0 M14 PTIO 300 0 M15 PTIO 400 0 M16 PTIO 0 200 M17 PTIO 100 200 M18 PTIO 200 200 M19 PTIO 300 200 M20 PTIO 400 200 42 Table 6: Treatments used in experiment 3, which was designed to test the effects of nitrification inhibitors ATU (100 uM) and PTIO (200 uM) and four concentrations of nitrogen in the form of urea (0, 20, 100 and 200 mg N/kg of soil) on soil collected at two depths (0-15 and 15-30 cm) from a grape vineyard in June 2015 from Summerland Research and Development Centre in the Okanagan Valley. there were three replicates of each treatment and the target water content was similar to that found in the vineyard. Treatment Soil depth (cm) Urea addition (mg N/ kg of dry soil) Inhibitor M1 0-15 0 none M2 0-15 0 PTIO M3 0-15 0 ATU M4 0-15 0 both M5 0-15 20 none M6 0-15 20 PTIO M7 0-15 20 ATU M8 0-15 20 both M9 0-15 100 none M10 0-15 100 PTIO M11 0-15 100 ATU M12 0-15 100 both M13 0-15 200 none M14 0-15 200 PTIO M15 0-15 200 ATU M16 0-15 200 both M17 15-30 0 none M18 15-30 0 PTIO M19 15-30 0 ATU M20 15-30 0 both M21 15-30 20 none M22 15-30 20 PTIO M23 15-30 20 ATU M24 15-30 20 both M25 15-30 100 none M26 15-30 100 PTIO M27 15-30 100 ATU M28 15-30 100 both M29 15-30 200 none M30 15-30 200 PTIO M31 15-30 200 ATU M32 15-30 200 both 43 2.2.3.1 Experiment 1- Varying urea concentration and soil depth In experiment 1, 10 uM of allylthiourea (ATU) (Sigma-Aldrich, Oakville, ON), primarily an inhibitor of AOB, and 100 uM of NO-scavenger 2-(4-Carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (PTIO) (Sigma-Aldrich, Oakville, ON), primarily an inhibitor of AOA, were tested with three concentrations of nitrogen (0, 20, 200 mg N/kg of soil in the form of urea) (Table 4). Both concentrations of inhibitors had been tested in pure cultures of several species of Archaea and Bacteria, and therefore, were selected for this study (Shen et al. 2013, Martens-Habbena et al. 2015). The soil samples were collected from a grape vineyard at two soil depths (0-15, 15-30 cm). These samples were dried, and 5 g were placed at the bottom of a 60-ml test tube. To determine the nitrous oxide production from these microcosms, a mixture of urea and/or inhibitor was prepared and pipetted into 5 g of dried soil. The amount of this solution that was added back was equal to the amount of water lost in the drying process. I aimed to keep a similar moisture content to that found in the vineyard. The treatment test tubes were then sealed with Suba Seals (Ace Glass Incorporated, Vineland, NJ). Three vials without soil were also sealed to obtain a lab air sample for each day. There were three replicates of each treatment. Gas samples were collected in pre-evacuated 4.5-ml double-wadded Exetainers (Labco Ltd, Lampeter, Wales) using a 10-ml gas-tight glass syringe (National Scientific, Rockwood, TN). The samples for this experiment were collected every day for 14 days. At the end of the experiment, a soil sub-sample was collected for DNA extraction and qPCR analysis for nitrifying Archaea and Bacteria following protocols in sections 3.2.4 and 3.2.5, respectively. 44 2.2.3.2 Experiment 2- Varying concentrations of ATU and PTIO inhibitors This experiment was performed to test the appropriate concentration of ATU in a soil environment; concentrations higher than 10 µM were selected (Table 5). Shen et al. (2013) showed this concentration to be effective against Bacterial isolates in a pure culture. Since inhibitors can have a reduced effect in the soil environment, higher concentrations may be required to obtain the same inhibitory effect. The concentrations of this inhibitor and the treatments of this experiment are described in Table 5. One hundred micromolar PTIO was used by Shen et al. (2013) in pure culture to inhibit the Archaeal isolates (Jäntti et al. 2013, Shen et al. 2013, Sonthiphand and Neufeld 2014, Martens-Habbena et al. 2015). For the same reason as described above for ATU, higher concentrations of PTIO were tested in soil to determine its effectiveness against AOA (Table 5). Additionally, two urea concentrations were tested (0 and 200 mg N/kg of soil). This experiment was performed in triplicate. Three empty vials were also sealed for the collection of lab air for each sampling day. Gas samples for this experiment were collected every second day for 10 days, similar to experiment 1. At the end of the experiment, a soil sample was collected for DNA extraction and qPCR analysis for nitrifying Archaea and Bacteria following protocols in sections 3.2.4 and 3.2.5, respectively. 2.2.3.3 Experiment 3- Varying urea concentrations and soil depth with fixed concentrations of domain-specific inhibitors Experiment 3 was performed, using the optimal inhibitor concentration found in Experiment 2, and four nitrogen concentrations in the form of urea (Biorad, Hercules, CA) (0, 20, 100 and 200 mg N/ kg of dry soil) (Table 6). ATU (Sigma-Aldrich, Oakville, ON) was added to the appropriate tubes to inhibit AOB and allow measurement of N2O 45 production by the AOA population (Shen et al. 2013). NO-scavenger PTIO (Sigma-Aldrich, Oakville, ON) was added to other selected tubes to inhibit the AOA population (Jäntti et al. 2013, Shen et al. 2013, Sonthiphand and Neufeld 2014, Martens-Habbena et al. 2015). Control vials with no inhibitor added as well as experimental vials with both inhibitors were also tested for nitrous oxide production. After the application of the treatments to the test tubes, they were all sealed with Suba Seals (Table 6). This experiment was performed with three replicates of each treatment. Gas samples were collected as per the previous experimental procedure every second day for 10 days. Three empty vials were also sealed for the collection of lab air for each sampling day. At the end of the experiment, a soil sample was collected for DNA extraction and qPCR analysis for nitrifying Archaea and Bacteria following protocols in sections 3.2.4 and 3.2.5, respectively. 2.2.3.4 Gas Collection and Calculations performed Exetainers were first pre-evacuated to <27 Pa pressure, flushed with helium and then re-evacuated to <27 Pa. Ten milliliters of sample were collected, using a 10-ml gas tight syringe, from each experimental 60-ml test tube every 24 or 48 h for a period of 14 or 10 days. After each sample was obtained, the 60-ml experimental test tubes were opened and allowed to equilibrate to room air before closing the vials with the Suba Seals. The gas samples in Exetainers were analyzed using a gas chromatograph (GC) (Brucker Daltonics Inc. 456) equipped with electron capture detector (ECD) and headspace combi-pal autosampler. The GC was calibrated with fifteen low (0.220 ppm N2O), medium (1 ppm N2O) and high (2.20 ppm N2O) standards. The value that was generated for N2O is given in ppm. Firstly, the moles of gas that were in the 60-ml vial were calculated with the equation 4, 5 and 6. 46 rs = tuv (assume number of moles is 1) Eq. 4 s% = uvr Where P represents the pressure (atm), T is the absolute temperature (K), R is the ideal gas constant (0.08206 atm) and V2 is the volume of 1 mole of gas. To calculate moles of gas in the 60-ml test tube, the equation below was used. Ç! (ÉÑÖ*,) Ü! = Ç" (ÉÑÖ*,)Ü" Eq. 5 Where n1 in the moles of gas in an experimental test tube, V1 is the volume of an experimental tube, V2 is the volume of 1 mole of gas and t% is 1 mole. The equation below was used to determine the moles of N2O and convert ppm of N2O into moles of N2O. $%&(áàâäã) = $%& (ååá) ∗ ç ÉÑÖ*çé# èèÉ ∗ tç (áàâäã) Eq. 6 Where $%& (áàâäã) is the amount of nitrous oxide in moles, $%&(ååá) is the concentration of $%& in the sample detected by the GC and n1 is the moles of gas in an experimental test tube. Equation 6 above was also used to calculate moles of nitrous oxide in the experimental tubes as well as control tubes which had no soil. The difference between the moles of nitrous oxide produced in the experimental tubes and the control tubes was calculated to determine the moles of nitrous oxide produced by the treatment alone in each 60-ml test tube. Equation 7 was used to calculate the mass of nitrogen in nitrous oxide produced in the experimental tubes. êëãã $ (í) = $%&(áàâäã) ∗ É+,, Ñì 7 îÇ 7"5(ï)ç ÉÑÖ Eq. 7 47 Where êëãã $ (í) is the mass of nitrogen from nitrous oxide production, $%& (áàâäã) is amount of nitrous oxide in moles, mass of N in N2O(g))/ 1 mol is 28.02 g/mol. The rate of nitrous oxide produced per gram of dry soil per day was calculated (Equation 8). uëñä $ = 4+,, 7 (ï)+ÉÑ(Çó Ñì ,ÑîÖ∗ò+ô, Eq. 8 Where uëñä $ is the rate at which the nitrogen was produced (g N/g of dry soil/day); amount of soil was 5 g and days was the amount of time the experimental tubes were closed for before sample was collected. The total nitrogen for the duration of the experiment was also calculated (Equation 9). vàñëâ $ = ∑êëãã $ò+ô Eq. 9 Where Total N is the total nitrogen that was produced for the duration of the experiment and êëãã $ò+ô is the mass of nitrogen on one day of the experiment. The sum of all of the mass of nitrogen on all days from the same experimental tube gives the total nitrogen produced from that 60-ml test tube over the period of the experiment. 2.3 Statistical Analysis Means of the three replicates were calculated on each day of sampling as well as the total nitrous oxide produced for the duration of the experiment. Means and standard errors of replicates were calculated using Microsoft Office Excel (2010, Microsoft Corp, WA, USA). One-way analysis of variance (ANOVA) was performed to determine if there were significant effects of treatments on the nitrous oxide emissions from the 60-ml test tubes (p<0.05) using SPSS version 20.0 (IBM Co., Armonk, NY, USA) for the field study data. 48 Normality and homogeneity of the variance were tested and corrected for before ANOVA was performed. Data were normalized with log10, square root or cube root transformations when necessary. Pearson correlations were used to determine if the nutrient content and nitrous oxide fluxes were correlated with nitrifying Archaeal and Bacterial abundance in SAS (Version 9.3; SAS1 Institute, Inc., Cary, NC). In the in vitro microcosm experiment, a univariate test was performed to determine if there was a significant effect of inhibitor and urea concentration on nitrous oxide production over 1 (experiment 1) or 2 (experiment 2 and 3) days and total emissions over the duration of the experiment (p<0.05). Tukey Post Hoc test was used to determine if significant differences existed between means at the 95% confidence level (SPSS). 49 3 Results The results section of my thesis is divided into the field study and then followed by the in vitro microcosm experiment. 3.1 Field Experiment 3.1.1 Apple orchard The effects of orchard amendments on the abundance of total populations of Archaea and Bacteria and nitrifying populations in an apple orchard were determined by sampling 5 times between August 2013 and June 2015. Total Bacteria averaged over all treatments were more abundant in August 2014 compared to other samples collected in 2013 and 2014 (Table 7). Total Archaeal abundance over all treatments was highest in August 2014 and was the lowest in September 2014. In August 2013 and 2014 and September 2013, the mean total populations of Bacteria were at least four times more abundant in this agricultural soil than those of Archaea with the highest ratios of 8:1 in September 2014 and 7:1 in June 2015 (Table 8). The mean total Bacterial abundance was significantly higher when compared with total Archaeal, nitrifying Archaeal and nitrifying Bacterial abundances at all sampling times (August 2013, August 2014, September 2013, September 2014 and June 2015) (Table 7). The overall mean nitrifying Bacterial abundance did not differ significantly among the different sampling times (Table 7). Mean nitrifying Archaeal abundance was significantly higher in 2013 than in 2014 and 2015 samples. The overall mean abundance of nitrifying Archaea was significantly lower than that of the total Archaea in samples collected in 2013. 50 The ratio of total Bacteria to nitrifying Bacteria was higher in August 2014 than at other sampling times (Table 8). The total Bacteria to nitrifying Bacterial ratios were considerably higher than the total Archaeal to nitrifying Archaeal ratios. The overall mean total Archaeal abundance was significantly higher in 2013 (August and September) than that of the nitrifying Archaeal community (Table 7). The ratio of total Archaeal to nitrifying Archaeal abundance averaged 30:1 in 2013 and September 2014 but due to a decrease in the nitrifying Archaeal abundance and an increase in the total Archaeal abundance the ratio increased to 272:1 in August 2014 (Table 8). As a result of an increase in the nitrifying Archaeal abundance in 2013, nitrifying Archaea were five times more abundant than nitrifying Bacteria in 2013 but roughly equal in 2014 and 2015. (Table 8). Table 7: Means over all treatments at each sampling time of total and nitrifying Archaeal and Bacterial abundance in an apple orchard. Means with the same lower-case letter are not significantly different within each column (p>0.05). Means with the same upper-case letter are not significantly different within each row (p>0.05). Sampling Date Total Bacteria (x106) Total Archaea (x106) Nitrifying Bacteria (x106) Nitrifying Archaea (x106) August 2013 680 bA 141 bB 0.829 aC 4.43 aBC September 2013 665 bA 155 bB 0.894 aC 5.13 aC August 2014 1130 aA 266 aB 0.982 aB 0.975 bB September 2014 568 bA 67.5 cB 1.30 aB 2.14 bB June 2015 900 abA 132 bB 1.92 aB 2.32 bB Table 8: Ratio between abundances of total and nitrifying Archaeal and Bacterial populations over all treatments at each sampling time in an apple orchard. Sampling Date Total Bacteria: Total Archaea Total Bacteria: Nitrifying Bacteria Total Archaea: Nitrifying Archaea Nitrifying Archaea: Nitrifying Bacteria August 2013 5:1 821:1 32 :1 5:1 September 2013 4:1 744:1 30:1 6:1 August 2014 4:1 1156:1 272:1 1:1 September 2014 8:1 437:1 32:1 2:1 June 2015 7:1 470:1 57:1 1:1 51 The total Bacterial abundance tended to decrease with increasing soil depth (Figure 5). In August 2013 (Figure 5A), there was a decrease in Bacterial abundance as soil depth increased, independent of mulch application or calcium nitrate concentration (not significant when mulch and 40 g N/ tree were applied). In September 2013, a significant decrease with increase in soil depth was observed when mulch and 20 g N/tree were applied and in the absence of mulch with 40 g N/ tree (Figure 5B). Despite the two-fold increase in abundance of Bacteria in August 2014 compared to August 2013, the trend was similar in the August 2014 sample: total Bacteria decreased with depth in the treatments with 20 g N/tree (Figure 5C). In September 2014, there were no significant differences with soil depth or treatment (Figure 5D). In June 2015 (Figure 6A), in the treatments with mulch, a decrease in the total Bacterial abundance was observed with increasing depth. Nitrifying Bacterial populations (Figure 7) showed no effect of depth, nitrogen concentration or mulch application at any sampling time. The total Archaeal abundance decreased with soil depth in August 2013 (Figure 8A) (p=0.05) regardless of treatment but there were no significant effects of depth at other sample times except for the June 2015 sample (Figure 6A). At the June 2015 sampling, total Archaeal abundance decreased with increasing depth, but only with low application of nitrogen (20 g N/ tree) in the presence of mulch (Figure 6A). Neither mulch nor nitrogen concentration had an effect on the abundance of Archaea in the apple plot at any sampling time (Figure 8). The abundance of nitrifying Archaea increased with increasing depth in all treatments in August 2013 (Figure 9A), except at 40 g N/ tree without mulch, and also increased with depth in June 2015 (Figure 6B) at 20 g N/ tree with mulch. In August 2014 (Figure 9C), 52 September 2013 (Figure 9B) and 2014 (Figure 9D) there was no effect of depth, nitrogen concentration or mulch application on the abundance of nitrifying Archaeal populations. As total Archaeal abundance tended to decrease with increasing depth (Figure 8), the ratio of nitrifying Archaea to total Archaea was higher at lower depths (data not shown). 53 Figure 5: The abundance of total Bacterial populations in the apple orchard determined by qPCR using Bacterial 16S-rRNA specific primers (BACT1369F and PROK1492R) at 4 sampling times: A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no mulch (N) or presence of mulch (Y), second letter: 20 g Ca(NO3)2/tree (L) or 40 g Ca(NO3)2/ tree (H), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). aabbababbaabbababb0.05.010.015.020.025.030.035.0copy # (x10^8) /g of dry soilTreatmentAUGUST 2014Cabbccababccabccabccc0.05.010.015.020.025.030.035.0copy # (x10^8) / g of dry soilTreatmentAUGUST 2013Aabababababaab ababbabab0.05.010.015.020.025.030.035.0copy # (x10^8) / g of dry soilTreatmentSEPTEMBER 2014Dabbcbcabc bcabbccabc bcbc0.05.010.015.020.025.030.035.0copy # (x10^8) / g of dry soilTreatmentSEPTEMBER 2013 B 54 Figure 6: The abundance of total Bacterial (A), nitrifying Bacterial (B), total Archaeal (C) and nitrifying Archaeal (D) in the apple plot determined by qPCR using gene-specific primers in June 2015. Abbreviations represent different treatments: first Letter: no mulch (N) or presence of mulch (Y), second letter: 20 g Ca(NO3)2/ tree (L) or 40 g Ca(NO3)2/ tree (H), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). ababbabababaabbabbb0.00.51.01.52.02.53.03.54.0copy # ( x10^8)/ g of dry soilTreatmentTOTAL ARCHAEA Cababcabcabcabc abcaabcbcababcc0.05.010.015.020.025.030.035.040.0copy # ( x10^8) / g of dry soilTreatmentTOTAL BACTERIA Abababbababbabaab abab0.02.04.06.08.010.012.014.0copy # ( x10^6) / g of dry soilTreatmentNITRIFYING ARCHAEA Da a aaa aaa aaa a0.02.04.06.08.010.012.014.0copy # ( x10^6) / g of dry soilTreatmentNITRIFYING BACTERIA B 55 Figure 7: The abundance of nitrifying Bacteria in the apple orchard determined by qPCR using Bacterial amoA-specific primers (amoA-1F and amoA-2R) at 4 sampling times A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no mulch (N) or presence of mulch (Y), second letter: 20 g Ca(NO3)2/tree (L) or 40 g Ca(NO3)2/ tree (H), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). aaaaaaaaaaaa0.00.51.01.52.02.53.03.54.0copy # (x10^6) /g of dry soilTreatmentAUGUST 2013 Aabababababababab ababa0.00.51.01.52.02.53.03.54.0copy # (x10^6) / g of dry soilTreatmentAUGUST 2014Caaaaaaaaaaaa0.00.51.01.52.02.53.03.54.0copy # (x10^6) / g of dry soilTreatmentS EP TEMBER 2013 Baa aaaaaaaaaa0.00.51.01.52.02.53.03.54.0copy # (x10^6) / g of dry soilTreatmentS EP TEMBER 2014D 56 Figure 8:The abundance of total Archaeal populations in the apple orchard as determined by qPCR using Archaeal 16S-rRNA specific primers (A364aF and A934Br) at 4 sampling times: A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no mulch (N) or presence of mulch (Y), second letter: 20 g Ca(NO3)2/ tree (L) or 40 g Ca(NO3)2/ tree (H), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). ababcabcabcabcbcaabcabcabcabcc0.01.02.03.04.05.06.07.0copy # (x10^8) / g of dry soilTreatmentAUGUST 2014Caabcdeabc adeaabcdcdeabde e0.01.02.03.04.05.06.07.0copy # (x10^8) /g of dry soilTreatmentAUGUST 2013 Aa a a a aa aa aa aa0.01.02.03.04.05.06.07.0copy # (x10^8) / g of dry soilTreatmentSEPTEMBER 2014Dab ababaababababbababab0.01.02.03.04.05.06.07.0copy # (x10^8) / g of dry soilTreatmentSEPTEMBER 2013 B 57 Figure 9: The abundance of nitrifying Archaeal populations in the apple orchard determined by qPCR using Archaeal amoA-specific primers (A23F and A616r) at 4 sampling times: A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no mulch (N) or presence of mulch (Y), second letter: 20 g Ca(NO3)2/ tree (L) or 40 g Ca(NO3)2/ tree (H), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate trees. Bars headed by the same letters are not significantly different (p>0.05).eabcabcdeab abecdeabcddebcdea0.02.04.06.08.010.012.014.0copy # (x10^6) / g of dry soilTreatmentAUGUST 2013 Abc abcababcabcacabcabcabcabca0.02.04.06.08.010.012.014.0copy # (x10^6) /g of dry soilTreatmentAUGUST 2014 Caaaaaaaaaaaa0.02.04.06.08.010.012.014.0copy # (x10^6) /g of dry soilTreatmentS EP TEMBER 2013 Baab abababbaab aba abab0.02.04.06.08.010.012.014.0copy # (x10^6) /g of dry soilTreatmentS EP TEMBER 2014D 58 Orchard soil was analyzed for percent nitrogen (N) (Table 9), percent carbon (C) (Table 10), ammonium nitrogen (NH4-N) content (Table 11) and nitrate nitrogen (NO3-N) content (Table 12). In the samples collected in August 2014 from the apple plot, mulch application decreased percent N content (Table 9). At all sampling times, percent N (Table 9) and percent C (Table 10) were higher in surface soils than in soils at lower depth (10-30 cm). Mulch application and nitrogen concentration did not affect the percent C content of this apple plot (Table 10). NH4-N was not affected by mulch application or nitrogen concentration (Table 11). In September 2013 and 2014, NH4-N concentration was significantly higher at 0-10 cm depth than at 20-30 cm (Table 11). In August 2013, NO3-N was also significantly higher in surface soil (0-10 cm) than at lower depths (10-30 cm) (Table 12). Mulch application reduced the NO3-N concentration in the apple plot at all sampling times except for September 2014 (p = 0.056) (Table 12). NO3-N concentration increased with the application of 40 g N/ tree compared to 20 g N/ tree in June 2015 (Table 12). Pearson correlations were determined between nitrifying Archaeal or Bacterial abundance and nitrous oxide flux (Fentabil et al. 2016a) (Table 13). Nitrifying Archaeal abundance in surface soils was negatively correlated with N2O flux but nitrifying Archaeal abundance in 20-30 cm soils was strongly positively correlated with N2O emissions (Table 13). Nitrifying Archaeal abundance also exhibited a negative correlation with percent N, percent C, NH4-N and NO3-N; while no correlation was observed between nitrifying Bacteria and any of the measured soil nutrients. Moreover, total Bacterial and Archaeal abundances were all positively correlated with percent nitrogen and carbon (Table 14). 59 Table 9: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on percent soil nitrogen in an apple orchard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05). Total Nitrogen (%) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 0.077 a 0.089 a 0.096 a 0.085 a 0.079 a Mulch Present 0.072 a 0.081 a 0.080 b 0.085 a 0.071 a Nitrogen 20 g N/tree 0.073 a 0.086 a 0.087 a 0.086 a 0.075 a 40 g N/ tree 0.076 a 0.084 a 0.088 a 0.084 a 0.075 a Depth 0-10 cm 0.096 a 0.127 a 0.107 a 0.105 a 0.100 a 10-20 cm 0.072 b 0.081 b 0.088 b 0.088 b 0.070 b 20-30 cm 0.057 b 0.052 c 0.068 c 0.062 c 0.054 c Table 10: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10cm, 10-20 cm, 20-30 cm) on percent soil carbon in an apple orchard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05). Total Carbon (%) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 0.873 a 0.905 a 0.893 a 0.807 a 0.801 a Mulch Present 0.873 a 0.887 a 0.824 a 0.909 a 0.754 a Nitrogen 20 g N/tree 0.801 a 0.917 a 0.917 a 0.884 a 0.884 a 40 g N/ tree 0.754 a 0.875 a 0.875 a 0.832 a 0.832 a Depth 0-10 cm 1.068 a 1.342 a 1.036 a 1.087 a 1.102 a 10-20 cm 0.831 b 0.830 b 0.832 b 0.891 a 0.696 b 20-30 cm 0.720 b 0.513 c 0.709 c 0.598 b 0.534 b 60 Table 11: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil ammonium-nitrogen in an apple orchard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05). Ammonium Nitrogen (mg/ kg of soil) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 3.28 a 1.58 a 1.42 a 1.85 a 2.16 a Mulch Present 2.72 a 2.01 a 1.32 a 2.04 a 0.87 a Nitrogen 20 g N/tree 2.82 a 1.89 a 1.89 a 1.97 a 1.97 a 40 g N/ tree 3.18 a 1.71 a 1.71 a 1.92 a 1.92 a Depth 0-10 cm 3.59 a 3.10 a 1.65 a 2.47 a 2.73 a 10-20 cm 2.71 a 1.66 b 1.39 a 1.99 ab 0.98 a 20-30 cm 2.70 a 0.65 c 1.07 a 1.38 b 0.83 a Table 12: Effect of mulch, nitrogen concentration (20 g N/tree or 40 g N/tree) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil nitrate-nitrogen in an apple orchard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05). Nitrate Nitrogen (mg/ kg of soil) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 4.42 a 13.78 a 8.61 a 12.00 a 18.31 a Mulch Present 1.42 b 1.25 b 1.26 b 1.20 a 4.67 b Nitrogen 20 g N/tree 2.93 a 8.18 a 4.36 a 4.37 a 7.14 b 40 g N/ tree 2.92 a 6.85 a 5.51 a 8.83 a 15.84 a Depth 0-10 cm 6.66 a 12.32 a 9.22 a 14.11 a 13.88 a 10-20 cm 1.40 b 5.48 a 4.02 a 3.27a 10.33 a 20-30 cm 0.71 b 4.74 a 1.58 a 2.41 a 10.26 a 61 Table 13: Pearson correlations between nitrifying Bacterial and Archaeal abundance and different parameters in the apple orchard with combined data from August and September 2013 and 2014. The first value represents the Pearson correlation coefficient and the second value is the p value. Parameter Soil Sample Depth (cm) Nitrifying Bacteria Nitrifying Archaea Nitrous Oxide Flux 0-10 -0.23 (p= 0.12) -0.33 (p= 0.03) 10-20 -0.19 (p= 0.20) 0.09 (p= 0.57) 20-30 -0.20 (p= 0.18) 0.53 (p= <0.0001) Percent Nitrogen 0-30 -0.10 (p= 0.23) -0.38 (p= <0.0001) Percent Carbon 0-30 -0.14 (p= 0.09) -0.33 (p= <0.0001) Ammonium Nitrogen 0-30 -0.02 (p= 0.79) -0.21 (p= 0.013) Nitrate Nitrogen 0-30 0.02 (p= 0.79) -0.22 (p= 0.008) Table 14: Pearson correlations between total Bacterial and Archaeal abundance and percent nitrogen and carbon in the apple orchard soil with combined data from August and September 2013 and 2014. The first value represents the Pearson correlation coefficient and the second value is the p value. Parameter Soil Sample Depth (cm) Total Bacteria Total Archaea Percent Nitrogen 0-30 0.48 (p= <0.0001) 0.53 (p= <0.0001) Percent Carbon 0-30 0.68 (p= <0.0001) 0.59 (p= <0.0001) 62 3.1.2 Grape vineyard In the grape vineyard, a similar trend was observed as in the apple orchard. The average total Bacterial abundance was highest in June 2015 and lowest in September 2014 (Table 15). Total Archaeal abundance was significantly higher in August 2013 and 2014 than in September 2014 and June 2015 (Table 15). Total Bacterial abundance was 5 times higher than total Archaeal abundance, but in June 2015 this abundance was 10-fold higher (Table 16). Total Bacterial abundance was significantly higher than total Archaeal, nitrifying Archaeal and Bacterial abundances at all sampling times (Table 15). The average nitrifying Bacterial abundance was highest in June 2015 and lowest in August 2013 (Table 15). No significant differences were found at each sampling date between the total Archaeal abundance and nitrifying Archaeal abundance (Table 15). The ratio between total and nitrifying Bacteria ranged from 325:1 in September 2014 to 1962:1 in August 2013 (Table 16). Mean abundance of total Archaea was not significantly different from that of nitrifying Archaea and Bacteria (Table 15). Table 15: Mean of all treatments at each sampling time of total and nitrifying Archaeal and Bacterial abundance in a grape vineyard. Means with the same lower-case letter are not significantly different within each column (p>0.05). Means with the same upper-case letter are not significantly different within each row (p>0.05). Sampling date Total Bacteria (x106) Total Archaea (x106) Nitrifying Bacteria (x106) Nitrifying Archaea (x106) August 2013 1560 abA 310 aB 0.796 bB 2.06 aB September 2013 1690 abA 204 abB 1.32 abB 2.02 aB August 2014 1700 abA 300 aB 1.73 abB 2.41 aB September 2014 928 bA 157 bB 2.86 abB 3.53 aB June 2015 1830 aA 181 bB 3.01 aB 1.87 aB 63 Table 16: Ratios between abundances of total and nitrifying Archaeal and Bacterial populations at all sampling times in a grape vineyard. Sampling date Total Bacteria: Total Archaea Total Bacteria: Nitrifying Bacteria Total Archaea: Nitrifying Archaea Nitrifying Archaea: Nitrifying Bacteria August 2013 5:1 1962:1 151:1 3:1 September 2013 8:1 1274:1 101:1 2:1 August 2014 6:1 983:1 124:1 1:1 September 2014 6:1 325:1 44:1 1:1 June 2015 10:1 609:1 97:1 1:1 Overall, both the total Bacterial and Archaeal abundances tended to decrease as the soil depth increased. In August 2013 (Figure 10A) and 2014 (Figure 10C), the total Bacterial abundance decreased as soil depth increased. In September 2013 (Figure 10B) and June 2015 (Figure 11C), no significant differences were observed with soil depth, mulch application or nitrogen source. In September 2014 (Figure 11D), mulch and compost application contributed to a decrease in total Bacterial abundance with soil depth. Total Archaeal abundance also decreased with soil depth in August 2013 in all mulch and nitrogen treatments (Figure 12A). This result was also observed in September 2013 in the urea fertigated treatment without mulch (Figure 12B). No differences were observed in August 2014 (Figure 12C), September 2014 (Figure 12D) and June 2015 (Figure 11A). Nitrifying Bacterial abundance did not generally show any effect of soil depth in this grape vineyard soil. However, the mulch application and the nitrogen source did have an effect on the nitrifying Bacterial abundance. An increase in their abundance with soil depth was seen in August 2014 (Figure 13B) in the soil fertigated with urea in the presence of mulch. In August 2013, nitrifying Bacteria were higher in soil samples that were fertigated with urea than with compost (Figure 13A), in the first 10 cm of soil. A similar result was 64 observed in September 2014; in samples collected in the absence of mulch, nitrifying Bacterial abundance was significantly higher when fertigated with urea than when compost was applied (Figure 13D). In soils collected from 0-10 cm in August 2013 (Figure 13A), a decrease in the nitrifying Bacterial abundance was observed when mulch was applied. Additionally, In September 2013 (0-10 cm soil) (Figure 13B), mulch application reduced the nitrifying Bacterial abundance in soils with compost applied and in September 2014 (0-10 cm soil) (Figure 13D), mulch application also reduced the nitrifying Bacterial population in the urea-fertigated soils. An increase in Archaeal amoA abundance, detected using qPCR, with soil depth was significant in August 2013 (Figure 14A) and 2014 (except for mulch and compost treatment in 2014) (Figure 14C). In September 2013 (Figure 14B) and June 2015 (Figure 11B), a significant increase was seen in the nitrifying Archaeal population when vineyard soil depth increased in the presence of compost and mulch. In surface soils collected in August 2013, the nitrifying Archaea were higher in soil samples that were fertigated with urea and when mulch was not applied rather than when compost and mulch were applied (Figure 14A). In September 2014, no significant differences in nitrifying Archaeal abundance with soil depth, mulch application or nitrogen source were observed (Figure 14D). 65 Figure 10: The abundance of total Bacterial populations in the grape vineyard determined by qPCR using Bacterial 16S-rRNA specific primers (BACT1369F and PROK1492R) at 4 sampling times: A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no presence of mulch (N) or presence of mulch (Y), second letter: compost (C) or urea fertigation (F), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). ababcddabccddaabcdcdabbcdd0.010.020.030.040.050.060.070.080.0copy # (x10^8) /g of dry soilTreatmentAUGUST 2014 Cabccdabbcdaabdabbcd0.010.020.030.040.050.060.070.080.0copy # (x10^8) /g of dry soilTreatmentAUGUST 2013 Aabbc bcabcbc cabcbc abcbc bc0.010.020.030.040.050.060.070.080.0copy # (x10^8) / g of dry soilTreatmentSEPTEMBER 2014Dabababaababababbababab0.010.020.030.040.050.060.070.080.0copy # (x10^8) /g of dry soilTreatmentSEPTEMBER 2013 B 66 Figure 11: The abundance of total Bacterial (A), nitrifying Bacterial (B), total Archaeal (C) and nitrifying Archaeal (D) in the grape plot determined by qPCR using gene-specific primers in June,2015. Abbreviations represent different treatments: first Letter: no presence of mulch (N) or presence of mulch (Y), second letter: compost (C) or urea fertigation (F), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May, 2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). abbbababbaabab abbb0.010.020.030.040.050.060.070.0copy # ( x10^8) / g of dry soilTreatmentTOTAL BACTERIA Aabab abab abababaababa0.02.04.06.08.010.012.014.0copy # ( x10^6) / g of dry soilTreatmentNITRIFYING ARCHAEADabab ababababbabbb ab0.02.04.06.08.010.012.014.0copy # ( x10^6) / g of dry soilTreatmentNITRIFYING BACTERIABaaaaaaaaa aaa0.01.02.03.04.05.06.07.0copy # ( x10^8) / g of dry soilTreatmentTOTAL ARCHAEA C 67 Figure 12: The abundance of total Archaeal populations in the grape vineyard determined by qPCR using Archaeal 16S-rRNA specific primers (A364aF and A934Br) at 4 sampling times: A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no presence of mulch (N) or presence of mulch (Y), second letter: compost (C) or urea fertigation (F), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05) aaaaa aaaaaaa0.02.04.06.08.010.012.0copy # (x10^8) /g of dry soilTreatmentAUGUST 2014Caabcdbcdeaabceaacdeaabde0.02.04.06.08.010.012.0copy # (x10^8) /g of dry soilTreatmentAUGUST 2013 Aaa aa a aaaaaa a0.02.04.06.08.010.012.0copy # (x10^8) / g of dry soilTreatmentSEPTEMBER 2014Dab ababaabbab abbab abab0.02.04.06.08.010.012.0copy # (x10^8) /g of dry soilTreatmentSEPTEMBER 2013 B 68 Figure 13: The abundance of nitrifying Bacterial populations in the grape vineyard determined by qPCR using Bacterial amoA-specific primers (amoA-1F and amoA-2R) at 4 sampling times: A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no presence of mulch (N) or presence of mulch (Y), second letter: compost (C) or urea fertigation (F), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). b ab abaaabcab ab b ab ab0.02.04.06.08.010.012.014.016.018.0copy # (x10^6) / g of dry soilTreatmentAUGUST 2013 Aab ab ab abaab ab ab baa0.02.04.06.08.010.012.014.016.018.0copy # (x10^6) /g of dry soilTreatmentAUGUST 2014Caaabaa ababab ab aba0.02.04.06.08.010.012.014.016.018.0copy # (x10^6) /g of dry soilTreatmentSEPTEMBER 2013 Bbcbc caab abcbc bc bcbcbcbc0.02.04.06.08.010.012.014.016.018.0copy # (x10^6) / g of dry soilTreatmentSEPTEMBER 2014 D 69 Figure 14: The abundance of nitrifying Archaeal populations in the grape vineyard determined by qPCR using Archaeal amoA-specific primers (A23F and A616r) at 4 sampling times: A: August 2013, B: September 2013, C: August 2014, and D: September 2014. Abbreviations represent different treatments: first Letter: no presence of mulch (N) or presence of mulch (Y), second letter: compost (C) or urea fertigation (F), number: soil depth 0-10 cm (5), 10-20 cm (15) or 20-30 cm (25). Mulch consisted of bark and wood chips and was applied in May,2014. The error bars represent the standard error of the mean of triplicate samples. Bars headed by the same letters are not significantly different (p>0.05). dbcabcabcadcabdabcab0.02.04.06.08.010.0copy # (x10^6) /g of dry soilTreatmentAUGUST 2013Adbcdabdabcdadcdabcddabcdabc0.02.04.06.08.010.0copy # (x10^6) /g of dry soilTreatmentAUGUST 2014Cab abaabaabaaababa0.02.04.06.08.010.0copy # (x10^6) / g of dry soilTreatmentSEPTEMBER 2013Baaaaa aaaaaaa0.02.04.06.08.010.0copy # (x10^6) / g of dry soilTreatmentSEPTEMBER 2014D 70 In the grape plot and at all sampling times, percent N (Table 17) and percent C ( Table 18) were higher in top soil (0-10 cm) than at lower depths (10-30 cm). Percent N content was not affected by the application of mulch. An increase in the percent N was observed with compost application compared to urea-fertigation in August 2013 and September 2013 and 2014. Similarly, percent C was higher in soils to which compost was applied than in urea-fertigated treatments in August 2013 and September 2013 and 2014. Mulch also increased the percent C of these vineyard soils in August 2013. NH4-N was significantly higher at 0-10 cm than at lower soil depths (20-30 cm) (Table 19). In September 2014, NH4-N content was higher in soils to which mulch was applied (Table 19). Compost application also significantly increased the NH4-N content in September 2013 (Table 20). NO3-N was significantly higher in soils at 0-10 cm depth rather than 20-30 cm depth in September 2013, August 2014 and June 2015. In August 2014 and June 2015, NO3-N was higher in the absence than in the presence of mulch. Also in June 2015, a higher NO3-N content was observed in urea-fertigated treatments than when compost was applied. In the grape vineyard, nitrifying Bacterial abundance was not correlated with N2O flux collected by Fentabil et al. (2016a). Nitrifying Archaeal abundance was positively correlated at 20-30 cm soil depth with N2O flux (Table 21). Nitrifying Archaea showed a strong (p>0.0001) negative correlation with percent nitrogen, percent carbon, ammonium-nitrogen and nitrate-nitrogen. Nitrifying Bacteria also exhibited a negative correlation with these soil nutrients except nitrate-nitrogen. Both total Bacterial and Archaeal abundances were positively correlated with percent nitrogen and carbon (Table 22). 71 Table 17: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on percent soil nitrogen in a grape vineyard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05). Total Nitrogen (%) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 0.130 a 0.124 a 0.143 a 0.126 a 0.146 a Mulch Present 0.141 a 0.127 a 0.146 a 0.125 a 0.139 a Nitrogen Compost 0.146 a 0.142 a 0.145 a 0.143 a 0.148 a Fertigation 0.125 b 0.110 b 0.144 a 0.108 b 0.137 a Depth 0-10 cm 0.204 a 0.191 a 0.200 a 0.181 a 0.206 a 10-20 cm 0.127 b 0.118 b 0.143 b 0.111 b 0.129 b 20-30 cm 0.076 c 0.069 c 0.090 c 0.085 c 0.093 c Table 18: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on percent soil carbon in a grape vineyard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05). Total Carbon (%) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 1.31 b 1.35 a 1.35 a 1.41 a 1.59 a Mulch Present 1.70 a 1.64 a 1.46 a 1.65 a 1.82 a Nitrogen Compost 1.762 a 1.775 a 1.482 a 1.808 a 1.888 a Fertigation 1.251 b 1.215 b 1.333 a 1.257 b 1.526 a Depth 0-10 cm 2.486 a 2.508 a 2.113 a 2.353 a 2.772a 10-20 cm 1.271 b 1.268 b 1.283 b 1.213 b 1.365 b 20-30 cm 0.760 b 0.709 c 0.828 c 1.033 b 0.983 b 72 Table 19: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil ammonium-nitrogen in a grape vineyard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05) Table 20: Effect of mulch, nitrogen source (compost or urea via fertigation) and soil depth (0-10 cm, 10-20 cm, 20-30 cm) on soil nitrate-nitrogen in a grape vineyard at Summerland Research and Development Centre, Summerland, BC at 5 different sampling times (August 2013, September 2013, August 2014, September 2014 and June 2015). Means with the same letter within each treatment, were not significantly different (p>0.05). Ammonium Nitrogen (mg/ kg of soil) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 3.53 a 2.79 a 1.87 a 2.29 b 5.70 a Mulch Present 3.69 a 3.53 a 1.59 a 4.35 a 4.71 a Nitrogen Compost 3.56 a 4.05 a 1.85 a 4.11 a 4.32 a Fertigation 3.66 a 2.27 b 1.61 a 2.53 a 6.10 a Depth 0-10 cm 5.13 a 5.76 a 2.84 a 5.06 a 8.74 a 10-20 cm 3.15 b 2.57 b 1.49 b 2.77 ab 4.66 ab 20-30 cm 2.54 b 1.15 b 0.85 b 2.14 b 2.23 b Nitrate Nitrogen (mg/ kg of soil) Treatment Aug 2013 Sept 2013 Aug 2014 Sept 2014 June 2015 Mulch No Mulch 5.36 a 2.24 a 4.99 a 4.82 a 14.92 a Mulch Present 1.84 a 1.59 a 1.65 b 1.83 a 5.82 b Nitrogen Compost 2.49 a 1.95 a 3.49 a 2.59 a 8.08 b Fertigation 4.71 a 1.88 a 3.16 a 4.06 a 12.66 a Depth 0-10 cm 6.99 a 3.30 a 6.42 a 4.38 a 13.82 a 10-20 cm 2.03 a 1.63 b 2.50 b 3.70 a 10.67 ab 20-30 cm 1.78 a 0.82 b 1.04 b 1.90 a 6.61 b 73 Table 21: Pearson correlations between nitrifying Bacterial and Archaeal abundance in the grape vineyard with combined data from August and September 2013 and 2014. The first value represents the Pearson correlation coefficient and the second value is the p value. Table 22: Pearson correlations between total Bacterial and Archaeal abundance in the grape vineyard with combined data from August and September 2013 and 2014. The first value represents the Pearson correlation coefficient and the second value is the p value. Parameter Depth (cm) Total Bacteria Total Archaea Percent Nitrogen 0-30 0.77 (p= <0.0001) 0.54 (p= <0.0001) Percent Carbon 0-30 0.79 (p= <0.0001) 0.62 (p= <0.0001) Parameter Depth (cm) Nitrifying Bacteria Nitrifying Archaea Nitrous Oxide Flux 0-10 -0.15 (p= 0.32) -0.23 (p= 0.12) 10-20 -0.04 (p= 0.81) -0.09 (p= 0.57) 20-30 0.003 (p= 0.99) 0.39 (p= 0.008) Percent Nitrogen 0-30 -0.27 (p= 0.0014) -0.72 (p= <0.0001) Percent Carbon 0-30 -0.31 (p= 0.0002) -0.71 (p= <0.0001) Ammonium Nitrogen 0-30 -0.30 (p= 0.0005) -0.52 (p= <0.0001) Nitrate Nitrogen 0-30 0.16 (p= 0.07) -0.32 (p= <0.0001) 74 3.2 In vitro Microcosm Experiment 3.2.1 Experiment 1- Varying urea concentration and soil depth This in vitro experiment was performed to determine the effect of various concentrations of urea (0, 20, 200 mg N/kg of soil) on the production of nitrous oxide (Table 4). Neither ATU nor PTIO addition significantly affected nitrous oxide production; therefore, the data were combined (Table 23). Overall, I observed higher nitrous oxide emissions with increasing N concentration. During the 14-day period nitrous oxide emissions following application of 200 mg N/kg of soil applied to surface soils (0-15 cm) were significantly higher than when no N was added to soil (not significant on day 14). Additionally, when 20 mg N/kg of soil were applied to these soils, nitrous oxide emissions were significantly increased on days 1-3 compared to soils without any addition of N. Soils with 200 mg N/kg added produced significantly more nitrous oxide on days 2-13 than soils with 20 mg N/kg of soil added. In soils from 15-30 cm depth there was no effect of addition of 20 mg N/kg compared to no addition of urea during the 14-day experimentation period. In soils from 15-30 cm depth addition of 200 mg N/kg of soil increased nitrous oxide emissions relative to those with no added N on day 4 and days 6 to 12. Cumulative nitrous oxide emissions over the 14-day experimental period were highest when 200 mg N/kg of soil of urea was applied at both soil depths (0-15 and 15-30 cm) (Figure 15). The effect of soil depth from which the sample was collected on nitrous oxide emissions was also analyzed at the three different concentrations of nitrogen (Table 23). Without the addition of N, higher N2O emissions were observed in soils at 0-15 cm depth than at 15-30 cm on Day 1. With the addition of 20 mg N/kg of soil, N2O emissions were higher in surface soils (0-15 cm) than at 15-30 cm depth on Days 1-3. With addition of 200 75 mg N/kg of soil higher nitrous oxide emissions were observed at 0-15 cm depth than at 15-30 cm on days 1-9. Nitrous oxide produced when 200 mg N/kg of soil of urea were applied over the duration of this experiment (14 days) was higher from soil collected at 0-15 cm than 15-30 cm (Figure 15). Table 23: Effects of three urea concentrations (0, 20, 200 mg N/kg of soil) on N20 emissions in an in vitro experiment over a 14-day period. Soil was collected from two soil depths (0-15 cm and 15-30 cm) from a grape vineyard at Summerland Research and development Centre, Summerland, BC. Means followed by the same letters within a day are not significantly different (p<0.05). Soil Sample Depth [Urea] (mg N/kg of soil) Nitrous Oxide Flux (ng N/g of dry soil) Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 0-15 0 1.45 b 1.55 c 1.17 c 0.90 bc 1.07b 0.69 c 0.66 c 20 3.30 a 4.67 b 3.56 b 2.28 b 1.97b 0.98 c 0.80 c 200 3.53 a 6.91 a 7.18 a 9.28 a 11.38 a 11.38 a 8.68 a 15-30 0 0.45 c 0.55 c 0.68 c 0.58 c 0.67 b 0.53 c 0.63 c 20 1.30 b 1.69 c 1.26 c 1.17 bc 0.94 b 1.03 c 1.11 c 200 0.80 bc 1.79 c 1.95 c 2.23 b 2.21 b 3.87 b 5.14 b Depth [Urea] (mg N/kg of soil) Day 8 Day 9 Day 10 Day 11 Day 12 Day 13 Day 14 0-15 0 0.64 c 1.04 c 0.56 b 0.37 b 0.48 b 0.50 b 0.40 a 20 0.99 c 1.39 c 0.76 b 0.79 b 0.85 b 0.80 b 0.40 a 200 7.63 a 6.68 a 5.35 a 4.96 a 5.09 a 6.02 a 12.24 a 15-30 0 0.51 c 0.68 c 0.57 b 0.49 b 0.40 b 0.34 b 0.25 a 20 0.91 c 0.74 c 0.69 b 0.64 b 0.56 b 0.47 b 0.26 a 200 6.22 b 5.11 b 4.79 a 4.37 a 4.06 a 3.42 ab 2.91 a 76 Figure 15: Effects of three urea concentrations (0, 20, 200 mg N/kg of soil) on Total N2O emissions in an in vitro experiment over a 14-day period. Soil was collected from two soil depths (0-15 cm and 15-30 cm) from a grape vineyard at Summerland Research and development Centre, Summerland, BC. This experiment was performed in the dark. Means followed by the same letters within a day are not significantly different (p>0.05). The error bars represent the standard error of the mean of nine samples. 3.2.2 Experiment 2- Varying concentrations of ATU and PTIO inhibitors Experiment 2 was conducted to determine the appropriate concentrations of ATU and PTIO inhibitors to add to soil collected from the grape vineyard in June 2016 (Figure 16). Without the addition of urea, a minimal amount of nitrous oxide was produced and no significant differences were observed with the addition of either inhibitor (Figure 16A and Figure 16C). Over this 10-day period and with urea addition, N2O production in these microcosms increased to a total of 172 ng N/g dry soil in comparison to 5 ng N/g of soil without any addition of urea. Only ATU significantly decreased N2O production when an N source was provided. The total nitrous oxide emissions did not change without addition of 77 urea. Total nitrous oxide emissions were unchanged with the addition of PTIO at both concentrations of urea (Figure 18). When 200 ng N/kg of soil of urea were added, 100 uM and 200 uM of ATU significantly decreased emissions compared to when no ATU was added to the sample (Figure 18). On Day 2, addition of 50, 100, and 200 uM of ATU significantly decreased the production of N2O. All tested concentrations of this inhibitor decreased N2O production in samples collected on Day 4. Nitrous oxide production on Day 4 was plotted against the concentration of this Bacterial inhibitor (Figure 17A) to determine the effect of inhibitor concentration on N2O emission. Increasing concentrations of ATU decreased N2O emissions in a linear fashion. With addition of 200 mg N/kg of soil, the highest emission of nitrous oxide was observed on Day 6 in the 10-day period (Figure 16B). One hundred micromolar ATU was selected for further experimentation. When PTIO and urea were added the highest emission of nitrous oxide was observed on Day 4 on average (Figure 16D). Although no significant differences were found with this inhibitor at any of the concentrations tested, an inhibitory trend was observed on day 6. Therefore, nitrous oxide production on Day 6 was plotted against the concentration of this Archaeal inhibitor (Figure 17B). At Day 6, there was a decrease with the addition of 100 uM and 200 uM PTIO concentration but no further decrease was observed with increasing concentrations of this inhibitor (Figure 17B). Therefore, 200 uM PTIO was selected for further experimentation. At Day 10, soil samples were collected to assay for nitrifying community abundance and the effect of inhibitors on abundance. No significant differences in the nitrifying 78 Bacterial and Archaeal abundances were observed with increasing inhibitor concentration (Figure 19). Since abundance of nitrifying communities did not significantly change within the 10-day period, the data were combined for all inhibitor concentrations. Using this approach there was a significant increase in the nitrifying Bacterial abundance with the addition of 200 mg N/kg of soil (p<0.0001) compared with no addition of N. No significant differences were observed in the nitrifying Archaeal abundance. 79 Figure 16: Effect of Bacterial inhibitor (ATU, Green) concentration, Archaeal inhibitor (PTIO, Blue) concentration and/or urea (200 mg N/kg of soil) addition on nitrous oxide emissions in an in vitro microcosm study over a period of 10 days on soil collected from in a grape vineyard at Summerland Research and Development Centre, Summerland, BC. Various concentrations of ATU; 10 uM (square), 50 uM (diamond), 100 uM (triangle), 200 uM (circle) and PTIO; 100 uM (square), 200 uM (diamond), 300 uM (triangle), 400 uM (circle), were tested and nano pure water was used as a control (small circle, Red). This experiment was performed in the dark. Asterisks indicate significance difference at p<0.05 from no inhibitor addition. 0102030402 4 6 8 10Net Nitrous Oxide (ng N/g dry soil/day)Day ATU with Urea addition10 uM50 uM100 uM200 uMNo inhibitor*******B0102030402 4 6 8 10Net Nitrous Oxide (ng N/g dry soil/ day)DayPTIO with Urea addition100 uM200 uM300 uM400 uMNo inhibitorD0102030402 4 6 8 10Net Nitrous Oxide (ng N/g dry soil/ day)DayPTIO without urea addition100 uM200 uM300 uM400 uMNo inhibitorC05101520253035402 4 6 8 10Net Nitrous Oxide (ng N/g dry soil/day)Day ATU without urea addition10 uM50 uM100 uM200 uMNo inhibitorA 80 Figure 17A: Effect of Bacterial inhibitor (ATU, N2O collected on day 4) concentration; and B: Archaeal inhibitor (PTIO, N2O collected on day 6) concentration with the addition of urea (200 mg N/kg of soil) on nitrous oxide emissions in an in vitro microcosm study on soil collected from in a grape vineyard at Summerland Research and Development Centre, Summerland, BC. Various concentrations of ATU (10, 50, 100, 200 uM) and PTIO (100, 200, 300, 400 uM) were tested and nano pure water was used as a control. This experiment was performed in the dark. The R2 value and the equation of each line is displayed on both graphs (B: the last two squares are excluded, since they didn’t decrease nitrous oxide further). The equation of line and R2 value for graph B only considers the first 3 PTIO concentrations (closed circles)y = -0.0889x + 28.477R² = 0.95694051015202530350 50 100 150 200 250Net Nitrous Oxide (ng N/ g dry soil/ day)Concentration of ATU (uM)Increasing ATU concentration Ay = -0.0575x + 30.908R² = 0.82278051015202530350 100 200 300 400Net Nitrous Oxide (ng N/ g dry soil/day)Concentration of PTIO (uM)Increasing PTIO concentrationB 81 Figure 18: Effect of Bacterial inhibitor (ATU) concentration, Archaeal inhibitor (PTIO) concentration and/or urea (200 mg N/kg of soil) addition on total nitrous oxide emissions in an in vitro microcosm study over a period of 10 days on soil collected from a grape vineyard at Summerland Research and Development Centre, Summerland, BC. Various concentrations of ATU (10, 50, 100, 200 uM) and PTIO (100, 200, 300, 400 uM) were tested and nano pure water was used as a control. This experiment was performed in the dark. Means followed by the same letters are not significantly different (p<0.05). The error bars represent the standard error of the mean of triplicate samples. aaa aa024681012141618200 100 200 300 400Nitrous Oxide (ng N/g of dry soil)PTIO Concentration (uM)Total Nitrous Oxide (no urea, PTIO)Baabcabc aba0204060801001201401601802000 100 200 300 400Nitrous Oxide (ng N/g of dry soil)PTIO Concentration (uM)Total Nitrous Oxide (urea added, PTIO)Daaa aa024681012141618200 10 50 100 200Nitrous Oxide (ng N/g of dry soil)ATU Concentration (uM)Total Nitrous Oxide (no urea , ATU )aabc abcc bc0204060801001201401601802000 10 50 100 200Nitrous Oxide (ng N/g of dry soil)ATU Concentration (uM)Total Nitrous Oxide (urea added, ATU)CA 82 Figure 19: Effect of Bacterial inhibitor (ATU) concentration, Archaeal inhibitor (PTIO) concentration and/or urea (200 mg N/kg of soil) addition on nitrifying Bacterial (B: ATU and D: PTIO) and nitrifying Archaeal (A: ATU, and C: PTIO) abundance in an in vitro microcosm study at the end of a 10-day experiment on soil collected from a grape vineyard at Summerland Research and Development Centre, Summerland, BC. This experiment was performed in the dark. Various concentrations of ATU (10, 50, 100, 200 uM) and PTIO (100, 200, 300, 400 uM) were tested and nano pure water was used as a control. No significant differences at p>0.05 were seen. 01020304050600 10 50 100 200copy # (x 10^5) /g of dry soilATU concentration (uM)Nitrifying Archaea (ATU)No addition of urea200 mg NA01020304050600 100 200 300 400copy # (x 10^5) /g of dry soilPTIO concentration (uM)Nitrifying Archaea (PTIO) no addition of Urea200 mg NC051015202530350 10 50 100 200copy # (x 10^6) /g of dry soilATU concentration (uM)Nitrifying Bacteria (ATU) no addition of Urea200 mg NB051015202530350 100 200 300 400copy # (x 10^6) / g of dry soilPTIO concentration (uM)Nitrifying Bacteria (PTIO) No addition of Urea200 mg ND 83 3.2.3 Experiment 3-Varying urea concentrations and soil depth with fixed concentrations of domain-specific inhibitors In experiment 3, the goal was to determine how nitrous oxide production varied with urea concentration when fixed concentrations of either or both the ATU and PTIO inhibitors were added. When 200 mg N/ kg of soil were applied as urea to these vineyard soils, there was minimal production of nitrous oxide in the 10-day experimental time (Figure 20). In soil from 0-15 cm soil depth, addition of 20 mg N/kg of soil and 100 mg N/kg of soil increased N2O production compared to when no N was added on days 4 and 6 (Figure 21A and C). These emissions were also significantly reduced with the addition of ATU inhibitor (Figure 21A). At day 8, microcosms with the addition of 100 mg N/kg of soil showed an increase in production of N2O compared to those without N addition, but the effect of inhibitor was minimal (Figure 21A and C). In soil collected from 15-30 cm soil depth, the addition of 20 mg N/kg of soil increased N2O production relative to that without N addition on day 2 and it continued to increase until Day 6 (Figure 21B and D). From day 6 to day 10, N2O production declined in these samples but was still significantly higher than that with no N addition on days 6 and 8 (Figure 21B and D). The ATU inhibitor significantly decreased N2O production on days 2 and 4 (Figure 21B). On day 8, the addition of both 20 and 100 mg N/kg of soil led to higher N2O emissions compared to no N addition and ATU inhibitor did not affect this production (Figure 21B). Microcosms with the addition of 100 mg N with or without inhibitor, still continued to produce N2O on day 10 (Figure 21B). In soil collected at 0-15 cm soil depth with addition of 20 and 100 mg N/kg of soil addition, a decrease in N2O emissions was observed with the addition of PTIO compared to 84 soil without PTIO on day 4 and for 20 mg N/kg of soil, also on day 6 (Figure 21C). At day 8 in samples containing 100 mg N/kg of soil N2O production continued to increase but PTIO did not significantly reduce the synthesis of this greenhouse gas (Figure 21C). All samples on day 10 were similar to when no N was added to the samples (Figure 21C). N2O production in deeper soils (15-30 cm) with the addition of 20 mg N/kg of soil was higher than without N addition on days 2, 4, 6 and 8 but about 4-fold less than that in surface soils (0-15 cm) (Figure 21). With the application of 100 mg N/kg of soil N2O production was higher than that with no addition of N on days 8 and 10 (Figure 21D). PTIO addition did not affect production of N2O for the duration of this experiment (Figure 21D). When both inhibitors were added at their respective concentrations to soil from 0-15 cm, N2O production was lower on days 4 and 6 in samples with 20 mg N/kg of soil applied (Figure 22A) than with no addition of inhibitor. When 100 mg N/kg of soil were added, the production of N2O continued to increase until day 10. The combination of both inhibitors significantly reduced this production at day 4 but for the remainder of the experiment there was no effect of the inhibitors (Figure 22A). In deeper soils (15-30cm), no inhibitor effect was observed, but N2O emissions were higher with 20 mg N/kg of soil on days 4, 6 and 8 than without N addition (Figure 22B). With the addition of 100 mg N/kg of soil the N2O production was higher than that without N addition on days 8 and 10 (Figure 22B). The abundance of both the nitrifying domains was assessed at the end of the experiment, but there were no differences that were significant with any of the treatments (Figure 23). Total nitrous oxide emissions over the 10-day experimental period increased with increasing concentration of urea in surface soils (0-15 cm) (Figure 20), but the nitrous oxide 85 production when 200 mg N/kg of soil (highest N concentration tested) was applied did not produce a significant amount of nitrous oxide (Figure 20). The 10-day experimental duration may not have been long enough to see any emissions from this treatment. A longer experimental time may be needed to determine the total emissions of nitrous oxide when 200 mg N/kg of soil of urea are applied. The nitrous oxide emissions at 15-30 cm soil depth did not vary with urea concentration. When 100 mg N/kg of soil were applied, production of nitrous oxide was considerably higher from soils collected from 0-15 cm than 15-30 cm (Figure 20). Figure 20: Effect of urea concentration (20, 100, or 200 mg N/kg of soil) and soil depth [0-15 cm or 15-30 cm on nitrous oxide emissions in an in vitro microcosm study over a period of 10 days on soil collected from a grape vineyard at Summerland Research and Development Centre, Summerland, BC. This experiment was performed in the dark. Means followed by the same letters are not significantly different (p>0.05). The error bars represent the standard error of the mean of triplicate samples.cbaccbcbcc01002003004005006007008000 20 100 200Nitrous Oxide (ng N/g dry soil)Urea Concentration (mg N/kg soil)Total Nitrous Oxide Emissions 0-15 cm15-30 cm 86 Figure 21: Effect of Bacterial inhibitor (100 uM, ATU, Green), Archaeal inhibitor (200 uM, PTIO, Blue) concentration, urea concentration (20, 100, or 200 mg N/kg of soil) and soil depth [0-15 cm (A: ATU, C:PTIO) or 15-30 cm (B: ATU, D: PTIO)] on nitrous oxide emissions in an in vitro microcosm study over a period of 10 days on soil collected from a grape vineyard at Summerland Research and Development Centre, Summerland, BC. This experiment was performed in the dark. When asterisks are present, it represents a significant difference at p<0.05 when compared to no inhibitor addition within each nitrogen concentration. When circles are present, it represents a significant difference at p<0.05 when compared to no nitrogen addition. 0204060801001201401602 4 6 8 10Change in Nitrous Oxide (ng N/g dry soil/day)DayATU (0-15 cm)0 mg N20 mg N100 mg N200 mg N0 mg N + ATU 20 mg N + ATU100 mg N+ ATU 200 mg N + ATU*A0204060801001201401602 4 6 8 10Change in Nitrous Oxide (ng N/g dry soil/day)DayPTIO (0-15 cm)0 mg N20 mg N100 mg N200 mg N0 mg N+ PTIO20 mg N+ PTIO100 mg N+ PTIO200 mg N+ PTIO***C05101520252 4 6 8 10Change in Nitrous Oxide (ng N/g dry soil/day)DayATU (15-30 cm)0 mg N20 mg N100 mg N200 mg N0 mg N + ATU 20 mg N + ATU100 mg N+ ATU 200 mg N + ATU********B05101520252 4 6 8 10Change in Nitrous Oxide (ng N/g dry soil/day)DayPTIO (15-30 cm)0 mg N20 mg N100 mg N200 mg N0 mg N+ PTIO20 mg N+ PTIO100 mg N+ PTIO200 mg N+ PTIO*****D*********** 87 Figure 22: Effect of Bacterial inhibitor (100 uM, ATU) and Archaeal inhibitor (200 uM, PTIO) concentration, urea concentration (20, 100, or 200 mg N/kg of soil) and soil depth [A:0-15 cm or B:15-30 cm] on nitrous oxide emissions in an in vitro microcosm study over a period of 10 days on soil collected from a grape vineyard at Summerland Research and Development Centre, Summerland, BC. This experiment was performed in the dark. When asterisks are present, it represents a significant difference at p<0.05 when compared to no inhibitor addition within each nitrogen concentration. When circles are present, it represents a significant difference at p<0.05 when compared to no nitrogen addition. 0204060801001201401602 4 6 8 10Change in Nitrous Oxide (g N/g dry soil/day)DayATU + PTIO (0-15 cm)0 mg N + ATU +PTIO20 mg N + ATU +PTIO100 ug N + ATU +PTIO200 mg N + ATU + PTIO0 mg N20 mg N100 mg N200 mg N** *A05101520252 4 6 8 10Change in Nitrous Oxide (g N/g dry soil/day)DayATU + PTIO (15-30 cm)0 mg N + ATU +PTIO20 mg N + ATU +PTIO100 ug N + ATU +PTIO200 mg N + ATU + PTIO0 mg N20 mg N100 mg N200 mg N*****B****** 88 Figure 23: Effect of Bacterial inhibitor (100 uM, ATU) and Archaeal inhibitor (200 uM, PTIO) concentration, urea concentration (20, 100, or 200 mg N/kg of soil) and soil depth (0-15 cm or 15-30 cm) on nitrifying Bacterial (B:0-15 cm or D:15-30 cm) and nitrifying Archaeal (A:0-15 cm or C:15-30 cm) abundance in an in vitro microcosm study at the end of 10 days on soil collected from a grape vineyard at Summerland Research and Development Centre, Summerland, BC. Various concentrations of urea (20, 100, or 200 mg N/kg of soil) were tested and nano pure water was used as a control. The error bars represent the standard error of the mean of triplicate samples. No significant differences at p<0.05 were seen. 051015202530350 20 100 200copy # (x10^6) / g of dry soilUrea Concentration (mg N)Nitrifying Archaea (0-15cm) No inhibitorBacterial inhibitorArchaeal inhibitorBoth inhibitorsA051015202530350 20 100 200copy # (x10^6) /g of dry soilUrea Concentration (mg N)Nitrifying Archaea (15-30cm) No inhibitorBacterial inhibitorArchaeal inhibitorBoth inhibitorsC051015202530350 20 100 200copy # (x10^6) / g of dry soilUrea Concentration (mg N)Nitrifying Bacteria (0-15cm) No inhibitorBacterial inhibitorArchaeal inhibitorBoth inhibitorsB051015202530350 20 100 200copy # (x10^6) /g of dry soilUrea Concentration (mg N)Nitrifying Bacteria (15-30cm) No inhibitorBacterial inhibitorArchaeal inhibitorBoth inhibitorsD 89 4 Discussion The field study and in vitro microcosm study will be discussed separately in this section of my thesis. Overall, the total Archaeal and Bacterial abundances decreased with soil depth. The nitrifying Bacterial abundance decreased with soil depth while the nitrifying Archaeal abundance increased with soil depth. The abundances are effects by many different factors including different nitrogen applications and presence of mulch. In the in vitro microcosm study nitrous oxide production increased when urea concentration increased, and specific Bacterial and Archaeal inhibitors decreased the nitrous oxide emissions. The study provides us with more insight on the major nitrous oxide producers in these woody perennial cropping systems. 4.1 Field Study Archaea have been detected in agricultural soil and contribute to the nitrifying community (Leininger et al. 2006, Zhang et al. 2012, Podolyan et al. 2014, Stempfhuber et al. 2015, Ouyang et al. 2017, Segal et al. 2017, Hink et al. 2017). They also were observed in the apple and grape plots in the present study. Since, 43% and 26% of total area planted fruits, berries and nuts in the Central Okanagan is apple and grape, respectively, these cropping systems were ideal to study the abundance of these populations and their potential contribution to N2O emissions (Statistics Canada 2015). This study evaluated the effect of mulch, nitrogen application and soil depth on the abundance of total Bacteria and Archaea and nitrifying Bacteria and Archaea in these two agricultural systems. 90 4.1.1 Effect of mulch and carbon concentration I hypothesized that the presence of bark mulch would increase total populations of Archaea and Bacteria. Decomposition of bark mulch increases the organic carbon content of the soil. Many members of these communities require an organic source of carbon to grow and proliferate, and higher organic carbon content of the soils contributes to a higher abundance of the total Bacterial community (Lammel et al. 2017, Ye et al. 2018). However, in both apple and grape plots mulch had no significant effect on the abundances of total Bacteria and Archaea. The bark mulch was applied every second year since the apple (2014, 2016) and grape (2011, 2013, 2015) plots were established. A positive Pearson correlation was observed with the percent carbon content of all the samples and the total abundances of Archaea and Bacteria. The total carbon was likely not due to the application of mulch but where a high percent carbon was found high abundance of Archaea and Bacteria were present in the apple orchard and grape vineyard (Lammel et al. 2017, Ye et al. 2018). In the grape plot, application of compost increased total percent carbon at three of the five sample times relative to fertigation with urea. Compost is a complex organic form of carbon and also contains many other nutrients (Li et al. 2012). Percent carbon in soil increased when compost rather than urea was applied but there were no significant differences in the total microbial populations (Figure 10 and Figure 12), likely due to the high variability in these samples. Nonetheless, a positive correlation between the percent carbon and total Archaeal and Bacterial abundances was observed. 91 I hypothesized that the nitrifying populations would decrease in the presence of mulch because mulch is known to increase water retention in soil, hence decreasing the availability of oxygen (Hannam et al. 2016b). As oxygen is the electron donor in the process of nitrification (Guo et al. 2013), decrease in oxygen concentration in the soil may decrease the ability of both AOA and AOB to oxidize ammonia into hydroxylamine. Since the apple plot was fertigated with calcium nitrate, rather than an ammonia-based fertilizer, there was no significant effect of any treatment on the abundances of nitrifying Archaea and Bacteria. The application of bark mulch decreased the amount of nitrate reaching the soil surface at most sampling times (Table 12). In the grape plot, either urea was fertigated or an organic fertilizer (compost) was applied. Urea breaks down into ammonia which is the substrate required for nitrification. Since the application of mulch may decrease oxygen availability in the soil and nitrification is an aerobic process, I hypothesized that the nitrifying populations would be lower in soils to which mulch was applied. Nitrifying Bacterial abundance was lower in soils when mulch was applied at two sampling times (Figure 13). Since the nitrate-nitrogen concentration was higher in the grape plot when mulch was not applied, this suggests that nitrification is occurring in these soils and the major nitrifiers are Bacteria not Archaea (Table 20). Mulch had no significant effect on the nitrifying Archaeal abundance (Figure 14). I conclude that mulch application may lower the nitrification potential which may further lead to a decrease in the nitrous oxide emissions from this pathway. The nitrifying populations are autotrophs, meaning that they reduce CO2 to make organic compounds for biosynthesis (Sarwee et al. 2016). The decomposition of mulch would provide an increase in organic carbon, if the proper weathering for complete degradation of the bark mulch were present. Since the nitrifying populations do not require 92 an organic source of carbon, mulch decomposition would not affect the nitrifying communities directly. The total Archaeal and Bacterial populations were positively correlated with percent carbon of the soil. Soil with higher percent carbon content would have high microbial populations which may contribute to competition for nutrients other than carbon, and for space. In this study I observed a negative correlation between percent carbon and nitrifying Archaeal and Bacterial abundance, suggesting that competition for nutrients other than carbon might inhibit growth of the nitrifying populations. 4.1.2 Effect of nitrogen source and concentration I hypothesized that the AOA and AOB populations would not be affected by Ca(NO3)2 concentration in the apple orchard and my results supported this hypothesis. Hatzenpichler (2012) states that the principal factor controlling the ratio between AOA and AOB is substrate concentration. As Ca(NO3)2 was the nitrogen source for the apple plot and both AOA and AOB use NH4+ as a substrate for nitrification, the concentration of NO3- would not be expected to affect the abundance or nitrification rates in this plot. The apple plot had a higher concentration of nitrate when 40 g N/tree was applied rather than 20 g N/tree. In the grape plot 15 g N/vine was applied as either compost or urea via fertigation. I hypothesized that nitrifying Bacteria would dominate when a mineral fertilizer, urea, was applied and nitrifying Archaea would dominate when an organic fertilizer, compost, was applied. Organic matter mineralization produces low, steady levels of ammonia, whereas application of mineral ammonia-based nitrogen fertilizer increases soil ammonia 93 concentrations immediately after application (Stopnisek et al. 2010). At four of five sampling times, the ammonia concentration did not differ with application of compost or urea. The application of mineral nitrogen fertilizer, urea, led to a higher nitrate content in June 2015 than when compost was applied. The grape plot is fertigated with urea from early May to late June and the samples were collected approximately a week before the end of fertigation. Increases in nitrate concentration would suggest that the urea has been broken down into ammonia and further nitrified into nitrate. June is the end of the fertigation period unlike August and September, which is later in the season and potentially the ammonia had already been nitrified. This suggests that compost may be slowly broken down into NH4+ and a steady level of ammonia maintained in these soils, but when fertigated with urea the NH4+ is nitrified quickly into NO3-. In surface soils, an increase in the Bacterial amoA copy number was seen in the fertigated treatments at two sampling times, suggesting that nitrifying Bacteria may be principal contributors to nitrification in these treatments and may be stimulated by a high ammonia environment. In this study, the lower abundance of Bacterial amoA at two sampling times when a more complex nitrogen source was applied, may suggest that Archaea are the primary contributors to nitrification in soils to which compost was applied (Figure 13). A negative correlation was observed between Archaeal amoA and ammonium-nitrogen suggesting that AOA may be adapted to growth in low ammonium conditions, as was observed by Di et al. (2010) and Verhamme et al. (2011). Verhamme et al. (2011) showed that the AOA:AOB ratio was higher in soil ecosystems with low-to-intermediate ammonium concentration, and growth of AOB was favoured at high ammonia concentration (Verhamme et al. 2011). 94 Additionally, Tourna et al. (2010) suggested that substrate inhibition of Archaeal nitrification may occur at high concentrations of ammonia. AOA have exceptionally high affinities and low thresholds to their substrate compared to AOB (Martens-Habbena et al. 2009, Hatzenpichler 2012). My hypothesis, that an increase in AOA abundance would be observed in organic N-amended soils, was not supported. Stopnisek et al. (2010) reported that AOA did not respond to the addition of nitrogen supplementation with ammonium sulfate or urine to soil, whereas AOB increased in abundance after addition of ammonium sulfate. It has been reported that in four different soil types, 16S rRNA abundance of AOA was higher in plots where organic fertilizers were added than in plots with chemical fertilizer addition (Offre et al. 2009).Zhou et al. (2015) also found a significant increase in Archaeal amoA genes when soils were amended with organic N and Bacterial amoA abundance increased with addition of inorganic N. Further studies on the soils from SuRDC are needed to determine whether the soil conditions or the amount of nitrogen being applied was optimal for Archaea to nitrify. Application of urea provides the microbial populations of the soil with a readily available source of ammonia and the concentration at which it is applied may select for nitrifying Bacteria. While we now know that nitrifying Archaea are present in these soils, we don’t know if they express the AMO gene for nitrification. Thus, further studies are required to analyze RNA for evidence of transcription of amoA, as the lower numbers of AOA at the higher nitrogen levels do not necessarily indicate their activity in the soil. 95 4.1.3 Effect of soil depth Depth had a significant effect on the total numbers of Archaea and Bacteria. As soil depth increased the abundance of total Archaea and Bacteria decreased in both apple and grape plots. Both percent soil nitrogen and carbon decreased with increasing depth leading to a positive correlation of total Archaeal and Bacterial abundance with soil nitrogen and carbon (Table 14 and Table 22). Most species of Bacteria and Archaea require a source of organic carbon in the soil to grow. Further, all Bacterial and Archaeal populations require nitrogen for cellular metabolism and structural components. Data from this study suggest that the decrease in total abundance of Archaea and Bacteria with depth may be related to a decrease in the concentrations of soil total % carbon in both the apple and grape plots at most sampling times. In contrast, in the grape plot the abundance of nitrifying Archaea increased with soil depth while ammonium nitrogen concentrations decreased, which supported my hypothesis that the nitrifying Archaeal abundance would increase with soil depth. On the other hand, there was no effect on nitrifying Bacterial abundance, which did not support my hypothesis that abundance of AOB would decrease. The decrease in NH4-N with depth could lead to increases in AOA abundance according to Tourna et al. (2008). In the grape plot at 0-10 cm the AOB were the predominant nitrifying population, at 10-20 cm AOB and AOA were present in approximately equal proportion, while at 20-30 cm AOA were about 5-fold more abundant than AOB. Thus, the ratio of AOA to AOB tended to increase as depth increased. This suggests that AOA may make a greater contribution to nitrification as soil depth increases. Di et al. (2010) reported that Bacterial amoA genes declined significantly with depth, while amount of Archaeal amoA genes remained high. Consequently, nitrification 96 rates were higher in topsoil than in the subsoils (Di et al. 2010). Mushinski et al. (2017) also reported a similar result in forest soils. With a decrease in ammonium and nitrate content within the first metre of soil, the AOA to AOB ratio increased with increasing soil depth and the community structure was altered (Mushinski et al. 2017). It seems likely that species of Archaea that have a higher affinity for ammonium may reside lower in the soil profile. Further community structure analysis, for example metagenome analysis on 16SrRNA sequencing reads, would be required to determine if there are changes in the nitrifying Archaeal and Bacterial species with changes in the soil depth in the apple and grape plots. 4.2 In Vitro Microcosm Experiments This study evaluated the effect of urea concentration, soil depth and nitrifying inhibitor addition on the abundance of nitrifying Bacteria and Archaea and nitrous oxide emissions in an in vitro soil microcosm experiment. 4.2.1 Effect of increasing urea concentration I hypothesized that increasing urea concentration would increase nitrous oxide production from this vineyard soil. In my study, nitrous oxide production increased with urea concentration but there were no differences in the abundance of nitrifying Archaea at the end of the experiment (Figure 15 and Figure 20). Urea can be broken down into two molecules of ammonia in wet soils, increasing the substrate for nitrification and potentially leading to increases in nitrous oxide production (Venterea et al. 2010, Heil et al. 2016). Recent studies have indicated AOA are oligotrophs, able to survive in low nutrient environments (Tourna et 97 al. 2011, Hatzenpichler 2012, Mushinski et al. 2017). A good example of this was seen in the first discovered soil cultivar Nitrososphaera viennensis EN76, which grew considerably better when provided with a small amount of pyruvate and low concentrations of urea than with high concentrations of urea and when no pyruvate was added (Tourna et al. 2011). This suggests that the AOA population, unlike the AOB, are adapted to low nutrient environmental conditions and also are more versatile in their metabolism than previously thought (Leininger et al. 2006, Mushinski et al. 2017). This may indicate that the urea application in this study might need to be lower to see an immediate nitrous oxide production by the AOA population. The duration of this experiment might not have been long enough to see any production of nitrous oxide when 200 mg N/ kg of soil were applied or this may have been an experimental error since nitrous oxide production of both 200 mg N/kg and no urea were very similar. The nitrifying species may have a lag period before they produce nitrous oxide and this lag period may be proportional to the urea concentration in the soil. In another recent study, the application of urea over 4 years in grasslands did not affect the abundance or community composition of nitrifying Archaea (Xiang et al. 2017). Xiang et al. (2017) suggested that, since urea quickly breaks down into ammonia via the enzyme urease and AOB are faster to respond to urea addition, AOA might not be as important as AOB in regulating the first step of nitrification. In experiment one, as there was no effect of inhibitor on the abundance of the nitrifying populations, the data for all inhibitor concentrations were combined, increasing the sample size. Using this approach, the nitrifying Bacterial population was higher in the presence of higher urea concentration than the control without inhibitor after incubation for 14 days. Xiang et al. (2017) also observed an increase in AOB abundance and a shift in the 98 community composition of AOB but not AOA in response to urea application. As the concentration of urea increased there was a shift from AOA to AOB dominance (Xiang et al. 2017). My study also reported a correlation between the concentration of NO3- and AOB abundance, indicating that nitrification by AOB was likely occurring in these soils. Xiang et al. (2017) concluded that Bacteria were the dominant domain oxidizing ammonia into nitrous oxide in urea-fertilized soils. In my study, since AOB abundance was increased in urea-treated soil and AOA abundance was not altered, nitrous oxide was likely produced by nitrifying Bacteria rather than nitrifying Archaea in these soils. 4.2.2 Effect of soil depth Before the start of the experiment the nitrifying Bacterial population did not vary with soil depth but the nitrifying Archaeal population was higher at 15-30 cm depth than at 0-15 cm soil depth (data not shown). No effect of the treatments on AOA and AOB abundances was observed, but when 100 mg N/kg of soil was applied six times as much nitrous oxide was produced from surface soils (0-15 cm) than from deeper soils (15-30 cm), which supported my hypothesis that surface soils will produce more N2O than deeper soils (Figure 17 and Figure 20). Since all fertilizers were applied on the surface, we assumed that surface soils would have a higher concentration of most nutrients which diffuse as soil depth increases. This may lead to different species predominating in these soils and differing nitrification potential of those species. In this study, the community composition and differing ratio of AOA:AOB at 0-15 and 15-30 cm soil depths may contribute to the differences observed in nitrous oxide production. Species at 0-15 cm may be more adapted to higher N concentrations and, therefore, may be faster at converting urea to nitrous oxide than species at 15-30 cm soil depth. Species thriving at 15-30 cm may be more adapted to low 99 nitrogen availability; therefore, the production rate may be lower than that for species at 0-15 cm soil depth. Other factors that may affect the nitrous oxide production are the root concentration, nutrient availability and other living creatures that directly or indirectly affect the biotic factors in the soil. Xiang et al. (2017) found that the nitrifying Bacterial community structure differed significantly with nutrient availability. Community structure refers to the association of one or more species sharing the same geographical area at one specific time (Bomberg and Timonen 2009). Nitrifying Archaea have been shown to be more resilient to environmental and nutrient changes; therefore, the community composition of nitrifying Archaea does not differ as much with nutrient concentration as that of the nitrifying Bacterial communities (Mushinski et al. 2017). Soils with high N concentration may harbour Bacterial species that are adapted to high concentrations of N and have low affinities for the substrate (Mushinski et al. 2017). On the other hand, soils with low N concentration may harbour Bacterial and Archaeal species adapted to low concentrations with higher affinities for the substrate (Mushinski et al. 2017). Seasonal changes contribute to differences in nutrient availability and, therefore, significant changes primarily in the AOB community rather than the AOA community (Pereira e Silva et al. 2012, Mushinski et al. 2017). Additionally, Mushinski et al. (2017) observed a significant increase in the ratio of AOA:AOB with soil depth (0-100 cm). Jia and Conrad (2009) suggested that when ammonia (breakdown product of urea and inorganic N sources), rather than an organic source, is applied as a substrate, ammonia oxidation is mainly carried out by nitrifying Bacteria rather than nitrifying Archaea. My study suggests Bacteria may be more competitive at using urea as a nitrogen source when compared with Archaea and produce nitrous oxide at a significantly faster rate 100 than Archaea. According to my field study, the ratio of AOA: AOB increased with soil depth. Since urea was the nitrogen source in this experiment and nitrifying Archaea were more abundant in deeper soils than nitrifying Bacteria, this may explain the lower nitrous oxide production at 15-30 cm soil depth compared to 0-15 cm soil depth. 4.2.3 Effect of increasing inhibitor concentration Over a 10-day period the nitrifying Bacterial inhibitor, ATU significantly reduced nitrous oxide emissions in the first 4 days of the experiment. When testing the appropriate ATU concentration range, increasing ATU concentrations decreased nitrous oxide emissions in a linear fashion. Ali et al. (2013) used this inhibitor on activated sludge and saw a significant decrease in the emissions as early as 4 hours after 86 nM ATU was applied. Results from my study suggest that after four to six days the inhibitor may have degraded or become adsorbed to soil particles and was ineffective in inhibiting nitrous oxide production by nitrifying Bacteria. ATU is a copper chelator and copper is an essential component of ammonia oxidation by Bacterial amoA; therefore, the higher the concentration of ATU, the less copper available to convert ammonia into hydroxylamine leading to a decrease in nitrous oxide production. This inhibitor did not affect nitrifying Archaea at the concentration used in this experiment, since Archaeal amoA and Bacterial amoA enzymes only share common functionality and not their primary protein sequence. In this study ATU decreased nitrous oxide production in soils collected from a vineyard and addition of this inhibitor did not affect the abundance of either AOB or AOA 10 days following application of ATU. If soil samples were taken simultaneously with each gas sample and DNA extracted, we may have observed a difference in the abundances of the nitrifying species. 101 In this study, PTIO decreased nitrous oxide production in soils collected from a vineyard and addition of this inhibitor did not affect the abundance of either AOB or AOA 10 days following application of PTIO. Martens-Habbena et al. (2015) showed nearly complete inhibition (>95% inhibition) of ammonia oxidation in two pure cultures of AOA (isolates HCA1 and SCM1) in the presence of PTIO and a direct link between inhibition by PTIO and scavenging of NO produced by Archaeal ammonia oxidation. This suggests that NO is an essential part of AOA metabolism (Martens-Habbena et al. 2015). Since NO has a short life-span and is replaced in cells very quickly, this may lead to low efficacy of this inhibitor. The studies to date have only tested this Archaeal inhibitor on pure cultures of both nitrifying Archaea and Bacteria. In soil, PTIO may be absorbed by the soil particles and have a lower effectiveness than if applied at the same concentration in a culture-based medium. Studies have not been conducted regarding the potential of these inhibitors to affect nitrification in soil and their long-term effects on the nitrifying populations. I hypothesized that with high urea concentration nitrifying Bacteria would contribute to the majority of the nitrous oxide emissions rather than nitrifying Archaea, and at low urea concentrations nitrifying Archaea would be the major contributors to nitrous oxide emissions. In surface soils, until Day 4 both Bacterial and Archaeal inhibitors decreased the nitrous oxide emissions equally compared to when no inhibitor was applied. At Day 6, the Archaeal inhibitor did not show any inhibition when a high amount of urea was applied. In microcosms with a low concentration of applied urea, nitrous oxide production was reduced with the Archaeal inhibitor. This suggests that when a high concentration of urea was applied more ammonia is used by nitrifying Bacteria to produce nitrous oxide, but when the 102 concentration of urea applied was low, both nitrifying Archaea and Bacteria may contribute to nitrous oxide production equally. This study also looked at the addition of both inhibitors at the same concentration and the effect of this treatment on nitrous oxide emissions. The addition of both inhibitors did not reduce the nitrous oxide production further from when they were added alone. The interactions between these two chemicals is unknown and need further analysis. The abundance of both populations was not affected when both inhibitors were added simultaneously to the soil. No studies have looked at the combination of both these inhibitors and their effects on nitrifying populations. Further studies, including enzyme kinetics or radiolabeling, are required at the protein level to see the effect and the interaction of these inhibitors in the same solution. 4.3 Relationship Between the Field and the In-Vitro Microcosm Study The soil used in the microcosm experiment was obtained from the grape vineyard fertigated with urea. Therefore, urea was applied as an additional source of N in the in vitro experiments. In this microcosm study as urea concentration increased, N2O emissions also increased. This suggests that if the concentration of nitrogen applied is increased in the field, N2O emissions would also increase. In the microcosms, N2O emissions were higher in surface soil than in deeper soils and in the surface field samples, abundance of nitrifying bacteria was 3 times higher than that of nitrifying Archaea. Thus, nitrifying Bacteria may produce the majority of the N2O emissions in surface soils. In the field, the nitrifying Archaeal abundance increased with increasing soil 103 depth and it was positively correlated with the N2O emissions collected at the same time. Hence, nitrifying Archaea may produce the majority of the N2O emissions in deeper soils. This difference may be due to nutrient availability. Nitrifying Bacteria may be adapted to higher N concentrations and, therefore, may be more competitive at converting urea to nitrous oxide than nitrifying Archaea in surface soils. Nitrifying Archaea present at 15-30 cm may be better adapted to low nitrogen availability; therefore, Archaeal nitrifiers may have a much higher affinity for ammonia than Bacterial nitrifiers and be more competitive at these lower depths. Overall, both domains contribute to N2O emissions. In the efforts to reduce greenhouse gases, all studies should assess both nitrifying Bacteria and Archaea. Using the results obtained in this study, I can conclude that both Nitrifying Archaea and Bacteria contribute to nitrous oxide emissions and both populations should be verified that they can reduce nitrous oxide emissions. Nitrification inhibitors, such as ATU and PTIO, may be used to reduce the negative effects of fertilizer application on the environment, but still not sacrifice the positive attributes of fertilizer application. Both inhibitors applied reduced N2O production in the microcosms and did not affect the abundance of both species, but their efficacy in the field setting and their effect on the environment is unknown. Further studies with these inhibitors are required to assess their potential in a large-scale field setting. 104 5 Conclusions This study detected the presence of Archaea in two woody perennial cropping systems. The ratios of total Archaeal and Bacterial abundances were in between 1:4 and 1:10, depending on the sampling day and the crop. This thesis examined the Archaeal and Bacterial abundances in two studies: a field and an in vitro microcosm study. The first objective of the field study was to evaluate the effect of mulch on the abundance of total and nitrifying abundances of Archaea and Bacteria. There was no effect on the abundance of the total populations due to mulch application, but the nitrifying Bacterial abundance was higher in the absence of mulch at two of five sampling times. In soils without mulch, nitrate-nitrogen concentration was higher, suggesting that nitrification might be occurring, and nitrifying Bacteria rather than Archaea may be the major nitrifiers. In the second and third objectives the effect of nitrogen concentration and nitrogen source, either as compost (organic) or urea (inorganic), on the abundances of total and nitrifying Archaea and Bacteria were examined. Nitrogen concentration had no effect on the total Archaea and Bacteria. The nitrogen source that was used in the apple plot was calcium nitrate, which is not a substrate for nitrification; therefore, no effect of nitrogen concentration was observed on the abundances of nitrifying Archaea and Bacteria. Compost application in the grape plot increased carbon content of the soil at most sampling times, and total Archaeal and Bacterial abundances and percent carbon were positively correlated. A lower abundance of nitrifying Bacteria when compost was applied suggests that Archaea may be the primary contributors to nitrification in soils to which an organic N source is applied. 105 For the fourth objective I investigated the effect of soil depth on the abundance of total and nitrifying Archaea and Bacteria. Total Archaeal and Bacterial abundances decreased with increasing soil depth. The abundance of nitrifying Archaea increased in deeper soils and ammonium-nitrogen concentration decreased, suggesting that nitrifying Archaea may make a greater contribution to nitrification as soil depth increases. The ratio of nitrifying Archaea to Bacteria tended to increase as depth increased, consequently the ability of nitrifying Archaea to produce nitrous oxide in deeper soils is greater than that of nitrifying Bacteria. The first objective of the in vitro microcosm study was to determine the effect of urea concentration on nitrous oxide production and abundance of nitrifying Archaea and Bacteria. The abundance of nitrifying Archaea was unchanged and abundance of nitrifying Bacteria increased with increasing concentration of N. This study suggests that urea application stimulated the nitrifying Bacteria rather than Archaea. Therefore, Bacteria may contribute more to nitrous oxide emissions than Archaea in these conditions. For the second objective I examined the effect of soil depth on nitrous oxide production and the abundance of nitrifying Archaea and Bacteria. Four times as much nitrous oxide was produced from surface soils than from deeper soils. Soil depth had no effect on the abundance of nitrifying Archaea and Bacteria. The application of 200 mg N as urea in these soils is higher than would be applied in the vineyard. The species of Archaea and Bacteria at the surface may be better adapted to higher N concentrations and species at lower depths may be better adapted to lower N concentrations. 106 For the third objective of this study I examined the effect of Bacterial nitrification inhibitor, ATU and Archaeal nitrification inhibitor, PTIO. Increasing ATU concentrations decreased nitrous oxide emissions in a linear fashion and ATU significantly reduced nitrous oxide emissions until the eighth day of the experiment. PTIO also decreased nitrous oxide production and neither inhibitor had any effect on the abundance of either nitrifying Bacteria or Archaea 10 days following application. In microcosms with a low concentration of applied urea, nitrous oxide production was reduced with the Archaeal inhibitor. This suggests that when a high concentration of urea was applied nitrifying Bacteria were the dominant group producing nitrous oxide, but when the concentration of urea applied was low, both nitrifying Archaea and Bacteria may have contributed to nitrous oxide production. More experimentation is required to determine the estimated potential of Archaea to produce N2O under varying conditions and to analyse the use of these nitrification inhibitors and their potential for commercial application to reduce nitrous oxide emissions. 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Hannam unpublished 2015) 1A 1B 1C 1D 1E 1F 1G 1H 1I 1J 2A 2B 2C 2D 2E 2F 2G 2H 2I 2J 3A 3B 3C 3D 3E 3F 3G 3H 3I 3J 4A 4B 4C 4D 4E 4F 4G 4H 4I 4J1T 1S 1R 1Q 1P 1O 1N 1M 1L 1K 2T 2S 2R 2Q 2P 2O 2N 2M 2L 2K 3T 3S 3R 3Q 3P 3O 3N 3M 3L 3K 4T 4S 4R 4Q 4P 4O 4N 4M 4L 4K24A 24B 24C 24D 24E 24F 24G 24H 24I 24J 23A 23B 23C 23D 23E 23F 23G 23H 23I 23J 22A 22B 22C 22D 22E 22F 22G 22H 22I 22J 21A 21B 21C 21D 21E 21F 21G 21H 21I 21J24T 24S 24R 24Q 24P 24O 24N 24M 24L 24K 23T 23S 23R 23Q 23P 23O 23N 23M 23L 23K 22T 22S 22R 22Q 22P 22O 22N 22M 22L 22K 21T 21S 21R 21Q 21P 21O 21N 21M 21L 21K25A 25B 25C 25D 25E 25F 25G 25H 25I 25J 26A 26B 26C 26D 26E 26F 26G 26H 26I 26J 27A 27B 27C 27D 27E 27F 27G 27H 27I 27J 28A 28B 28C 28D 28E 28F 28G 28H 28I 28J25T 25S 25R 25Q 25P 25O 25N 25M 25L 25K 26T 26S 26R 26Q 26P 26O 26N 26M 26L 26K 27T 27S 27R 27Q 27P 27O 27N 27M 27L 27K 28T 28S 28R 28Q 28P 28O 28N 28M 28L 28K48A 48B 48C 48D 48E 48F 48G 48H 48I 48J 47A 47B 47C 47D 47E 47F 47G 47H 47I 47J 46A 46B 46C 46D 46E 46F 46G 46H 46I 46J 45A 45B 45C 45D 45E 45F 45G 45H 45I 45J48T 48S 48R 48Q 48P 48O 48N 48M 48L 48K 47T 47S 47R 47Q 47P 47O 47N 47M 47L 47K 46T 46S 46R 46Q 46P 46O 46N 46M 46L 46K 45T 45S 45R 45Q 45P 45O 45N 45M 45L 45K49A 49B 49C 49D 49E 49F 49G 49H 49I 49J 50A 50B 50C 50D 50E 50F 50G 50H 50I 50J 51A 51B 51C 51D 51E 51F 51G 51H 51I 51J 52A 52B 52C 52D 52E 52F 52G 52H 52I 52J49T 49S 49R 49Q 49P 49O 49N 49M 49L 49K 50T 50S 50R 50Q 50P 50O 50N 50M 50L 50K 51T 51S 51R 51Q 51P 51O 51N 51M 51L 51K 52T 52S 52R 52Q 52P 52O 52N 52M 52L 52K72A 72B 72C 72D 72E 72F 72G 72H 72I 72J 71A 71B 71C 71D 71E 71F 71G 71H 71I 71J 70A 70B 70C 70D 70E 70F 70G 70H 70I 70J 69A 69B 69C 69D 69E 69F 69G 69H 69I 69J72T 72S 72R 72Q 72P 72O 72N 72M 72L 72K 71T 71S 71R 71Q 71P 71O 71N 71M 71L 71K 70T 70S 70R 70Q 70P 70O 70N 70M 70L 70K 69T 69S 69R 69Q 69P 69O 69N 69M 69L 69KIrrig + N Irrig N mulch 100% 2x day 20g N/tree none100% every 2 days 20g N/tree black plastic100% 2x day 40gN/tree bark mulch100% every 2 days 40gN/treeRep 5Rep 6Guard rowWEST SIDEEAST SIDEGuard rowRep 1Rep 2Rep 3Rep 4G 1 3 G2GGGGGGGGGG GG G G12111098765469 68 67 66 65 64 63 62 6172 71 70GGGGG G G G76 77 78 79 80 81 82 83 8473 74 75G 144 142 G143 GGGGG G133134135136137138139140141G 145 147 G146 GGGGG G156155154153152151150149148GGGGG G G G213 212 211 210 209 208 207 206 205216 215 214G G G GG GGGGG GGGGGGGGGGG G G GG GGGGG GGGGGGGGGG 120 Figure A 2: Grape plot treatment layout (K. Hannam unpublished 2015)21T 21S 21R 21Q 21P 21O 21N 21M 21L 21K 22T 22S 22R 22Q 22P 22O 22N 22M 22L 22K 23T 23S 23R 23Q 23P 23O 23N 23M 23L 23K30A 30B 30C 30D 30E 30F 30G 30H 30I 30J 29A 29B 29C 29D 29E 29F 29G 29H 29I 29J 28A 28B 28C 28D 28E 28F 28G 28H 28I 28J30T 30S 30R 30Q 30P 30O 30N 30M 30L 30K 29T 29S 29R 29Q 29P 29O 29N 29M 29L 29K 28T 28S 28R 28Q 28P 28O 28N 28M 28L 28K31A 31B 31C 31D 31E 31F 31G 31H 31I 31J 32A 32B 32C 32D 32E 32F 32G 32H 32I 32J 33A 33B 33C 33D 33E 33F 33G 33H 33I 33J31T 31S 31R 31Q 31P 31O 31N 31M 31L 31K 32T 32S 32R 32Q 32P 32O 32N 32M 32L 32K 33T 33S 33R 33Q 33P 33O 33N 33M 33L 33K40A 40B 40C 40D 40E 40F 40G 40H 40I 40J 39A 39B 39C 39D 39E 39F 39G 39H 39I 39J 38A 38B 38C 38D 38E 38F 38G 38H 38I 38J40T 40S 40R 40Q 40P 40O 40N 40M 40L 40K 39T 39S 39R 39Q 39P 39O 39N 39M 39L 39K 38T 38S 38R 38Q 38P 38O 38N 38M 38L 38K41A 41B 41C 41D 41E 41F 41G 41H 41I 41J 42A 42B 42C 42D 42E 42F 42G 42H 42I 42J 43A 43B 43C 43D 43E 43F 43G 43H 43I 43J41T 41S 41R 41Q 41P 41O 41N 41M 41L 41K 42T 42S 42R 42Q 42P 42O 42N 42M 42L 42K 43T 43S 43R 43Q 43P 43O 43N 43M 43L 43K50A 50B 50C 50D 50E 50F 50G 50H 50I 50J 49A 49B 49C 49D 49E 49F 49G 49H 49I 49J 48A 48B 48C 48D 48E 48F 48G 48H 48I 48J50T 50S 50R 50Q 50P 50O 50N 50M 50L 50K 49T 49S 49R 49Q 49P 49O 49N 49M 49L 49K 48T 48S 48R 48Q 48P 48O 48N 48M 48L 48K51A 51B 51C 51D 51E 51F 51G 51H 51I 51J 52A 52B 52C 52D 52E 52F 52G 52H 52I 52J 53A 53B 53C 53D 53E 53F 53G 53H 53I 53J51T 51S 51R 51Q 51P 51O 51N 51M 51L 51K 52T 52S 52R 52Q 52P 52O 52N 52M 52L 52K 53T 53S 53R 53Q 53P 53O 53N 53M 53L 53K60A 60B 60C 60D 60E 60F 60G 60H 60I 60J 59A 59B 59C 59D 59E 59F 59G 59H 59I 59J 58A 58B 58C 58D 58E 58F 58G 58H 58I 58J60T 60S 60R 60Q 60P 60O 60N 60M 60L 60K 59T 59S 59R 59Q 59P 59O 59N 59M 59L 59K 58T 58S 58R 58Q 58P 58O 58N 58M 58L 58Kg16A g16B g16C g16D g16E g16F g15A g15B g15C g15D g15E g15F g14A g14B g14C g14D g14E g14F g13A g13B g13C g13D g13E g13F g12A g12B g12C g12D g12E g12Fg16T g16S g16R g16Q g16P g16O g15T g15S g15R g15Q g15P g15O g14T g14S g14R g14Q g14P g14O g13T g13S g13R g13Q g13P g13O g12T g12S g12R g12Q g12P g12OColourColour Compost NMicrosprinkler, 4 times daily N fertigationDripper, 4 times dailyNPKB fertigationCompost N + mulchWEST SIDERow 10Row 5Row 11Row 12N fertigation + mulchRow 6Row 7Row 8Row 9Guard RowGGGGG G87 86 85 84 83 8290 89 88GGGGG G94 95 96 97 98 9991 92 93G 120 118 G119 GGGG 112113114115116117GGGGG G124 125 126 127 128 129121 122 123G 150 148 G149 GGGG 142143144145146147GGGGG G154 155 156 157 158 159151 152 153G 180 178 G179 GGGG 172173174175176177G G G GG GGGG GGGGGG 121 Appendix B: Creation of Standard Curves with Plasmid DNA Templates Step 1 The mass of a single plasmid molecule was calculated by inserting the plasmid size value into the formula below m = [n][1. 096e-21 g/ bp] (where: n = plasmid size in bp 3929 topo cloning + PCR Insert (primers nosZ1F/nosZ1R= 259 bp) m = mass, e-21 = ×10-21 m= [3929+259][1. 096e-21 g/ bp] m= [4188] [1. 096e-21 g/ bp] m= 4590.048 e-21 g/ bp m= 4.59 e-18 g/ bp Step 2 The mass of plasmid containing the copy #s of interest that are 1000,000 to 10 copies was determined as follows: Copy # of interest × mass of single plasmid = mass of plasmid DNA needed 1 000 000 X 4.59 e-18 g/ bp= 4.59 e-12 g/ bp The following table presents the calculated plasmid masses needed to achieve the copy numbers of interest. Copy # Mass of plasmid DNA (g) 1 000,000 4.59e-18 g/ bp 4.59 e-12 100,000 4.59 e-13 10,000 4.59 e-14 1000 4.59 e-15 100 4.59 e-16 10 4.59 e-17 Step 3 122 The concentrations of plasmid DNA needed to achieve the copy #s of interest was calculated by dividing the mass needed (calculated in Step 2) by the volume pipetted into each reaction. In this example, 5μL of plasmid DNA solution was pipetted into each PCR reaction. The concentration of gDNA needed to achieve the required masses of gDNA was calculated Copy # Mass of plasmid DNA needed (g) ÷ 5μL Final concentration of plasmid DNA (g/μL) 1 000,000 4.59 e-12 9.18 e-13* used in step 4 calculation 100,000 4.59 e-13 9.18 e-14 10,000 4.59 e-14 9.18 e-15 1000 4.59 e-15 9.18 e-16 100 4.59 e-16 9.18 e-17 10 4.59 e-17 9.18 e-18 Step 4 A serial dilution of the plasmid DNA was prepared. Cloned sequences are highly concentrated in purified plasmid DNA stocks. A series of serial dilutions was performed to achieve a working stock of plasmid DNA for quantitative PCR applications. The table shows that the first 3 dilutions were prepared so that the plasmid would be at a workable concentration, that is 2e-12 grams/μL or 1.32e5 copies/μL. Once the plasmid is at a workable concentration, the following formula was used to calculate the volume needed to prepare the 1000,000 copy standard dilution (Dilution #3). C1V1 = C2V2 Dilution #4 (see table below for C1 ,C2,V1and V2 values) Nano drop reading 30 ng/μL= 30 e-9 grams/μLOr 3 e-8 grams/μL(1g=1-e-9 ng) [3 e-12 g /μL] [V1]= [9.18 e-13g/μL ] [100 μL] V1= [0.918 e-12g/μL ] [100 μL] / [3 e-12 g /μL] V1 = 30.6 μL Volume of diluent = 100 μL – 30.6 μL = 69.4 μL To achieve the final volume of 100 μL, 30.6 μL of stock plasmid PCR product was added to 69.4μL of diluent ie H2O 123 Note: The diluent can be sterile 1X TE (1mM Tris, 0.1mM EDTA, pH8.0) or sterile, nuclease-free H2O Dilutions 3 to 7 were calculated using the same types of calculations as Dilution #4 above. Dilution # Source of plasmid DNA for dilution Initial conc. (grams/μL) C1 Volume of plasmid DNA (μL) V1 Volume of diluent (μL) Final Volume (μL) V2 Final conc. in (g/μl) C2 Resulting copy # of ß-actin sequence / 5 μl 1 stock 3 e-8 10 μl 990 μl 1000 μl 3e-10 N/A 2 Dilution 1 3 e-10 10 μl 990 μl 1000 μl 3e-12 N/A 3 Dilution 2 3 e-12 30.6 μL 69.4μl 100μl 9.18 e-13 1 000,000 4 Dilution 3 9.18 e-13 10 μl 90 μl 100 μl 9.18 e-14 100,000 5 Dilution 4 9.18 e-14 10 μl 90 μl 100 μl 9.18 e-15 10,000 6 Dilution 5 9.18 e-15 10 μl 90 μl 100 μl 9.18 e-16 1000 7 Dilution 6 9.18 e-16 10 μl 90 μl 100 μl 9.18 e-17 100 8 Dilution 7 9.18 e-17 10 μl 90 μl 100 μl 9.18 e-18 10 In the example above, dilutions 3 to 8 would be used for the quantitative PCR application. Derivation of DNA Mass Formula m = n 1.096e-21 g / bp The formula above was derived as follows m = [n] [ 1 mole] ][ 660g ]= [n ] [1.096e-21 g ] 6.023e23 molecules (bp) mole bp where: n = DNA size (bp) m = mass Avogadros number = 6.023e23 molecules / 1 mole Average MW of a double-stranded DNA molecule = 660 g/mol 124 Appendix C: Additional Statistical Analysis for the Field Study Table A. 1: SPSS output between total and nitrifying populations in both apple and grape plot for August 2013 sampling. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 2: SPSS output between total and nitrifying populations in both apple and grape plot for September 2013 sampling. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 125 Table A. 3: SPSS output between total and nitrifying populations in both apple and grape plot for August 2014 sampling. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 4: SPSS output between total and nitrifying populations in both apple and grape plot for September 2014 sampling. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 126 Table A. 5: SPSS output between total and nitrifying populations in both apple and grape plot for June 2015 sampling in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 6: SPSS output for determining variation in sampling dates for total bacterial abundance in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 127 Table A. 7: SPSS output for determining variation in sampling dates for total archaeal abundance in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 8: SPSS output for determining variation in sampling dates for nitrifying bacterial abundance in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 128 Table A. 9: SPSS output for determining variation in sampling dates for nitrifying archaeal abundance in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 10: ANOVA results for total Bacterial abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 11: ANOVA results for total Bacterial abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 129 Table A. 12: ANOVA results for total Bacterial abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 13: ANOVA results for total Bacterial abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depthss in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 14: ANOVA results for total Bacterial abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 130 Table A. 15: ANOVA results for total archaeal abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 16: ANOVA results for total archaeal abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 17: ANOVA results for total archaeal abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 131 Table A. 18: ANOVA results for total archaeal abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 19: ANOVA results for total archaeal abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 20: ANOVA results for nitrifying Bacterial abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 132 Table A. 21: ANOVA results for nitrifying Bacterial abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 22: ANOVA results for nitrifying Bacterial abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 23: ANOVA results for nitrifying Bacterial abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 133 Table A. 24: ANOVA results for nitrifying Bacterial abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 25: ANOVA results for nitrifying Archaeal abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 26: ANOVA results for nitrifying Archaeal abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 134 Table A. 27: ANOVA results for nitrifying Archaeal abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 28: ANOVA results for nitrifying Archaeal abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 29: ANOVA results for nitrifying Archaeal abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen concentrations and soil depths in the apple orchard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 135 Table A. 30: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen concentration and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the apple orchard (August 2013). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 136 Table A. 31: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen concentration and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the apple orchard (September 2013). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 137 Table A. 32: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen concentration and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the apple orchard (August 2014). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 138 Table A. 33: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen concentration and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the apple orchard (September 2014). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 139 Table A. 34: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen concentration and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the apple orchard (June 2015). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 140 Table A. 35: SPSS output for determining variation in sampling dates for total Bacterial abundance in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 36: SPSS output for determining variation in sampling dates for total Archaeal abundance in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 141 Table A. 37: SPSS output for determining variation in sampling dates for nitrifying Bacterial abundance in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 38: SPSS output for determining variation in sampling dates for nitrifying Archaeal abundance in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 142 Table A. 39: ANOVA results for total Bacterial abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 40: ANOVA results for total Bacterial abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 41: ANOVA results for total Bacterial abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 143 Table A. 42: ANOVA results for total Bacterial abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 43: ANOVA results for total Bacterial abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 44: ANOVA results for total Archaeal abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 144 Table A. 45: ANOVA results for total Archaeal abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 46: ANOVA results for total Archaeal abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 47: ANOVA results for total Archaeal abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 145 Table A. 48: ANOVA results for total Archaeal abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 49: ANOVA results for nitrifying Bacterial abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 50: ANOVA results for nitrifying Bacterial abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 146 Table A. 51: ANOVA results for nitrifying Bacterial abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 52: ANOVA results for nitrifying Bacterial abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 53: ANOVA results for nitrifying Bacterial abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 147 Table A. 54: ANOVA results for nitrifying Archaeal abundance in August 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 55: ANOVA results for nitrifying Archaeal abundance in September 2013 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 56: ANOVA results for nitrifying Archaeal abundance in August 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 148 Table A. 57: ANOVA results for nitrifying Archaeal abundance in September 2014 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Table A. 58: ANOVA results for nitrifying Archaeal abundance in June 2015 samples, to determine variations between mulch treatments, nitrogen treatments and soil depths in the grape vineyard. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 149 Table A. 59: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen treatments and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the grape vineyard (August 2013). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 150 Table A. 60: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen treatments and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the grape vineyard (September 2013). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 151 Table A. 61: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen treatments and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the grape vineyard (August 2014). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 152 Table A. 62: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen treatments and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the grape vineyard (September 2014). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 153 Table A. 63: SPSS output for univariate analysis of variance, determining the effect of mulch, nitrogen treatment and soil depth on percent nitrogen, percent carbon, ammonium nitrogen and nitrate nitrogen in the grape vineyard (June 2015). Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 154 Appendix D: Additional Statistical Analysis for the In Vitro Microcosm Study Table A. 64: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 1 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 155 Table A. 65: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 2 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 156 Table A. 66: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 3 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 157 Table A. 67: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 4 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 158 Table A. 68: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 5 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 159 Table A. 69: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 6 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 160 Table A. 70: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 7 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 161 Table A. 71: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 8 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 162 Table A. 72: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 9 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 163 Table A. 73: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 10 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 164 Table A. 74: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 11 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 165 Table A. 75: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 12 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 166 Table A. 76: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 13 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 167 Table A. 77: SPSS output for univariate analysis of variance for experiment 1, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm on Day 14 of 14. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 168 Table A. 78: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of bacterial inhibitor (ATU) concentration on N2O emissions without the addition of urea in an in vitro soil microcosm on every second day for the 10-day experimental period. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 169 Table A. 79: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of Archaeal inhibitor (PTIO) concentration on N2O emissions without the addition of urea in an in vitro soil microcosm on every second day for the 10-day experimental period. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 170 Table A. 80: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of bacterial inhibitor (ATU) concentration on N2O emissions with the addition of urea in an in vitro soil microcosm on every second day for the 10-day experimental period. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 171 Table A. 81: SPSS output for univariate analysis of variance for Experiment 2, determining the effect of Archaeal inhibitor (PTIO) concentration on N2O emissions with the addition of urea in an in vitro soil microcosm on every second day for the 10-day experimental period. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. 172 Table A. 82: SPSS output for univariate analysis of variance for Experiment 3, determining the effect of urea, inhibitor addition and soil depth and their interactions on N2O emissions in an in vitro soil microcosm. Degrees of freedom (df), and P-and F-values for each variable from the univariate analysis of variance are given. Tests of Between-Subjects Effects Dependent Variable: N2O Source df Mean Square F Sig. Corrected Model 31 18428062447145.258 .894 .626 Intercept 1 868408252402490.600 42.127 .000 depth 1 121612809172640.480 5.899 .018 urea 3 48597531286993.060 2.357 .080 inhibitor 3 26007293341329.200 1.262 .295 depth * urea 3 30575098970863.516 1.483 .227 depth * inhibitor 3 4044661534809.461 .196 .899 urea * inhibitor 9 10777013573953.617 .523 .853 depth * urea * inhibitor 9 2776694346810.670 .135 .999 Error 64 20614238772980.273 Total 96 Corrected Total 95
Thesis/Dissertation
eng
Biochemistry and Molecular Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Abundance of ammonia-oxidizing Archaea and Bacteria and their contribution to nitrous oxide emissions in woody perennial cropping systems
Text