UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Water conservation management practices in vineyards and apple orchards : strategies for mitigating greenhouse… Fentabil, Mesfin Mesganaw 2016

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2016_May_Fentabil_Mesfin.pdf [ 13.29MB ]
Metadata
JSON: 24-1.0228793.json
JSON-LD: 24-1.0228793-ld.json
RDF/XML (Pretty): 24-1.0228793-rdf.xml
RDF/JSON: 24-1.0228793-rdf.json
Turtle: 24-1.0228793-turtle.txt
N-Triples: 24-1.0228793-rdf-ntriples.txt
Original Record: 24-1.0228793-source.json
Full Text
24-1.0228793-fulltext.txt
Citation
24-1.0228793.ris

Full Text

WATER CONSERVATION MANAGEMENT PRACTICES IN VINEYARDS AND APPLE ORCHARDS: STRATEGIES FOR MITIGATING GREENHOUSE GAS EMISSIONS    by  Mesfin Mesganaw Fentabil   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in THE COLLEGE OF GRADUATE STUDIES  (Environmental Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) March 2016  © Mesfin Mesganaw Fentabil, 2016     The undersigned certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled:   Water Conservation Management Practices in Vineyards and Apple Orchards: Strategies for Mitigating Greenhouse Gas Emissions    Mesfin Fentabil submitted by   in partial fulfilment of the requirements of   Doctor of Philosophy the degree of   .         Craig Nichol, Unit 7 IKBSAS  Supervisor, Professor (please print name and faculty/school above the line)       Denise Neilsen, Unit 2 IKBSAS Adjunct, Ag. And Agri-Food Canada  Supervisory Committee Member, Professor (please print name and faculty/school in the line above)             Melanie Jones, Unit 2 IKBSAS  Supervisory Committee Member, Professor (please print name and faculty/school in the line above)       Sean Smukler, UBC Land and Food Systems  University Examiner, Professor (please print name and faculty/school in the line above)        Brian Amiro, Soil Science Dept, Faculty of Ag. And Food Sciences, University of Manitoba  External Examiner, Professor (please print name and university in the line above)       March 18, 2016         (Date Submitted to Grad Studies)     Additional Committee Members include:      Mark Johnson, UBC Land and Food Systems     (Please print name and faculty/school in the line above)         ii  Abstract Micro-irrigation scheduling, fertigation and mulching can be used by growers to improve the nutrient and water-use efficiency of crop production. Agriculture contributes to global warming through emission of greenhouse gases CO2, N2O, and CH4. There is little information about how management practices affect N2O emissions from vineyard and orchard soils. In a two year field experiment, N2O fluxes from a grape (Vitis vinifera L.; Merlot) vineyard and an apple (Malus domestica Borkh; Ambrosia) orchard under micro-irrigation in the semiarid Okanagan Valley of British Columbia were measured using static chambers. Soil mineral N and organic carbon, environmental variables and fruit yield were also monitored. Treatments in the grape vineyard involved micro-irrigation types (Drip or Micro-sprinkler), nitrogen sources (surface-applied compost or fertigated Urea; 40kg N ha-1), and two floor managements (surface-applied shredded bark and wood Mulch or Clean - bare soil). Treatments in the apple orchard involved drip irrigation frequency (twice per day or twice per day on every 2nd day) delivering the same total amount of water, orchard floor management (Mulch or Clean) and nitrogen application rate applied as calcium nitrate by fertigation (20 or 40 g N tree-1). Spring thaw accounted for 30 to 50% of annual emissions in both experiments. Over a period of two complete years in the grape vineyard Micro-sprinkler irrigation  reduced growing season N2O emissions by 29% (compared to Drip) and on an annual basis Mulch decreased them by 28% (compared to Clean ). In the apple orchard irrigation every 2nd day reduced N2O emissions by 30% and application of Mulch reduced them by 20%. Reduced drip irrigation frequency and mulching may provide an opportunity for suppressing N2O emissions from drip-irrigated vineyards and orchards. There was also large spatial variability of N2O and CO2 emissions with respect to distance relative to the   iii  apple tree and dripper locations. The emission factor (N emitted as N2O per unit of total applied N) from the applied fertilizer (uncorrected for background emission) across all treatments averaged 2.8% in the vineyard and 2.4% in the orchard. The 1% default IPCC emission factor likely underestimates N2O emissions from these systems.      iv  Preface I, Mesfin Mesganaw Fentabil, prepared all the contents of this thesis including literature review, data analysis, and interpretation of results and writing of manuscripts (listed below) under the supervision of Dr. Craig F. Nichol who also provided guidance in thesis organization, editing and proof reading for the entire thesis. The co-authors of the manuscripts are Dr. Gerry H. Neilsen, Dr. Denise Neilsen and Dr. Tom A. Forge of Agriculture and Agri-Food Canada (AAFC) and Dr. Melanie D. Jones and Dr. Kirsten D. Hannam of UBC who provided technical advice and editing the manuscripts. This research was conducted at pre-existing experimental plots that were designed by Dr. Denise Neilsen and Dr. Gerry H. Neilsen of AAFC and the plots were maintained by their technicians (Shawn Kuchta, Istvan Losso, and Bill Rabie). Dr. Kirsten D. Hannam was also involved in planning and establishing the grape plots. Shawn Kuchta undertook the analysis of nitrate and ammonium in the soil extracts. This thesis consists of two manuscripts which are either accepted or under review for publication in peer-reviewed journals and a third manuscript in preparation as given below:  A version of chapter 3 has been accepted by the Journal of Agricultural Water Management, and entitled “Effect of micro-irrigation type, N-source and mulching on nitrous oxide emissions in a semi-arid climate: An assessment across two years in a Merlot grape vineyard” (Fentabil et al., 2016).  A version of chapter 4 has been submitted to the Journal of Agriculture Ecosystems and Environment, and entitled “Effect of drip irrigation frequency, nitrogen rate and mulching on nitrous oxide emissions in a semi-arid climate: An assessment across two years in an apple orchard”, and is under review.   v   Chapter 5 presents the results of a short-term study of fine-scale spatial distribution of greenhouse gases and soil parameters around drippers and apple trees. The manuscript for this chapter is in preparation and journal selection has not been finalized.     vi  Table of Contents Abstract…………………………………………………………………………………......ii Preface……………………………………………………………………………………...iv Table of Contents ................................................................................................................ vi List of Tables ix List of Figures ...................................................................................................................... xi List of Abbreviations ........................................................................................................ xvi List of Symbols and Formulas ....................................................................................... xviii Acknowledgements ........................................................................................................... xix Dedication………………………………………………………………………………...xxi Chapter 1: INTRODUCTION ............................................................................................ 1 1.1 Motivation ......................................................................................................................... 1 1.2 Goals and Objectives ........................................................................................................ 4 1.3 The Research and Thesis Organization ............................................................................. 4 Chapter 2: LITERATURE REVIEW…………………………………………………….8 2.1 Mechanisms of N2O and CO2 generation .......................................................................... 8 2.2 Irrigation Type/Frequency and N2O emission .................................................................. 9 2.3 Mulching, Organic Amendments and N2O emissions .................................................... 11 2.4 Nitrogen application rate, yield and N2O emission ......................................................... 12 Chapter 3: EFFECT OF MICRO-IRRIGATION TYPE, NITROGEN SOURCE      AND MULCHING ON NITROUS OXIDE EMISSIONS IN A MERLOT GRAPE VINEYARD……………………………………………………………………………….13 3.1 Background ..................................................................................................................... 13 3.2 Materials and methods .................................................................................................... 14 3.2.1 Study site and experimental design ............................................................................ 14 3.2.2 Soil sampling and analyses ......................................................................................... 17 3.3 N2O flux measurements .................................................................................................. 19 3.3.1 Yield-scaled N2O emissions ....................................................................................... 21 3.3.2 Data and statistical analysis ........................................................................................ 22 3.4 Results ............................................................................................................................. 23 3.4.1 Weather, soil WFPS and temperature ......................................................................... 23 3.4.2 Daily N2O emissions ................................................................................................... 25 3.4.3 Seasonal and annual cumulative N2O emissions ........................................................ 26 3.4.4 Soil nutrients and chemistry ....................................................................................... 28 3.5 Discussion ....................................................................................................................... 29 3.5.1 Seasonal N2O emissions: thaw and the pre-growing season ...................................... 29 3.5.2 Seasonal N2O emissions: Growing Season ................................................................. 31 3.5.3 Inter-annual variations ................................................................................................ 32 3.5.4 Soil and water management effects on N2O emissions .............................................. 34 3.5.5 Yield-scaled N2O emission and N2O emission factor ................................................. 37 3.6 Summary ......................................................................................................................... 38   vii  Chapter 4: EFFECT OF DRIP IRRIGATION FREQUENCY, NITROGEN RATE AND MULCHING ON NITROUS OXIDE EMISSIONS IN AN APPLE      ORCHARD………………………………………………………………………………..48 4.1 Background ..................................................................................................................... 48 4.2 Materials and methods .................................................................................................... 49 4.2.1 Study site and experimental design ............................................................................ 49 4.2.2 Soil sampling and analyses ......................................................................................... 51 4.2.3 N2O flux measurements .............................................................................................. 53 4.2.4 Yield-scaled N2O emissions ....................................................................................... 55 4.2.5 Data and statistical analysis ........................................................................................ 55 4.3 Results ............................................................................................................................. 56 4.3.1 Weather, soil temperature and WFPS ......................................................................... 56 4.3.2 Daily N2O emissions ................................................................................................... 57 4.3.3 Seasonal and annual cumulative N2O emissions ........................................................ 58 4.3.4 Soil nutrients and chemistry ....................................................................................... 59 4.4 Discussion ....................................................................................................................... 60 4.4.1 Effect of irrigation frequency, nitrogen rate and mulching on N2O emissions ........... 60 4.4.2 Seasonal N2O emissions: thaw and the pre-growing season ...................................... 62 4.4.3 Seasonal N2O emissions: growing season .................................................................. 64 4.4.4 Yield-scaled N2O emission and N2O emission factor ................................................. 64 4.5 Summary ......................................................................................................................... 66 Chapter 5: FINE-SCALE SPATIAL DISTRIBUTION OF GREENHOUSE GAS EMISSIONS AND SOIL PARAMETERS AROUND DRIPPERS AND APPLE    TREES…………………………………………………………………………………….77 5.1 Background ..................................................................................................................... 77 5.2 Materials and methods .................................................................................................... 79 5.2.1 Study site and experimental design ............................................................................ 79 5.3 Gas sampling ................................................................................................................... 82 5.3.1 Soil moisture and soil sampling .................................................................................. 83 5.3.2 Data presentation and statistical analysis .................................................................... 85 5.4 Results and discussion .................................................................................................... 85 5.4.1 Overview of initial results and data analysis .............................................................. 85 5.4.2 Spatial patterns of WFPS, nutrients and GHG fluxes: Before irrigation .................... 87 5.4.3 Spatial patterns of WFPS, nutrients and GHG fluxes: During fertigation .................. 88 5.4.4 Spatial patterns of WFPS, nutrients and GHG fluxes: After fertigation (during irrigation) ................................................................................................................................. 88 5.4.5 Spatial correlation between soil parameters and GHG fluxes .................................... 89 5.4.6 Implications of spatial variability of GHG for chamber design and deployment     location around dripper and tree .............................................................................................. 91 5.5 Summary ......................................................................................................................... 92 Chapter 6: CONCLUSIONS AND RECOMMENDATION…………………………104 Appendix A: SUPPLEMENTAL INFORMATION ON THE GRAPE EXPERIMENT………………………………………………………………………….121 Appendix B: SUPPLEMENTAL INFORMATION ON THE APPLE       EXPERIMENT………………………………………………………………………….124 Appendix C: SUPPLEMENTAL INFORMATION ON THE SPATIAL   EXPERIMENT IN THE APPLE SITE………………………………………………..127   viii  Appendix D: CONTRIBUTIONS OF THIS RESEARCH TO OTHER               STUDIES ON THE SAME SITE……………...……………………………….………134                                               ix  List of Tables Table 3.1: Summary of climate and soil data during experimental years 2013 and 2014 including thaw dates, precipitation, irrigation, and average air and soil   temperatures. Data presented during the pre-growing season (PreGS, January through April), the growing season (GS, May through October), and                    post-growing season (PGS, November and December)………………………… ...39 Table 3.2: F-values of repeated measures analyses of daily N2O emissions in the vineyard during the pre-growing season (PreGS, January through April) and during the growing season (GS, May through October) in 2013 and 2014……………………40 Table 3.3: Effects of irrigation type, nitrogen source and vineyard floor management on seasonal and annual N2O emissions during the pre-growing season (PreGS,    January through April) and during the growing season (GS, May through      October) in 2013 and 2014…………………………………………………………41 Table 3.4: Effects of irrigation type, nitrogen source and vineyard floor management on           2-year mean seasonal and annual N2O emissions, 2-year mean N2O emissions     factor (EF, N2O emissions per unit of total N applied), and 2-year mean             yield-scaled N2O emissions (N2O/yield). Year was included as a repeated      measure factor in the ANOVA……………………………………………………..42 Table 3.5: Effects of irrigation type, nitrogen source and vineyard floor management on     mean extractable NO3--N, NH4+- N, salt-extractable organic carbon (SEOC),         pH  and electrical conductivity (EC) of soil (0-15cm depth) during the growing season (May through October) in 2013 (n= 96) and 2014 (n=144) April           through October). …………………………………………………………………..43 Table 4.1: Summary of climate and soil data during experimental years 2013 and 2014 including thaw dates, precipitation, irrigation, and average air and soil  temperatures. Data presented during the pre-growing season (PreGS, January through April), the growing season (GS, May through October), and                    post-growing season (PostGS, November and December)…………………………68 Table 4.2: F-value of repeated measures analyses of daily N2O emissions in the apple     orchard during the pre-growing season (PreGS, January through April) and        during the growing season (GS, May through October) in 2013 and 2014………...69 Table 4.3: Effects of irrigation frequency, nitrogen application rate and orchard floor management on seasonal and annual N2O emissions in the apple orchard           during the pre-growing season (PreGS, January through April) and during the growing season (GS, May through October) in 2013 and 2014……………………70     x  Table 4.4: Effects of irrigation frequency, nitrogen application rate and orchard floor management on 2-year mean seasonal and annual  N2O emissions, 2-year           mean N2O emissions factor (EF, N2O emissions per unit of total N applied),          and 2-year mean yield-scaled N2O emissions (N2O/yield) in an apple orchard.     Year was included as a repeated measure factor in the ANOVA…………………..71 Table 4.5: Effects of irrigation frequency, nitrogen application rate and orchard floor management on mean extractable NO3--N, NH4+- N, salt-extractable organic   carbon (SEOC), pH and electrical conductivity (EC) of soil (0-15 cm depth)        from the apple orchard during the growing season (May through October) in       2013 (n= 96, i.e., 24 plots x 4 sampling events) and April through October in     2014 (n=144, i.e., 24 plots x 6 sampling events)………………………………..….72 Table 5.1: Subsets of treatments considered for spatial study in a drip-irrigated apple     orchard located in the Okanagan Valley, British Columbia, Canada………………94 Table 5.2: Mean soil and air temperatures and irrigation amounts during gas and soil   sampling days within three management events in a drip-irrigated apple orchard located in the Okanagan Valley British Columbia, Canada………………………..95 Table 5.3: Mean values of extractable NO3--N, NH4+- N, salt-extractable organic carbon (SEOC) of soil (0-15 cm depth), water-filled pore space (WFPS) of soil (0-5 cm depth) and fluxes of CO2 and N2O measured inside 9 mini-chambers in the apple orchard during three events in 2013: prior to start of irrigation (BI, May 7 & 8), during fertigation (DF, June 11 & 12), and after fertigation during irrigation        (AF, August 14 & 15). Soil variables were measured only on May 8, June 12            and August 15………………………………………………………………………96 Table 5.4: Multiple linear regression and correlation of (A) N2O flux and (B) CO2 fluxes     with soil properties, distance from tree (DT) and distance from dripper (DD).      Only those regressions with p<0.05 are shown. Data shown in Figure 5.4………..97 Table 5.5: Estimates of the accuracy of GHG fluxes measured using different chamber             sizes and locations with respect to apple tree and dripper. Gas sampling in the     mini-chambers occurred at three events in 2013: prior to start of irrigation            (May 8),during fertigation (June 12), and during irrigation after fertigation     (August 15)…………………………………………………………………………98 Table C.1: Effects of irrigation frequency, sampling event, chamber location on N2O flux     (µg m2 h-1) and CO2 (mg m2 h-1) flux in the apple orchard. The sampling event before the start of irrigation was excluded from the analysis because the effect         of irrigation frequency cannot be assessed without the occurrence of irrigation.      Gas sampling during fertigation occurred June 11 & 12. Gas sampling after fertigation (during irrigation) occurred August 14& 15……..……………………133     xi  List of Figures Figure 1.1: Study location including: (A) location of the Okanagan region within Canada,  (B) the city of Summerland within the Okanagan region and (C) the research    site where studies were conducted, showing specifically the vineyard plots…….7 Figure 3.1: Spatial representation of a single vine functional unit in: (A) Drip irrigated      plot, and (B) Micro-sprinkler irrigated plot. “x” and “y” represent examples        of soil sampling locations for row chamber and alley chamber for one      sampling event, respectively. Similar locations relative to other experimental vines (not shown) in the same plot were sampled for each soil sampling event     in May, June, August, and October in 2013 and every month from April to August, and October in 2014. .............................................................................. 44 Figure 3.2: Air and soil temperature, precipitation and irrigation inputs for 2013 and        2014. “Fertig” in the top insert represent periods of fertigation, the application    of nitrogen through the irrigation system. ............................................................ 45 Figure 3.3: Soil water-filled pore space (WFPS) and soil temperature over 2013 and 2014 across: (A) irrigation type (B) nitrogen source and (C) orchard floor   management. Bars indicate standard error of the mean. *, **, *** indicate differences of least squares means of WFPS using the Tukey-Kramer     adjustment, at p < 0.05, p < 0.01, p < 0.001, respectively. Differences of        least squares means of temperature are not shown. “Fertig” in the top insert indicate fertigation, the application of nitrogen through the irrigation system. ... 46 Figure 3.4: Mean daily measurement of N2O emissions in 2013 and 2014 as averaged by:  (A) micro-irrigation type and alley position (B) nitrogen source and (C)      orchard floor management. Plus capped bars indicate standard error of the     mean (n= 12). *, **, ***, **** indicate differences of least squares means     using the Tukey-Kramer adjustment, at p < 0.05, p < 0.01, p < 0.001,                   p < 0.0001 respectively. “Fertig” in the top insert represent periods of   fertigation, the application of nitrogen through the irrigation system. ................ 47 Figure 4.1: Spatial representation of a single apple tree functional unit in a drip-irrigated   plot. “x” and “y”  represent soil sampling locations for row chamber and alley chamber, respectively........................................................................................... 73 Figure 4.2: Air and soil temperature, precipitation and irrigation inputs for 2013 and 2014. “Fertig” in the top insert represents periods of fertigation, the application of nitrogen through the irrigation system. The two different sized blue columns     are caused by two irrigation frequencies; the shorter column indicates the total mm of water applied as the result of daily irrigation and the longer column indicates the total mm of water applied as the result of irrigation every 2nd day (applied on even Julian days). Both strategies supply the same total volume, matched to estimated ET. ..................................................................................... 74   xii  Figure 4.3: Water-filled pore space (WFPS) of the soil in 2013 and 2014 across: (A) irrigation frequency (B) nitrogen application rate and (C) orchard floor management. Vertical dashed lines are used to separate seasons. “Fertig” in       the top insert represents periods of fertigation, the application of nitrogen    through the irrigation system……………………………………………………75 Figure 4.4: Mean daily of N2O emissions in 2013 and 2014 by: (A) irrigation frequency     and alley position (B) nitrogen application rate and (C) orchard floor management. Vertical dashed lines are used to separate seasons. Capped         bars indicate standard error of the mean (n= 12). *, **, ***, **** indicate    differences of least squares means using the Tukey-Kramer adjustment, at            p < 0.05, p < 0.01, p < 0.001, p < 0.0001 respectively. “Fertig” in the top      insert represents fertigation, the application of nitrogen through the          irrigation system………………………………………………………………...76 Figure 5.1: Spatial representation of a single apple tree and mini-chambers locations        (n=9)    in a drip-irrigated plot. Two transects forming axes were           designated, starting at the tree as the origin (0, 0); one axis (x-axis) was perpendicular to the tree row and into orchard alley, and the other                       (y-axis) directly in the tree row………………………………………………...99 Figure 5.2: Average spatial distributions of WFPS (%), NO3--N (mg kg-1), and               NH4+- N (mg kg-1) measured inside 9 mini-chambers at three events in           2013: prior to start of irrigation (BI, May 8), during fertigation                         (DF, June 12), and after fertigation during irrigation (AF, August 15). Each parameter is color plotted using same scale across events (BI, DF, AF);         contour lines are added when values fall outside of the color scale. For            each parameter measured inside each mini chamber n=6 (i.e averages of         three plots irrigated twice per day every day and three plots irrigated twice        per day every other day)…………………………………………………..….100 Figure 5.3: Average spatial distribution of salt-extractable organic carbon                       (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux (µg m2 h-1) measured  inside 9 mini-chambers during three events in 2013: prior to start of                irrigation (BI, May 7 & 8), during fertigation (DF, June 11 & 12), and after fertigation during irrigation (AF, August 14 & 15). SEOC was measured           only on May 9, June 12 and August 15. Each parameter is color plotted          using the same scale across events (BI, DF, AF). For SEOC measured           inside each mini chamber, n=6. For CO2 and N2O flux, n=12; plotted            valuesare the average of measurements on two consecutive days in three         plots irrigated twice per day every day and three plots irrigated twice per          day every other day…………………………………………………….........101     xiii  Figure 5.4: Results of multiple linear regression used to identify relationships between        (A) CO2 flux and (B) N2O flux with soil properties and distances from tree       and dripper in an apple orchard (see Table 5.4 for fitting parameters).       Averages of 6 measurements for each parameter (flux and soil parameter)       were used in the regression analysis……………………………….………...102 Figure 5.5: Spatial representation of a single apple tree in a drip-irrigated plot and        locations of (A) mini-chambers (n=9) and medium chamber (n=3)            locations and (B) mini-chambers (n=9), a long-term chamber and an             “ideal chamber”. Two transects forming axes crossing at the tree and     designated as the origin (0, 0); one transect (x-axis) was perpendicular to          the tree row and into orchard alley, and the other (y-axis) directly in the            tree row………………....................................................................................103 Figure A.1: Grape experiment plot layout. Capital letters inside each plot represent soil amendment: C = compost, U = urea, BM = bark mulch, and                               PKB = phosphorus, potassium and boron. Note that PKB plots were not          used in this study. There were four chambers in each row from row #5 to         #10. One chamber was deployed at a random location in each alleyway        between rows #5 to #11.…………………….……………………………….121 Figure A.2: Frequency of measured values of WFPS in the vineyard experiment during       the growing season of 2013 for (A) Drip irrigation and (B) Micro-sprinkler irrigation. Shaded areas on the left of the graphs indicate the times spent at       and above the 60% WFPS threshold for enhanced denitrification…………..122 Figure A.3: Frequency of measured values of WFPS in the vineyard experiment during       the growing season of 2014 for (A) Drip irrigation and (B) Micro-sprinkler irrigation. Shaded areas on the left of the graphs indicate the times spent at       and above the 60% WFPS threshold for enhanced denitrification……….….123 Figure B.1: Apple experiment plot layout………………………………………….…….124 Figure B.2: Frequency of measured values of WFPS in the orchard experiment during         the growing season of 2013 and 2014 for plots that were drip-irrigated              (A) twice per day every day and (B) twice per day every other day. Shaded           areas on the left of the graphs indicate the times spent at and above the            60% WFPS threshold for enhanced denitrification……………………….....125 Figure B.3: Frequency of measured values of WFPS in the orchard experiment during         the growing season of 2013 and 2014 for (A) Clean plots and (B) Mulch            plots. Shaded areas on the left of the graphs indicate the times spent at and       above the 60% WFPS threshold for enhanced denitrification...………..……126       xiv  Figure C.1: Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable      organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux                 (µg m2 h-1) in Clean plots that were drip-irrigated twice per day every day.        Gas samples were taken from 9 mini-chambers at three events in 2013:           prior to start of irrigation (May 8 & 9), during fertigation (June 11 & 12),          and during irrigation after fertigation (August 14 & 15). Soil samples              were taken only on May 8, June 12 and August 15. Each parameter is             color plotted at the same scale across events; contour lines are added              when values fall outside the color scale. Averages of three measurements       (n=3) for each soil parameter and six measurements (n=6) for GHG               fluxes were used for plotting……………………….......................................127  Figure C.2: Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable      organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux                 (µg m2 h-1) in Clean plots that were drip-irrigated twice per day every             other day. Gas samples were taken from 9 mini-chambers at three events             in 2013:   prior to start of irrigation (May 8 & 9), during fertigation                 (June 11 & 12), and during irrigation after fertigation (August 14 & 15).               Soil samples were taken only on May 8, June 12 and August 15. Each             parameter is color plotted at the same scale across events; contour lines              are added when values fall outside the color scale. Averages of three measurements (n=3) for each soil parameter and six measurements (n=6)          for GHG fluxes were used for plotting………………………………………128  Figure C.3: Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable        organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux                 (µg m2 h-1) in Mulch plots that were drip-irrigated twice per day every            day. Gas samples were taken from 9 mini-chambers at three events in            2013: prior to start of irrigation (May 8 & 9), during fertigation                       (June 11 & 12), and during irrigation after fertigation (August 14 & 15).          Soil samples were taken only on May 8, June 12 and August 15. Each             parameter is color plotted at the same scale across events; contour lines              are added when values fall outside the color scale. Averages of three measurements (n=3) for each soil parameter and six measurements (n=6)                for GHG fluxes were used for plotting……....………………………………129  Figure C.4: Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable      organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux                (µg m2 h-1) in Mulch plots that were drip-irrigated twice per day every            other day. Gas samples were taken from 9 mini-chambers at three events             in 2013: prior to start of irrigation (May 8 & 9), during fertigation                      (June 11 & 12), and during irrigation after fertigation (August 14 & 15).          Soil samples were taken only on May 8, June 12 and August 15. Each      parameter is color plotted at the same scale across events. Averages of            three measurements (n=3) for each soil parameter and six measurements        (n=6) for GHG fluxes were used for plotting………….…………………….130   xv  Figure C.5: Results of linear regression used to identify relationships between distance     from tree and (A) CO2 flux and (B) N2O flux across three events (before irrigation, during and after fertigation). Averages of 6 measurements of            flux were used in the regression. Linear fitted lines applied to correlations      when p< 0.05…………………………………………………………….......131 Figure C.6: Results of linear regression used to identify relationships between distance     from dripper and (A) CO2 flux and (B) N2O flux during fertigation,                     (C) CO2 flux and (D) N2O flux after fertigation. Averages of 6          measurements of flux were used in the regression. Linear fitted lines            applied to correlations when p< 0.05……………………………………..…132              xvi  List of Abbreviations  AAFC: Agriculture and Agri-Food Canada AF: After Fertigation BCGGA: British Columbia Grape Growers Association BF: Before Irrigation CEC: Cation Exchange Capacity DD: Distance from Dripper DF: During Fertigation DT: Distance from Tree E: Sampling Event EC: Electrical Conductivity ECD: Electron Capture Detector EF: Emissions Factor F: Floor management FAO: Food and Agriculture Organization of the United Nations Fertig: Fertigation FID: Flame Ionization Detector  GHG: Greenhouse Gas  GS: Growing Season (May through October) GWP: Global Warming Potential  HF: High Frequency irrigation (irrigation every day) HN: High Nitrogen (40 N g/tree or 127 kg N/ha) I: Irrigation   xvii  IPCC: Intergovernmental Panel on Climate Change L: Chamber location LF: Low Frequency irrigation (irrigation every second day) LN: Low Nitrogen (20 N g/tree or 63 kg N/ha) PKB: Phosphorus, Potassium and Boron PostGS/PGS: Post-Growing Season (November and December) PreGS: Pre-Growing Season (January through April) SEOC: Salt-Extractable Organic Carbon SRDC: Summerland Research and Development Centre  TCD: Thermal Conductivity Detector TDR: Time Domain Reflectometry  TN: Total nitrogen  UV: Ultraviolet  WFPS: Water-Filled Pore Space  xviii  List of Symbols and Formulas CH4: Methane CO2: Carbon dioxide  KCl: Potassium chloride log: common logarithm, log10 N2: Nitrogen gas N2O: Nitrous oxide gas NH3: Ammonia NH4+- N: Nitrogen in ammonium ion NO: Nitric oxide gas NO2: Nitrogen dioxide  NO2-:  Nitrite ion NO3-:  Nitrate ion NO3--N: Nitrogen in nitrate ion O2: Oxygen  O3: Ozone  RA : Autotrophic respiration RH : Heterotrophic respiration       xix  Acknowledgements My first special gratitude goes to my Ph.D supervisor Dr. Craig Nichol for his invaluable guidance, encouragement and unwavering support he provided directly and also by employing multiple undergraduate students to help me conduct field and lab work throughout this project; without that help I would not have been able to focus on my data analysis and writing, and my work would have been going on and on. I would also like to thank him for the generous financial support and for his flexibility, particularly for allowing me to spend some of my research time to take several courses for my professional development. Besides my advisor, special thanks go also to the members of my advisory committee Dr. Denise Neilsen, Dr. Melanie Jones, Dr. Mark Johnson and Dr. Uli Mayer for their guidance, helpful discussions and advice provided during meetings, manuscript and thesis writing. Many thanks for taking the time out of your busy schedule and holidays to enable me to complete my research. I would like to thank Dr. Denise Neilsen, Dr. Gerry Neilsen, Dr. Kirsten Hannam and Dr. Tom Forge for the base experiments upon which my work was based and  their contribution to manuscript writing. I would also like to thank Dr. Marina Molodovskaya, Valerie Ward, Scott Fazackerley and Dr. Markandu Anputhas, Shawn Kuchta, Istvan Losso, Bill Rabie for providing technical assistance at various stages in this research. In addition, I am grateful to many undergraduates including Brittany Derrick, Russell Kirchner, Tom Zochowski, Chris Perra, Mirage Leung, Graham Knibbs, Jeff Kerkovius, Stephen Kimanzi, Dan Lang, Leo Kerrigan, Danielle Homer, Jeffrey van Santen, Caroline Hedge and Robert Roskoden for sacrificing long hours to collect over 30,000 samples from the field and help   xx  analyze them at the lab. I am sure kneeling down 120 times holding a syringe every gas sampling day had kept you in a good shape; so keep it up! My study was financed primarily by the Agricultural Greenhouse Gases Program of AAFC, the Canada Foundation for Innovation (CFI), and the work-study program of UBC awarded to Dr. Craig Nichol and by the various graduate grants awarded to myself. These awards include several Ph.D. Tuition Fee Okanagan Awards, a Special UBC Okanagan Award, UBC Okanagan Graduate student Travel Grant and an American Society of Agronomy student Travel Grant. Thank you all for the financial support. My never-ending gratitude goes to my special mom, Yesharg Teshome, and my sisters (Marta, Haregewoin and Frehiwot) for their frequent encouragement and wise advice that helped me navigate through tough times. Special thanks goes to my lovely beautiful wife, Selamawit Tameene, not only for the love and care she have shown me even when I was negligent sometimes because of my studies but also for being special mom to my little daughters (Arsiema and Meron)…. not to mention the tasty healthy food she cooks every day to nourish my little brain  To Arsiema and Meron, your dad has become the first person in his family to earn a PhD degree; I hope this work will inspire you to achieve success in your life. We now have more time to play hide and seek   xxi  Dedication      In the loving memory of my dad, Mesganaw Fentabil, who inspired me, shaped my character and seeded confidence in me at the very early stages of my life.                  1  Chapter 1: INTRODUCTION 1.1 Motivation Climate change is believed by many to be the greatest threat the modern world has ever faced (Addiscott, 2005; Ki-moon, June 23, 2009). Extreme weather events caused by global climate change such as hurricanes, mud slides from sudden glacial floods, lakes swollen by melting mountain glaciers, heavy rain and drought, have been threatening the lives of hundreds of millions of people in recent years (Li et al., 2011; Rafferty, 2011). Increasing concentrations of greenhouse gases (GHGs) in the atmosphere have been responsible for the occurrence of global warming and changes in the global climate (Rogner, et al., 2007). Despite some controversies (Addiscott, 2005), the international community is working hard to control global climate change through international efforts such as the 1997 Kyoto Protocol, the 2009 Copenhagen accord (Rafferty, 2011) and the 2015 Paris climate summit. Scientists warn that if the world warms by more than 2 0C on average above pre-industrial levels by the end of this century, the effects of climate change will become catastrophic and irreversible. In an effort not to pass the crucial 2 0C global warming limit, over 190 countries, including Canada, have already agreed to reduce greenhouse gas emissions ahead of the 2015 Paris climate summit (The Guardian, 2015).  Agricultural soils cover 37% of the earth’s land surface (Smith et al., 2008). The main GHGs emitted from agricultural soils include nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2). Agriculture is responsible for 84% of the global anthropogenic N2O and 52% of the global anthropogenic CH4 emissions (Smith et al., 2008). Crop production is believed to contribute only small net emissions of CO2 since crop fields act both as sources and sinks for CO2 (Carlisle et al., 2010; Smith et al., 2008). Each of the aforementioned GHGs has a different   2  ability to trap heat in the atmosphere. They also have different lifetimes in the atmosphere. Because of that, the ability of each GHG to trap heat over a specified time period is usually measured relative to CO2 and is called the Global Warming Potential (GWP). Accordingly, over a period of 100 years, N2O has a GWP 298 times greater than that of CO2, and CH4 has a GWP 34 times greater than that of CO2 (IPCC, 2013). Of these three important GHGs, N2O poses an additional environmental concern because of photolytic conversion to nitric oxide (NO), which depletes ozone (O3) in the upper atmosphere. Nitric oxide together with nitrogen dioxide (NO2) undergo a set of reactions that transform ozone (O3) to molecular oxygen (O2), thereby leading to O3 layer depletion. Depletion of the ozone layer, which acts as a filter to remove biologically harmful ultraviolet (UV) light, may pose a serious risk to ecosystem and human health. High levels of UV radiation may be carcinogenic to humans and inhibitory for existence of certain microorganisms (Maier et al., 2009).  Adaption to changing climate and increasing world population requires more food production. Sustainable agricultural management practices are needed that maximize yield and food quality, and minimize the use of resources (water, nitrogen, organic additions) and environmental impacts (GHG emissions, nitrate leaching). There is a need to find adaptive global and regional management practices that reduce GHG emissions, preserve ecosystem functions and ultimately produce more food with fewer inputs (Verhoeven, 2012). For example, irrigation can increase food production, but water for irrigation is limited. Agricultural production has also been a source of a massive perturbation to the global nitrogen cycle since the industrial revolution, and has caused significant increases in the net global N2O emissions (Reay et al., 2012). A significant portion of the applied nitrogen for agricultural food production can be lost as gaseous N (as nitrous oxide, N2O; ammonia, NH3; nitric oxide, NO; and dinitrogen, N2) or   3  through leaching of nitrate (NO3-) into groundwater (e.g.: Armstrong, 2015). Global atmospheric concentrations of N2O have on average increased from background levels of about 0.270 ppm in preindustrial times (till 1750) to about 0.319 ppm in 2005 (IPCC, 2007). With an increasing world population, and the subsequent need for more food production, there is an urgent need for mitigating agricultural N2O emissions in an effort to control global warming. Most N2O emission studies have been undertaken on intensively managed, annual cropping systems and in animal production systems. There are limited data on N2O emissions from vineyards and apple orchards (Carlisle et al., 2010; Pang et al 2009; Steenwerth and Belina, 2010), yet these systems cover more than 12 million ha worldwide (FAO, 2013). In Canada, vineyards and apple orchards cover around 27,000 ha (FAO, 2013), with over 7,200 ha located in the semi-arid Okanagan region of British Columbia (BCGGA, 2011; Seymour, 2015). These woody perennial crops (grapes and apples) are expected to have low carbon footprints compared to other crops because of conservative management practices with respect to nitrogen fertilization, along with carbon sequestration in the permanent structures such as roots, trunks, and cordons, the “arms” of a grapevine that extend from the trunk (Carlisle et al., 2010). Despite covering a significant acreage of the global agricultural land and their potential for carbon sequestration, the effects of various agricultural management practices on GHG emissions from these woody perennial crops are not well known (Carlisle et al., 2010; Garland et al., 2011 and 2014). Most of the available N2O emission studies from woody perennial crops are based on monitoring over a short period (weeks or few months) and only during the growing season. Substantial losses of N2O have been reported during spring thaw events for other cropping systems. Nevertheless, continuous N2O emissions data across seasons within a year and between years are lacking from prior studies of woody perennials. Consequently, the long-term effect of   4  various agricultural managements on N2O emissions is still unknown. 1.2 Goals and Objectives The overall goal of this project was to determine how water-conserving management practices and soil amendments affect seasonal and annual N2O emissions in woody perennial fruit production systems. The specific objectives of this research were to investigate how N2O emissions were affected by (1) type of micro-irrigation system (drip or micro-sprinkler) and irrigation frequency, (2) nitrogen source (surface-applied compost or synthetic nitrogen applied through fertigation), and (3) vineyard or orchard floor management (shredded bark and wood mulch versus bare soil). In order to understand GHG drivers and the implications of spatial variability of GHGs on chamber methodology, the research further investigated spatial distribution of GHG emissions and soil parameters around trees and drippers at the sub-plot scale. 1.3 The Research and Thesis Organization This research was conducted as part of the AAFC- Agricultural Greenhouse Gases Program (AGGP) effort to develop beneficial management practices for mitigating GHG emissions from agricultural soils. The Government of Canada launched the AGGP in 2012 “as part of the Global Research Alliance on Greenhouse Gases, aimed at increasing international cooperation, collaboration and investment in public and private research activities to help the sector reduce GHG emissions while enhancing productivity and resilience to climate change” (Environment Canada, 2015). This research investigated the effects of various management practices on annual and seasonal N2O emissions from a grape vineyard (Vitis vinifera, Merlot variety) and an apple orchard (Malus domestica Borkh, cv. Ambrosia) at the Summerland Research and Development   5  Centre (SRDC) of Agriculture and Agri-Food Canada located in the Okanagan Valley, a semi-arid region of British Columbia, Canada (Figure 1.1). The experimental site has a 30-year average (1981 to 2010) annual precipitation of 346 mm, and daily average air temperature of 9.6 ºC with a minimum daily average of -1.5 ºC in January and a maximum daily average of 28.4 ºC in July (Environment Canada 2014a, 2014b). In this cold-climate wine-producing region, below freezing soil temperatures in the winter period occur between November and February. Daily maximum air temperature in July and August can reach 35-38 ºC. The grape and apple plantings were pre-existing experiments studying the effects of various inputs of nitrogen, carbon and other nutrients, water (timing and application method), soil floor management (mulches or bare soil ) on fruit quality and quantity. Both experiments were irrigated using micro-irrigation strategies to conserve water, and in those with artificial fertilizer, nutrients were added into the irrigation water (fertigation). These were used to investigate the relationship of agricultural management practices to long-term measurements of N2O emissions. Nitrous oxide fluxes were measured using manual-vented, non-steady-state chambers for selected treatments in periodic campaigns over a period of two years, along with soil moisture, temperature and nutrients. In order to better understand the parameters controlling emissions of GHGs (N2O and CO2), an additional series of experiments using small size chambers were conducted in the apple orchard to study the small-scale spatial distribution of GHG emissions and soil properties. The thesis is organized based on three manuscripts, which are either accepted, submitted or in preparation at the time of writing. Chapter 2 provides a common literature review to provide background information for all three main experiments. It covers mechanisms of N2O and CO2 formation, the key findings of previous studies and the gaps of knowledge that were the   6  motivation to conduct this research. Chapter 3 and 4 provide detailed findings based on the two-year studies conducted in the grape vineyard and apple orchard, respectively. A version of Chapters 3 is accepted by the Journal of Agricultural Water Management (Fentabil et al., 2016) and a version of Chapters 4 is submitted to Journal of Agriculture Ecosystems and Environment. Chapter 5 provides the results of a sub-plot scale experiment that was conducted in the apple orchard; the manuscript for this experiment is currently in preparation. Chapter 6 concludes the thesis by drawing together findings from the three studies, providing recommendations of beneficial agricultural management practices to mitigate N2O emission from vineyards and orchards, demonstrate how the work presented has been used in further studies at these experimental sites, and makes suggestions for further work.         7   Figure 1.1: Study location including: (A) location of the Okanagan region within Canada, (B) the city of Summerland within the Okanagan region and (C) the research site where studies were conducted, showing specifically the vineyard plots.                        8  Chapter 2: LITERATURE REVIEW 2.1 Mechanisms of N2O and CO2 generation Nitrous oxide from soil is formed by a wide range of microbial processes with two major pathways, nitrification and denitrification (Braker and Conrad, 2011). Nitrification is the conversion of reduced nitrogenous compounds to others having nitrogen in more oxidized states (Bock et al., 1990; Prosser and Embley, 2002). Denitrification is the reduction of nitrate (NO3-) or nitrite (NO2-) to gaseous nitrogen (usually N2O or N2) (Garcia and Tiedje, 1982). Two main groups of microorganisms, autotrophic nitrifiers and heterotrophic denitrifiers, are responsible for these processes (Braker and Conrad, 2011). Both nitrification and denitrification occur simultaneously under most soil conditions. However, the contribution of each these processes to the overall N2O flux depends on various factors such as soil type (Ambus et al., 2006), fertilizer applications (Senbayram et al., 2009), O2 availability (Bollmann and Conrad, 2004), irrigation (Panek et al., 2000), season (Kester et al., 1997), pH (Baggs et al., 2010), and competition between fungi and denitrifying bacteria for nitrate (Siciliano et al., 2009). Production of N2O by nitrification is generally favored by increasing the availability of NH4+, moderate pH and well-aerated soils, but declines as soils become very dry (Barnard et al., 2005).  Production of N2O by denitrification is generally favored by high availability of labile carbon as source of energy and of NO3- as an electron acceptor (Barnard et al., 2005). Denitrification is favored in poorly aerated soils, with a pH close to neutral. During denitrification, NO3- is reduced to NO2- and then to the gases NO, N2O, or N2. During nitrification, some NO, N2O, and N2 can be released through two pathways, chemical decomposition of hydroxylamine and nitrifier denitrification (Wrage,et al., 2001; Barnard et al., 2005).   9  Carbon dioxide is formed in soil primarily through plant root respiration and microbial respiration. Soil respiration, which is the sum of these two processes can be divided into two components. The autotrophic component (RA) is produced by roots and the associated rhizosphere (mycorrhizae and rhizosphere bacteria) and the heterotrophic component (RH) originates from soil micro-organisms that decompose the organic materials from both above-ground and below-ground litter (Boone et al., 1998; Bowden et al., 1993). RA involves root carbohydrates and root exudates that have very low residence time in soil and hence represent a labile portion of the total soil carbon pool. The RH component involves a larger carbon pool with a longer residence time in soil ranging from months to years for fresh litter and from years to centuries for old soil organic matter. Various environmental and physio-morphological factors are involved in determining the short- and long-term rates of respiration. These include changes in temperature, soil moisture, nutrient demand/supply, assimilate supply and plant phenology (Moyano, et al., 2009).  Carbon dioxide in soil can also be formed from inorganic carbon sources added to soil either advertently or inadvertently during agricultural production (e.g. liming and irrigation with carbonate rich water). Liming (using CaCO3 and/or dolomite (CaMg(CO3)2 to control soil acidity) (Mosier et al. 2005) and irrigation with carbonate rich water (Hannam et al., 2015) for  agricultural production can represent a major source of CO2 especially in regions where soil acidity is a problem and in semi-arid regions where fresh water supplies are limited. 2.2  Irrigation Type/Frequency and N2O emission Few studies have investigated water management effects on N2O emissions in general and from perennial cropping systems in particular, and none were in a northern climate that experiences a spring ground thaw. Sanchez-Martin et al. (2008) found that drip irrigation   10  resulted in N2O emissions 70% lower than furrow irrigation in a loamy arable soil and grassland soil after incorporation of broadcast ammonium sulphate in the soil. Sanchez-Martin et al. (2010) observed reduction of N2O emissions by drip irrigation (28%) compared to furrow irrigation in a melon crop on a sandy clay loam soil fertilized with digested pig slurry, Ca(H2PO4)2 and K2SO4. Emissions of N2O from these two experiments were monitored for a period of less than 5 months and only during the growing season under a Mediterranean climate in Madrid. In California under a Mediterranean climate, Smart et al. (2011) compared emissions from an almond orchard on a sandy loam soil fertigated by stationary fanjet sprinklers and drip irrigation; their observations covered a period of a month in the fall and another month in spring and they reported lower emissions of N2O with stationary fanjet sprinklers. In a grape vineyard in California, Suddick et al. (2011) indicated lower N2O emissions for sub-surface drip irrigation than surface drip irrigation following a four-day fertigation event. All of the above studies used non-steady-state manual chambers for measuring N2O emissions and most of them were short-, lasting few days or months during the growing season. Substantial losses of N2O have been reported during spring thaw events for other cropping systems in climates with winter temperatures below freezing (Lemke et al., 1999; Müller et al., 2002; Holst et. al, 2008; Tatti et al., 2014). Nevertheless, continuous N2O emissions data across seasons within a year and between years are lacking from published studies of woody perennials. Consequently, the long-term effects of various water management strategies on N2O emissions are still unknown. Published studies that investigated the effect of irrigation frequency on N2O emissions are also rare. Rolston et al. (1982) compared denitrification rates from three irrigation frequencies (three events per week, one per week and one every 2 weeks) applied to perennial ryegrass on a loamy soil in California; over a monitoring period of about two months, they found   11  the greatest total N2O emissions occurred under the most frequent irrigation treatment. By contrast, Abalos et al. (2014) found no relationship between irrigation frequency (weekly or daily) and N2O emissions in an 85-day experiment in a melon field on a clay loam soil in Madrid. These studies measured N2O emissions for less than 3 months during the growing season and none of them were conducted in woody perennial systems. Similar to above, continuous N2O emissions data across seasons within a year and between years are lacking and the longer-term effects of irrigation frequency on N2O emissions are still unknown.  2.3 Mulching, Organic Amendments and N2O emissions Woody perennial crops can be mulched in order to deter weed growth, moderate soil temperature and moisture, reduce evapotranspiration losses, and buffer soil pH in a range optimal for fruit growth (Forge et. al, 2013). Organic mulch adds carbon to the soil  and  depending on the source, can increase the supply of macronutrients to the soil (N-P-K) (Paul 2013) while reducing populations of root-lesion nematodes (Forge et al., 2008), improving soil biological activity (Neilsen et.al. 2014), and controlling soil salinity and sodicity (Aragüés et al., 2014). The addition of compost as a replacement for mineral N fertilizer can also increase soil organic carbon, and supply micronutrients. In prior studies, the addition of organic matter has had contradicting effects on N2O emission. Addition of organic matter in some field studies resulted in decrease of N2O emission (Lopez-Fernández et al., 2007; Livesley et al. 2010; Sanchez-Martin et. al, 2010; Steenwerth and Belina, 2010; Nyamadzawo et al. 2014) while it also resulted in increase of N2O emission in other studies (Laidlaw 1993; Cochran et al. 1997; Pelster, et al., 2012). Chantigny et al. (2010) and Pelster, et al. (2012) suggested that denitrification rates in sandy loam soils are more limited by C availability than mineral N concentrations, and suggested that the addition of C, through organic amendments such as   12  poultry manure, liquid cattle manure, or liquid swine manure, could lead to higher N2O emission compared to mineral fertilizers such as calcium nitrate and urea. However, labile organic C also helps in completing the denitrification process, resulting in conversion of N2O to N2, unless there are some inhibiting factors. Such factors including high EC, high pH (Ruiz-Romero, et al, 2009), acetylene and sulphides (Knowles, 1982) are present in some organic amendments like manure (Brown, 2012). Most studies have used manure as a source of organic matter and little is known about the effect of shredded bark and wood mulch or composted plant waste on N2O emissions. 2.4 Nitrogen application rate, yield and N2O emission Higher rates of nitrogen fertilizer are usually associated with higher yields, but a significant portion of the applied nitrogen can be lost as gaseous N (as nitrous oxide, N2O, ammonia, NH3; nitric oxide, NO; and dinitrogen, N2) or through leaching of nitrate (NO3-) (Armstrong, 2015). It is important to optimize the nitrogen application rate to satisfy plant needs while minimizing environmental impacts. For nitrous oxide, fertilizer efficiency can be assessed by expressing N2O emissions per unit of crop yield (i.e. yield-scaled emissions) (Venterea et al., 2011). Reports of yield-scaled N2O emissions in woody perennial crops are rare (Schellenberg et al., 2012), although reports of yield-scaled N2O emissions are becoming common for other cropping systems (Halvorson et al., 2010; Maharjan, et al., 2014; Wei et al., 2010; Gagnon et al., 2011; Venterea et al., 2011). Data on yield-scaled N2O emission from woody perennial crops are required to evaluate the environmental suitability of nitrogen application rate and other agricultural management practices.      13  Chapter 3: EFFECT OF MICRO-IRRIGATION TYPE, NITROGEN SOURCE AND MULCHING ON NITROUS OXIDE EMISSIONS IN A MERLOT GRAPE VINEYARD A version of this chapter has been accepted by the Journal of Agricultural Water Management (Fentabil et al., 2016). The lead author is Mesfin M. Fentabil and the co-authors are Craig F. Nichol, Gerry H. Neilsen, Kirsten D. Hannam, Denise Neilsen, Tom A. Forge and Melanie D. Jones.  3.1 Background In order to track and manage national and global emissions effectively, N2O emissions in woody perennial crops under different management regimes must be quantified across a range of soil types and geographic regions (Carlisle et al., 2010; Garland et al., 2011). There are limited N2O emissions data from vineyards (Carlisle et al., 2010; Steenwerth and Belina, 2010), yet these systems cover more than 7.1 million ha worldwide (FAO, 2013). Vineyards in the semi-arid Okanagan region of British Columbia alone cover approximately 4,150 hectares (BCGA, 2014). Many vineyards have converted from overhead sprinklers to under vine micro-irrigation (micro-sprinkler or drip) to improve water-use efficiency. The effect of this water management change on N2O emissions is not known, nor has a preferable micro-irrigation strategy been identified. This study aimed to determine the effects of agricultural management practices on N2O emission in a vineyard under a semi-arid climate. The chapter presents data collected over two full years, and includes measurements prior to, during and after the growing season in a Merlot grape vineyard. It presents an investigation of how N2O emissions are affected by: (1) type of micro-irrigation system (Drip vs Micro-sprinkler); (2) type of nitrogen source (surface-applied   14  Compost or Urea applied through fertigation); and (3) vineyard floor management (shredded bark and wood Mulch versus bare soil, here in referred to as “Clean”). 3.2 Materials and methods 3.2.1 Study site and experimental design The study was conducted in the south-central Okanagan Valley (Lat. 49°33′59′′N and Long. 119°38′12′′W), at the Summerland Research and Development Centre (SRDC) of Agriculture and Agri-Food Canada, British Columbia, Canada (Figure 1.1). The site has a 30-year average (1981 to 2010) annual precipitation of 346 mm, and daily average air temperature of 9.6 ºC with a minimum daily average of -1.5ºC in January and a maximum daily average of 28.4 ºC in July (Environment Canada 2014a, 2014b). In this cold-climate wine producing region, below freezing soil temperatures occur in the winter period occur between November and February. Daily maximums of air temperatures in July and August can reach 35-38 ºC. The soil is classified as a Skaha Sandy Loam, which is fluvio-glacial in origin, has moderate water holding capacity (Wittneben 1986) and a cation exchange capacity (CEC) of 10.6 meq/100g, a pH of 7.4, and a C:N ratio of 8.5 before treatment initiation. Abundant coarse fragments with coatings of secondary carbonates are found at 30 - 50 cm depth. Secondary carbonates are carbonates (CaCO3) and/or dolomite (CaMg(CO3)2) that precipitated from the soil solution rather than being inherited from a soil parent material. Secondary carbonates can be formed by irrigation with carbonate rich water (Hannam et al., 2015). The experiment was established on a moderate, south/south-west-facing slope (Figure A.1). The Merlot (Vitis vinifera) vines on SO4 rootstock were planted in 14 rows of vines in May 2011. The outer rows on each end of the planting were ‘guard rows’, used to eliminate edge effects and were not directly involved in the experiment. The planting was divided into six   15  blocks consisting of two rows of vines (12 rows total). Each row consisted of five plots and there were five vines per plot. The outer vines were ‘guard vines’ and middle three vines were ‘experimental vines’. Vine spacing was 1.2 m within rows and 3.0 m between rows. Vine density was 2 778 vines/ha. A common orchard grass mix was grown in the 1.5 m wide inter-row (alley). Plots were kept weed-free via the use of herbicides (primarily glyphosate). Experiments were factorial treatment designs consisting of two micro-irrigation types (Drip or Micro-sprinkler), two nitrogen sources (Compost or Urea), and two vineyard floor managements (bark Mulch or Clean). Main plots (complete rows) were comprised of two different low pressure irrigation systems (Drip, Micro-sprinkler) whereas subplots consisted of five fertilization/amendment combinations planted in each of the six replicates for each irrigation treatment. The Compost plots and Urea plots had different irrigation lines to enable selective fertigation of Urea plots. Two 4 L h-1 pressure compensating drippers were placed on opposite sides of each vine along the vine row (Figure 3.1A) in Drip plots, and one 20 L h-1 flow regulating micro-sprinkler (AquaSmart 2000 Naandanjain Ltd., Israel) was placed mid-way between two adjacent vines in Micro-sprinkler plots (Figure 3.1B). Both micro-irrigation types were suspended about 0.3 m above the soil surface. An atmometer (ETGage Co, Loveland, CO) was used to estimate potential evapotranspiration, which was combined with a crop coefficient model  (Neilsen et al., 2015) to estimate water required to replace 100% of the previous day’s losses to evapotranspiration via four equal applications every 6 h over a 24 h period controlled by a CR10X datalogger (Campbell Scientific, Logan, UT). The irrigation season extended from May through October in both 2013 and 2014.  All the grape plots received a total of 40 kg N ha-1 per year in the form of either Urea or Compost. Urea was fertigated for four weeks from mid-May to mid-June from 2011 to 2013, and   16  from late May to late June in 2014, timed to coincide with grape development. Compost was applied on a 1.5 m wide strip centered on the vine row. Compost was used as a nitrogen source in the plots that were not fertigated with Urea.  Compost was applied in late May each year (2011 to 2014) and manually incorporated into the soil to an approximate depth of 5 cm. The locally produced compost was made from approximately 15% grape pomace, 20% straw, 25% shredded bark and wood chips and 40% cow manure and screened through 0.6 cm mesh. Prior to application in the field, the N content of compost was determined on air-dried and ground samples using a LECO FP-528 (Leco Corporation, St. Joseph, MI).  Based on the C/N ratio of the compost (3 year average = 20), the estimated first-year mineralization of N would be no more than 20% of the total N content (Gale et al., 2006). This liberal estimate of Potentially Available N (PAN) was used to adjust the compost application rate each year to provide an estimated 40 kg PAN/ha. The mineralization of N from surface-applied compost in the second and third years after application is not well understood. Consequently, no attempt was made to account for previous years' applications when making the calculations for any given year. While a liberal estimate of PAN was used in this study, this approach nonetheless creates the potential for accumulation of residual mineralizable N, and the possibility that in later years of the experiment the release of mineral N from the compost exceeded the 40 kg N/ha target. Mulch made from shredded bark and wood chips was also applied on a 1.5 m wide strip centered on the vine row.  Mulch was used primarily to minimize evaporation of water from the soil. Both compost and mulch also add carbon to the soil. Mulch applied to the row was composed of shredded bark and wood-chips (primarily from Pinus contorta var latifolia and Picea glauca) generated as waste from local sawmills. It was surface applied in late May of every second year (2011, 2013) to maintain a total mulch depth of approximately 10 cm. New   17  mulch was added over top of existing mulch without disturbance to the underlying material. Mulch was not applied in 2012 and 2014 because it was still sufficiently thick to suppress weeds.  3.2.2 Soil sampling and analyses At each soil sampling time, soil cores to a depth of 15 cm were collected from nine locations (Figure 3.1) in the row and another nine locations in the alley using a 2-cm diameter auger. Samples were composited to get a representative sample for the row and the alley part of each plot. Soil sampling was conducted in May, June, August, and October in 2013, and every month from April to August, and in October in 2014 (n=10). Soil samples were kept frozen until extraction and analysis. Field moist soils were extracted for exchangeable nitrate-N and nitrite-N (NO3--N) and ammonium-N (NH4+-N) with 2 M KCl using a 1:5 soil to extractant ratio and a     1- h shaking time followed by filtration through Whatman No. 40 filter paper. Extracts were frozen at -20 OC and defrosted overnight prior to analysis for NO3--N and NH4+-N using a segmented flow analyzer (SFA, Model 305D, Astoria Pacific International, Clackamas, OR). Salt-extractable organic carbon (SEOC) samples were extracted similarly (in 2 M KCl, 1:5 soil extractant ratio), and filtered through a 0.45-μm membrane filter (Millipore Corp, USA) prior to storage at -20 C (Chantigny et al., 2008). The SEOC in the extracts were analysed by Aurora 1030W OI Analytical TOC analyzer (OI Analytical, USA). Calibration standards were made from potassium hydrogen phthalate in 2 M KCl. Calibration standards and samples were diluted 1:12 with ultrapure water to keep the chloride level to approximately 1% to prevent interference effects. 2 ml of the sample were reacted with 2 ml of 5% phosphoric acid and 2 ml of 10% sodium persulphate at 98 OC to oxidize and liberate the organic carbon and measure it as carbon dioxide.     18  Total C and N content were measured by combusting 15-20 mg finely ground air dried sample using Costech 4010 Elemental Analyzer with thermal conductivity detection. CEC was measured using an ammonium acetate extraction buffered to pH 7. Soil pH and EC were measured by pH-meter (WTW inoLab pH 7200) and EC-meter (WTW inoLab Cond 7200) on an extract from 1:2 (w/v) deionized water to finely ground air-dried soil which was sifted through a 2.00 mm sieve. Additional soil sampling and nutrient availability assessment using soil and ion exchange resin sampling is reported in Hannam et al, 2016. In-situ volumetric soil water content (0-30 cm depth) and temperature (at 2 cm and 10 cm depth) for each plot were measured continuously at 1-h intervals using 30 cm length time domain reflectometry (TDR) probes installed vertically (Campbell Scientific,CS616) and type-T thermocouples, respectively, monitored by Campbell Scientific CR1000 data logger. All TDR probes and thermocouples were installed permanently at the center of each plot where N2O flux was measured and kept undisturbed throughout the experiment. Soil bulk density samples were collected at 15 cm and 30 cm away from the dripper/micro-sprinkler towards the alley to represent the fertilized strip, and at the center of the alley, from samples from 3 to 9 cm depth using 6 cm diameter copper collars that were driven into the soil using a soil bulk density sampler. An estimate of the fraction of water-filled pore space (WFPS) was calculated as:                        WFPS = (θv /[1-(ρb/ρp)])                                        Equation 3.1                              Where ߠ௩ the volumetric water is content of the soil (cm3cm-3), ߩ௕ is the bulk density of the soil (g cm-3) and ߩ௣ is the approximate mineral particle density (2.65 g cm−3). Measurement of volumetric water content was conducted in the growing season as well as winter period. However, during winter the Campbell CS616 sensors measured the portion of water in soil that existed only in liquid phase (i.e. excluding the ice phase portion of water in the soil). Therefore, the soils   19  WFPS during winter were likely underestimated. Thus, only the WFPS data collected during the growing season of each year were used to determine the statistical differences in WFPS among treatments. 3.3 N2O flux measurements Emissions of N2O were measured in three replicates at the three middle blocks (from block 3 to 5) comprising all soil amendments except the treatment containing urea plus phosphorus, potassium and boron (PKB) (Figure A.1); replicates in other blocks were dedicated for parallel studies of carbon isotopes in carbon dioxide emissions. The gas flux sampling strategy was designed to capture high N2O emissions over short time periods caused by triggering events such as fertilization, irrigation, mulching, and major weather events such as spring thaw and intensive rainfall. The regular monitoring schedule involved sampling twice a week during fertigation, once a week during irrigation, and once every second week in winter. Sampling frequency was increased to two or three times a week around short-term weather and management events such as compost and mulch application, irrigation initiation, spring thaw or intensive rainfall. Regular flux monitoring started in January 2013. There were a total of 48 rounds of sampling each year, in 2013 and 2014. Fluxes were measured using non-flow through non-steady-state (NFT-NSS) chambers (Rochette and Eriksen-Hamel, 2007). Rectangular stainless steel frames (0.68 m x 0.56 m x 0.15 m depth) were installed to a depth of 0.13 m and left undisturbed for the duration of the experiment. At sampling, an insulated vented lid (with top closed, headspace ~0.11m) was tightly fitted to the frames. The size and location of the chamber within the plot were chosen to capture a representative emission footprint by taking into account the width of the alley and row (fertilized strip), the location of vines, and the drippers/ micro-sprinklers (Figure 3.1). To prevent the distortion of soil water distribution that may be caused by placement of the chamber collar,   20  the 4 L h-1 drippers adjacent to each chamber edge were replaced by two half-capacity smaller drippers (2 L h-1), one dripping into the chamber at the inside edge and the other dripping on the outside edge of the chamber hence in the adjacent “quadrant” on the opposite side of the row (Figure 3.1A).  Gas samples were collected using pre-evacuated 12 ml double-wadded Exetainers (Part No: 737W, Labco Ltd) that were evacuated to 200 mTorr, flushed with helium, and re-evacuated to 200 mTorr. During sampling, the Exetainers were over-pressurized by injecting 20 ml of sample using a 25-ml gas-tight glass syringe (catalog No. 03 378 207, National Scientific, Rockwood, TN). Samples were taken at 0, 7, 14, and 21 min in the summer when the magnitude of N2O flux was usually high. Nitrous oxide flux was usually low in winter and hence chamber deployment duration and sampling intervals were extended to allow buildup of enough gas in the chamber headspace. Samples were taken at 0, 10, 20, and 30 min in the winter. The samples were accompanied by three low and three high field standards (containing 0.220 ppm and 2.20 ppm N2O-N), which were prepared on the sampling day. Nitrous oxide concentrations were analyzed within one week of the sampling date using a gas chromatograph (Bruker 456 GC, Bruker) equipped with an electron capture detector (ECD) and a CTC Combi-Pal auto sampler (CTC Analytics AG, Zwingen, Switzerland). The soil-surface N2O fluxes were determined by calculating slope (dC/dt) of concentration (C) vs time (t) at chamber closer (t=0) via either linear or non-linear regression (as appropriate) using the equations of Rochette and Hutchinson (2005). Non-linear regression (Hutchinson and Livingston, 1993) was used when the accumulation of N2O decreased with time. Linear regression was used when the accumulation of N2O was consistent with time (Rochette and Eriksen-Hamel, 2007). Climate data (relative humidity, air temperature, and pressure) for flux calculations were obtained from a weather station located   21  within 0.5 km (Environment Canada, 2014b). Treatments were applied on the row functional unit only (Figure 3.1) and hence unless mentioned otherwise treatment comparison was based on measurements of N2O emission from the row. The alley functional unit did not receive treatment and hence represented an approximate N2O flux of the rest of the field, measured by six chambers deployed at six alleys; the alley orchard grass mix is not natural vegetation and so fluxes do not represent a true background value. The N2O emission measurements from alley were only used to calculate the emission factors as discussed below.  Vineyard-scaled emissions factors (EFs), uncorrected for background emission for each treatment, were expressed as the percentage of the applied N emitted as N2O-N using:          EF = (∑N2Oweighted/Applied available Nsource) x 100%           Equation 3.2                              Where: ∑N2Oweighted  is the area weighted annual cumulative N2O emission of a field                 (kg N2O-N ha–1) calculated by weighting row and alley fluxes by the area covered by the row and alley (each accounting for 50% of the field); and Applied available Nsource is the application rate of N fertilizer (40 kg N ha–1 ).  3.3.1 Yield-scaled N2O emissions Harvesting occurred on October 29, 2013 and October 20, 2014. Grape yield was determined from three central vines in every plot and extrapolated to a per hectare yield              (kg ha-1). Grape yield and fruit quality are a subject of separate future publication and are not presented here; but grape yield was used here to calculate yield-scaled N2O emissions (g N Mg-1) for the treatments by dividing the annual commutative N2O emissions (g N ha–1 year–1) by grape yield (Mg ha–1 year–1).     22  3.3.2 Data and statistical analysis Data were analysed to capture fluxes during (i) the spring thaw in the pre-growing season (PreGS) (January through April); (ii) the growing season (GS) (May through October); (iii) the period between harvest and the first ground thaw (post-harvest or post-growing season [PGS]: November and December); and (iv) annually (January through December). Area-scaled ∑ N2O for individual plots were calculated using linear interpolation of flux rates between sampling days (Millar, 2012) and by extrapolation to per hectare of fertilized strip. To correct for both lack of normality and nonconstancy of the error variance, all data (except pH and WFPS in 2013 and, pH and ∑ N2O in 2014) were first transformed using Box-Cox transformations (Box and Cox, 1964) using the SAS transgress procedure, but all data are reported as untransformed means. Effects of treatments on N2O emissions and environmental variables were determined using the PROC MIXED procedure in SAS, with repeated measurements in the model option (Version 9.3; SAS® Institute, Inc., Cary, NC) and block, and block-by-irrigation treated as random effect. Model selection was conducted as described by Littell, et al. (2006). Since N2O emissions were measured over unequally spaced time intervals, a spatial power law (SP(POW)) covariance structure was used. The SP(POW) structure for unequally spaced longitudinal measurements provides a direct generalization of the autoregressive model (order one) for equally spaced measurements. The SP(POW) models the covariance between two measurements at times t1 and t2 as:    ttttCov YY  2121 2,                                                Equation 3.3 Where Y is a measurement, ρ is an autoregressive parameter assumed to satisfy |ρ| < 1 and σ2 is an overall variance. Similarly, the effects of treatments on ∑N2O were conducted using the PROC MIXED procedure, for individual years, and for the 2-year combined data with years as   23  repeated measures. Contrary to daily measurements, cumulative measurements were equally spaced in time (i.e repeated over a year) and compound symmetry covariance structure was used in the “type” statements of the repeated model to draw overall statistical comparisons among management factors imposed over two years. Pairwise means comparisons were performed using the PDIFF statement and Tukey-Kramer adjustment method. Unless otherwise mentioned,          a p-value <0.05 was used for fixed effects and means separation.  3.4 Results 3.4.1 Weather, soil WFPS and temperature Environmental conditions in the two experimental years were typical of the region. The mean daily air temperature in 2014 (9.8 OC) was slightly higher than 2013 (9.6 OC) which was the same as the 30-year average (9.6 OC) (Table 3.1). Higher total precipitation with higher proportion during the fertigation period occurred in 2013. The largest individual rainfall events occurred in 2013, where three events occurred (June 18, 12 mm; June 20, 26 mm; June 24, 32 mm). In 2014, a single event of 35 mm was noted on June 13. The total irrigation applied in both years were similar. The freeze-thaw periods of the pre-GS of 2013 and 2014 had different characteristics (Table 3.1, Figure 3.2). The thaw period in the pre-GS of 2013 was shorter. Average daily air temperature increased linearly from below to above zero, followed by soil temperature. During the thaw period in 2014, average daily air temperatures fluctuated above and below zero three times in cycles lasting several days prior to staying above zero. There were more days in the PreGS of 2014 when air and soil temperature were below 0 OC.    24  Soil temperatures varied among treatments during the growing season but the mean differences were very small, and hence not visually differentiable when plotted (Figure 3.3). Near surface soil temperature was not affected by irrigation type while statistical differences were found for different N-sources (P<0.05) and floor management types (P<0.001). However, these differences were likely too small (< 0.6 OC) to drive any substantive changes in N2O emissions among treatments. Treatment and year did not affect soil bulk density. Two years after establishment of the treatments, soil bulk density remained similar across the vineyard averaging 1.31 gcm-3 (n = 54, two replicates per plot and one replicate per alley). These data were used to calculate WFPS.  During the GS of both years, WFPS varied significantly by irrigation (p<0.01) and floor management (p<0.01) but not by N-source (Figure 3.3). In 2013, the GS average WFPS for Drip (51.6%) > Micro-sprinkler (45.4%) and Mulch (51.6%) > Clean (45.4%) which was repeated in 2014 when the GS average WFPS for Drip (53.8%) > Micro-sprinkler (41.7%), and Mulch (50.4%) > Clean (45.1%). In both years, WFPS started to vary significantly by irrigation type and floor management approximately one week after irrigation started. However, differences in WFPS were lessened immediately following some isolated intense rainfalls events especially in late May and June of 2013 (Figure 3.2 and Figure 3.3). The difference in daily GS WFPS among the treatments was most pronounced in July and August when the highest soil and air temperatures were recorded. Mean GS WFPS in Drip-irrigated plots reached maximum of 59% and 64% in 2013 and 2014, respectively. Mean GS WFPS in Mulched plots had maximum of 61% in 2013 and 56% in 2014. Individual measurements in single replicates were higher.     25  3.4.2 Daily N2O emissions The temporal patterns of N2O emissions (Figure 3.4) were similar across treatments, from within the alley, and between years. N2O emission spikes were observed mainly within two periods: during freeze-thaw cycles in the PreGS and during the fertigation period in the GS of each year. Emission during the PGS was negligible. There was only one noticeable difference in the pattern of N2O emission between years, observed in July and August 2013, when there was a N2O emission spike under Drip but not under Micro-sprinkler; a similar difference was not observed in 2014. Treatment effects on daily N2O emissions were not consistent across seasons within a year and between years (Table 3.2). During the PreGS in 2013, Floor management and Date accounted for most of variation in N2O emissions. This caused lower N2O emissions in Mulched than Clean plots in 4 of the 12 monitoring days (Figure 3.4). During the 2013 GS, N-source, Floor management and Date accounted for most of the variation in N2O emissions; N2O emissions under Micro-sprinkler was significantly lower than Drip in 4 of the 32 monitoring days (Figure 3.4A). In addition, N2O emissions in Compost plots were significantly lower than Urea plots in 4 of the 32 monitoring days (Figure 3.4B).  In 2014 most of the variation in N2O emissions across seasons was caused by irrigation and sampling date (Table 3.2). During the PreGS the N2O emissions in Mulched plots were significantly lower than Urea plots in 5 of the 12 monitoring days (Figure 3.4C). During the GS the N2O emissions under micro-sprinkler irrigation were significantly lower than drip irrigation in 6 of the 32 monitoring days (Figure 3.4A). Generally, enhanced N2O fluxes during the GS of both years were observed in the days when WFPS was usually greater than 45% (for example May 31, June 18, and June 25 in 2013 and June 18 in 2014) (Figure 3.3 and Figure 3.4).    26  3.4.3 Seasonal and annual cumulative N2O emissions  Treatment effects on ∑N2O emissions were not consistent across seasons and years     (Table 3). For example, in 2013 irrigation type resulted in a significant difference in the PreGS ∑N2O emissions but did not significantly affect the GS ∑N2O emissions. Irrigation type did not result in significant differences in the PreGS ∑N2O emissions of 2014, contrary to 2013. During the PreGS of 2013, area-scaled ∑N2O emissions were: Drip < Micro-sprinkler; and Mulch < Clean. During the GS, area-scaled ∑N2O emissions were: Compost < Urea; and Mulch < Clean. Overall, the 2013 annual area-scaled ∑N2O emissions were: Micro-sprinkler ≈ 0.56 x Drip         (p = 0.118); Compost ≈ 0.67 x Urea (p = 0.027); and Mulch ≈ 0.59 x Clean (p = 0.003) but only the differences from N-source and floor management were significant. None of the simple treatment factors caused significant effects on 2014 seasonal and annual area-scaled ∑N2O emissions (p >0.05). The relative contribution of seasons towards the total annual ∑N2O varied across years (Table 3.3). In 2013, the majority (62%) of the annual ∑N2O emissions occurred during the GS, followed by PreGS (37%) and PGS (1%) whereas in 2014 the majority of N2O emission occurred during the PreGS (61%) followed by GS (32%) and PGS (7%) (Figure 3.4 and Table 3.3). The average N2O emissions in all the treatments in the PreGS of 2014 (0.95 kg ha-1) was much higher than 2013 (0.52 kg ha-1) while the emissions in the GS of 2013 (0.81 kg ha-1) was much higher than 2014 (0.49 kg ha-1). On average, the annual cumulative N2O emissions in 2014 were over 14% greater than 2013.  The 2-year averaged annual area-scaled ∑N2O emissions were: Micro sprinkler < Drip; Compost < Urea; and Mulch < Clean (Table 3.4). Overall, area-scaled ∑N2O emissions were: Micro-sprinkler ≈ 0.71 x Drip (p = 0.20); Compost ≈ 0.82 x Urea (p = 0.15); and                      27  Mulch ≈ 0.72 x Clean (p = 0.02). However, only the difference in floor management type was statistically significant. Surface application of bark mulch decreased the 2-year mean ∑N2O emission by 28%. The effects of treatments on seasonal area-scaled ∑N2O emission were somewhat different from the annual ∑N2O emission over the two year period (Table 3.4). During the GS, irrigation treatment resulted in lower area-scaled ∑N2O emissions in Micro-sprinkler than Drip. Floor management treatment caused lower area-scaled ∑N2O emissions in Mulch than Clean only in the PreGS. As the majority (49%) of N2O emission over the two years occurred during the PreGS (when there was no irrigation), only floor management effects were significant when all (preGS, GS, PGS) seasonal ∑N2O were added together. Hence, there was lower area-scaled ∑N2O in Mulch than Clean over the two year period. Emission factors (N2O emissions per unit of total N applied uncorrected for background emission) averaged 2.8% and ranged between 2.53% to 3.14% across treatments (Table 3.4). All treatments received an estimated available N-rate (40 kg ha-1 year-1) which means the emission factors were affected in a similar way as the annual area-scaled ∑N2O emissions. There were no significant differences in the emission factors caused by irrigation type or N-source. However, floor management caused a significant difference; the emission factor for Mulch was lower than Clean (p<0.05). Yield-scaled ∑N2O emissions for Mulched plots were lower than Clean plots by 23%. Hence, under the conditions of this experiment, production of an equivalent amount of grape yield using mulch would generate lower N2O than production without mulching.     28  3.4.4 Soil nutrients and chemistry In both years, soil NO3--N concentrations were not affected by irrigation or N-source (Table 3.5). Soil NO3--N concentrations were significantly affected by floor management as soil NO3--N concentrations in Clean plots were 4 times higher than Mulched plots (p<0.0001). On average, soil NO3--N concentrations increased by 58% from 2013 to 2014.  In 2013, none of the management factors affected soil NH4+-N concentrations (Table 3.5). In 2014, soil NH4+-N concentrations in Compost and Mulch plots were significantly higher than Urea and Clean plots, respectively. Most of the mineral N in soil was in the form of NO3--N (83% averaged across all treatments over two years); soil NH4+-N concentrations were low (mostly less than 10 mg kg-1). On average soil NH4+-N concentrations increased by 35% from 2013 to 2014. In both years, soil SEOC concentrations were not affected by the type of irrigation imposed (Table 3.5). However, the soil SEOC concentrations in Compost and Mulch plots were about 1.6 (p < 0.0001) and 1.4 times higher (p < 0.05) than Urea and Clean plots, respectively. On average soil SEOC concentrations increased by 12% from 2013 to 2014. In 2013, only the N source influenced the soil pH; the pH in Compost plots was higher than Urea plots (Table 3.5). In 2014 floor management affected pH in addition to N-source; the pH in Clean plots was higher than Mulch plots. The soil became more acidic between 2013 and 2014; soil pH was 7.4 in 2011 before treatment initiation and pH averaged 7.2 and 6.9, in 2013 and 2014 respectively, across all treatments.  In both years, soil EC was not affected by the type of irrigation used. However, the soil EC was significantly affected by N-source (p<0.01) and floor management (p<0.0001). Soil EC   29  measurements were Compost > Urea and Clean > Mulch plots. On average soil EC increased by 47% from 2013 to 2014, consistent with the increment of measured ionic nutrients (NO3--N and NH4+-N concentrations). 3.5 Discussion 3.5.1 Seasonal N2O emissions: thaw and the pre-growing season A characteristic pattern of N2O emission during two principle periods of higher emissions was observed in both years for all treatments: during freeze-thaw cycles in the PreGS and during fertigation in the GS. On average, a considerable portion (37% in 2013 and 61% in 2014) of the annual cumulative N2O emission occurred during the PreGS particularly during the thaw period. Prior studies in woody perennial crops expected minimal N2O emission in winter and hence focused their N2O monitoring only during the GS (Smart et al., 2011; Suddick et al., 2011). However, similar to this study, significant winter N2O emissions were observed for other cropping systems in climates that experience spring ground thaw in western Canada (Nyborg et al. 1997; Lemke et al. 1999; Izaurralde et al., 2004 ) and elsewhere (Müller et al., 2002; Holst et. al, 2008; Tatti et al. 2014).  In a recent review, Risk et al. (2013) elucidated two mechanisms for enhanced N2O emissions during the thaw period: (1) the physical release of N2O produced throughout the winter by the removal of ice blocking soil pores; and (2) the emission of newly produced N2O at the onset of thaw due to increased biological activity and changes in physical and chemical soil conditions. There was over a month of heighted N2O emission during the PreGS in both years in this study, suggesting that the physical release of N2O produced during the winter is likely low and so the contribution of this mechanism to the total cumulative PreGS N2O emissions in this system is likely minor.   30  The occurrence of enhanced denitrification by the second mechanism during freeze-thaw depends mainly on the prevalence of anaerobic conditions (Groffman, et al. 2009). During freeze-thaw events, it is likely that WFPS exceeded the WFPS threshold needed to create anaerobic conditions resulting in N2O emission spikes. WFPS in this study appears low (<45%) during freeze-thaw cycles but the actual WFPS was likely higher in the top 0-5cm of the soil, as the majority of the 30 cm long TDR probe was still under frozen ice at deeper depth, and any frozen water would not be measured. Spring emissions may also depend on the bacterial activity, and availability of substrate. Increased abundance of cold-adapted nitrifier and denitrifier communities in late winter, especially in soils receiving organic amendments, has also been suggested to cause thaw period N2O pulses (Tatti et al., 2014). Cell lysis in plant roots and soil microbes on freezing (Herrmann and Witter 2002) may also provide fresh substrates for surviving microbes. This mechanism may enhance the level of denitrification by both fueling denitrifiers and depleting O2 (Mørkved et al, 2006).  Some studies link greater spring N2O emissions following lower temperatures during early winter and attribute this to more cell lysis (Neilsen et al., 2001; Koponen and Martikainen, 2004). In our study, emissions in the pre-GS of 2014 were 83% higher than 2013, following a colder winter than 2013. The lower air temperatures measured prior to thaw in 2014 (lowest recorded winter temperature = -12.7 OC) than in 2013 (lowest recorded winter temperature= -8 OC) may indicate greater cell lysis during winter. The 2014 thaw period was also longer in duration and included multiple freeze thaw cycles, which may have promoted additional cell lysis during the thaw period.    31  3.5.2 Seasonal N2O emissions: Growing Season  Total growing season emissions were reduced between 2013 and 2014 despite a substantial increase of the mean growing season soil NO3--N and NH4+-N concentrations. The difference in the growing season N2O emissions was likely related to the dynamics of soil mineral N and moisture. During the GS of 2013, the highest daily fluxes (19-29 N ha-1day-1) occurred following intense rainfall that coincided with the end of fertigation when soil nitrate was at a maximum, creating potential for high water saturation, low oxygen diffusion rate, conditions suited to denitrification. There was no such concurrent occurrences of intense rain and enhanced soil mineral N in the growing season of 2014. The highest 2014 daily fluxes recorded around mid-fertigation period during the growing season and the values (7-12 N ha-1day-1) were much smaller than 2013. The distribution of nitrogen within plots may have contributed to the N2O emission spike seen under drip, but not micro-sprinkler, irrigation in July and August 2013. The wetter zone under drippers has higher root densities (Neilsen et al., 1997) and higher N concentrations during fertigation. The wetter zone expands over the fertigation season as more water is applied thereby expanding the flux zone as a result of the transport of N and degradation of organic matter away from the drippers (Sanchez-Martin et al., 2008; Sanchez-Martin et al., 2010). When fertigation ceases, but irrigation continues, the continued addition of fresh water via the dripper may transport N both laterally and downwards (Hanson et al., 2006) away from the zone of root concentration under the dripper and towards the plot margins where the N is less likely to be taken up. In Drip plots, the N component of lateral transport may be available near the soil surface for denitrification after fertigation ceases. In contrast, the flux zone under micro-sprinkler is more uniform during fertigation since nitrate and roots are more evenly distributed. Residual N   32  in soil at the end of micro-sprinkler fertigation is pushed downwards into the soil by additional irrigation water. Most of N2O generated at deeper depths from residual N is more likely to be consumed during diffusive transport to the soil surface (Kellman and Kavanaugh, 2008).  There were several intense rain events at the end of fertigation in late June 2013, when soil nitrate was maximum; it was noted the rainfall intensity exceeded the infiltration capacity and was sufficient to cause runoff from the rows towards the alleys, following the slightly mounded shape of the soils in the rows. This may have facilitated greater lateral movement of nitrate away from the drippers in that year, moving N earlier into soil with lower N uptake by roots, and increasing the zone of N2O emissions. In micro-sprinkler, lateral runoff would not distribute N to previously N free areas, and did not result in increased emissions. This emission spike in July and August 2013 in drip irrigation was consistent with increases in soil NO3--N availability (determined biweekly using ion-exchange resins) under drip irrigation later in the season, while no similar spikes in soil NO3--N availability were observed under micro-sprinkler (Hannam et al., 2016). No such similar N2O spikes were noted in the irrigation season of 2014. There was only one intense rainfall during fertigation in that year, occurring earlier in the season (June 13) well before the soil nitrate was expected to reach its maximum at the end of June. This may not have been sufficient to cause lateral migration of nitrogen away from the dripper. 3.5.3 Inter-annual variations Cumulative N2O emissions in 2014 were over 14% higher than in 2013. The analysis of seasonal effects indicates that the majority of the inter-annual variation relates to the thaw period, during which emissions increased between years by 83%.  The growing season emissions were reduced by 40% between 2013 and 2014. The variability in the total amounts of natural precipitation between 2013 and 2014 had little direct effect on the inter-annual variation on N2O   33  emission as irrigation additions resulted in WFPS being similar during the GS of both years. The intense rain events that occurred when soil nitrate was at its maximum around the end of fertigation in 2013 likely caused the higher GS season emission in that year. There was also inter-annual variations in nitrogen availability, soil temperature and soil pH. Soil NO3--N and NH4+-N concentrations both increased substantially from 2013 to 2014. The increase in the soil mineral-N may have been driven by two factors: accumulation of soil mineral-N when fertilizer application exceeded plant demand and/or changes in the total denitrification activity due to decreasing pH. The majority of the applied N (either urea or compost) in this system would first undergo ammonification, which involves enzymatic conversion of organic forms of N to NH4+. Some of the NH4+ would have been directly absorbed by the plant while the majority of the remainder would be oxidized to NO2- and then to NO3-, the most suitable nitrogen form for plant growth (Miller and Cramer, 2005; Rothstein and Cregg, 2005). Ammonium oxidation is an acidifying process (NH4+ + 1.5O2             NO2- + H2O + 2H+) (Van Dongen et al., 2001); which is consistent with the observed decrease in soil pH over four years. This decrease in pH thus has a potential to cause accumulation of soil NO3- as decreased pH would lead to reduced complete denitrification to N2. Thus decreasing soil pH may promote the increase in soil NO3- which may eventually lead to higher N2O amounts on an annual basis by increasing N availability in thaw periods and intense rainfall events. The results of this study match others (Schellenberg et al., 2012; Asgedom et al., 2013; Xie et al., 2014) who observed a positive relationship between soil mineral-N and N2O emission. The variability in the amounts of precipitation between 2013 and 2014 might have had little direct effect on the seasonal and interannual variation on N2O emission as WFPS was similar during the GS of both years. In addition, the 60% WFPS denitrification threshold was   34  exceeded similarly in both years; 14% of the time in 2013 and 17% of the time in 2014. In irrigated cropping systems, soil moisture is affected both by natural rainfall and the amount of irrigation applied. Total precipitation in 2014 was slightly lower than 2013, with the majority of the precipitation occurring in the GS of both years. Irrigation amounts were similar in both years, meaning that total water input was lower in 2014 than 2013. Despite this, the mean GS WFPS in the two years were similar. The lack of effect of total precipitation on  mean GS WFPS in the two years may be partly due to irrigation frequency (as water is applied four times daily based on the previous day’s evapotranspiration).  Overall, the 14% interannual variability in the N2O emissions in this study is within the range of Verhoeven et al. (2014) who observed  2-58% variability of N2O in a 2-year study of a wine grape system amended with N (organic and inorganic) and biochar in California. The Merlot vineyard in this study was only 3-years old; roots and vines may have still been establishing. There was a reduction in the variability of N2O emissions between treatments between the second and third year of the vineyard history. The pH, soil NO3--N, NH4+-N, SEOC and EC changing over the years may imply that the soil is not at steady state and hence may indicate the need for longer term N2O monitoring.  3.5.4 Soil and water management effects on N2O emissions Surface application of bark mulch decreased 2-year mean ∑N2O emission by 28%. To the best of my knowledge there is only one published study, Livesley et al., (2010), that investigated the effect of bark mulch on N2O emission using direct manual chamber measurements at the field scale. Livesley et al., (2010) observed 50% lower ∑N2O emissions from wood-chip mulched irrigated garden than irrigated lawn in Australia, but did not compare garden with and without wood chip mulch.    35  A combination of factors may be responsible for the lower N2O emission in bark Mulch plots compared to Clean plots. Under the bark mulch in this study, there was significantly higher organic carbon similar to other studies (Livesley et al., 2010; Neilsen et al., 2014). This study also found higher WFPS, and lower soil nitrate concentration, which complements the lower available nitrogen measured by soil exchange resins found by Hannam et al (2016). The higher WFPS and added carbon substrate may have created favourable anaerobic conditions for enhanced complete denitrification to N2, resulting in lower ∑N2O emissions under the bark mulch. Microbial immobilization of mineral N increases in the presence of an available labile carbon source (Homyak et al., 2008); hence, the reduced mineral-N availability under mulch could have been a result of microbial immobilization (Homyak et al., 2008). Lower levels of mineral N might have lowered the ∑N2O emissions under mulch even further.  Similarly, the lower ∑N2O emission in Compost than Urea in 2013 may be partly attributed to enhanced complete denitrification because of the higher soil carbon substrate in Compost plots. This result was similar to those reported by Alluvione et al. (2010) who found lower N2O emissions for municipal solid waste compost than mineral fertilizer in a silt loam soil under a temperate subcontinental climate. In both the 2014 and the combined 2-year data, there were no differences in annual ∑N2O emissions between compost and urea plots. The increase of EC in soil under compost by 41% from 2013 to 2014 might have partially limited the potentials of higher soil carbon under compost to enhance complete denitrification to N2, as increased EC has been reported to reduce the activities of N2O reductase (Ruiz-Romero, et al, 2009).  Micro-irrigation resulted in differences in 2-year mean ∑N2O emission only during the growing season; ∑N2O emission under micro-sprinkler was 29% lower than drip irrigation. This difference was likely caused by increased frequency of high WFPS in Drip over Micro-sprinkler   36  plots. Bateman and Baggs (2005) observed higher emissions with WFPS > 60%. During the growing season, the Drip plots exceeded 60 % WFPS 21 % and 30% of the time in 2013 and 2014, respectively, compared to Micro-sprinkler plots where WFPS was recorded above 60% only 7%  and 4 % of the time in 2013 and 2014, respectively, as shown in Appendix A (Figure A. 2 and A.3). These water contents are measured at 30 cm lateral distance from the location of the dripper and thus water contents directly under the dripper can be expected to be higher. Those under micro-sprinkler irrigation are representative of the whole plot. The effect of irrigation on subplot-scale N2O emissions (Chapter 5) and on microbial pathways for the production of N2O was investigated in parallel studies. Early results of the microbial study indicate that drip irrigation reduces the abundance of N2O reducers (abundance of nosZ genes) compared to irrigation with micro-sprinklers (Voegel, personal communication). The higher WFPS observed under drip irrigation might have limited the activity of the enzyme nitrous oxide reductase (nosZ) to convert N2O to N2 gas, thus resulting in higher N2O emissions. The higher N2O emission with drip irrigation in this study was similar to the findings of Smart et al. (2011), who observed approximately 60% lower ∑N2O emission under fanjet sprinkler irrigation than drip irrigation in a Mediterranean climate during the growing season. Their vineyard was irrigated twice a week and received 34 kg N ha-1 year-1. The N2O mitigation potential of micro-sprinkler irrigation was similar in this study. However, Smart et al.’s (2011) measurements lasted for only a month in the fall and another month in spring during fertigation and the longer-term N2O mitigation potential of micro-sprinkler in their system is unknown. The findings of major pre-growing ∑N2O emissions, and the significant effect of micro-irrigation in the 2-year mean growing season ∑N2O emission (but not annual) in this study indicates the importance of long-term (≥ 2 year) continuous year round monitoring across seasons.    37  3.5.5 Yield-scaled N2O emission and N2O emission factor  Studies that only consider area-scaled N2O emission cannot be used to recommend management practices that are suitable for mitigation of N2O emission without compromising agricultural productivity. In this study, both area-scaled N2O emission and yield-scaled N2O emissions were significantly decreased by surface application of bark mulch. Surface application of bark mulch reduced 2-year mean yield-scaled N2O emissions by 23%. Thus bark mulch can be used to mitigate N2O emission without compromising productivity. To the best of my knowledge, this study represents the first report of this metric (i.e yield-scaled N2O emissions) being generated for a grape vineyard. Grapes used for wine production have a more complex relationship between production mass and eventual total wine value (i.e., increased yield may not be as important as optimal fruit quality parameters); further exploration of yield-scaled or value-scaled emissions may be needed. From the sandy loam soil across treatments over 2 years, the mean fraction of applied N lost as N2O (N2O emission factor), uncorrected for background emission, in this study ranged from 2.53 to 3.14%. The true emission factors (i.e corrected for the background emission) were not calculated because there was no control plot as vines with no treatment (such as irrigation) would not survive in the semi-arid climate of this study area. Thus true emission factors would likely have been slightly lower than these values. Emission factors in this study were at least two times greater than the 1% default direct emission factor of the Intergovernmental Panel on Climate Change (IPCC) methodology for calculating N2O emission rates from agriculture irrespective of fertilizer and soil type (IPCC, 2006). The 1% IPCC default direct emission factor may be overly simplistic for micro-irrigated systems receiving fertigation and organic amendments. In woody perennial crops water and nitrogen are usually applied in smaller area of   38  soil close to the vine/tree compared to annual crops. This practice increases nitrogen and water use efficiency. However, this also leads to increased localized concentration of nitrogen especially in drip-irrigated system as compared to other applications such as broadcast and furrow irrigation (Smart et al., 2011). Hence, there is a higher potential for enhanced denitrification and the 1% IPCC emission factor used to calculate N2O emission from agriculture would likely underestimate emissions from woody perennial crops. 3.6 Summary This study demonstrated the important contribution of the pregrowing season, particularly the thaw period, to the annual N2O emission in vineyards under a semi-arid climate with cold winters. Disregarding this period could lead to either a serious underestimation of annual N2O emission or/and bias in treatment comparisons because effects could be variable across seasons in a year. More work is needed in climates with and without winter freezing temperatures to assess annual flux changes. Two year results indicate that micro-irrigation type affected growing season emissions, but not annual emissions. Nitrogen sources used in our study did not affect N2O emissions significantly (p > 0.05). Surface application of bark mulch in vineyards can reduce both area-scaled and yield-scaled N2O emissions. The mechanism by which bark mulch reduces N2O emissions is not clear and further investigation using isotopic and/or molecular techniques is warranted. Further work is also required to determine the life-cycle consequences of carbon emissions from the bark mulch on the overall greenhouse gases emission. Our study has one of the longest monitoring durations for experiments examining the effects of micro-irrigation, N-source and mulching in a fruit production system. However, two years may still be a short time to fully understand long-term changes as C and N status of the soil changed over time, particularly in response to bark mulch decomposition.    39  Table 3.1: Summary of climate and soil data during experimental years 2013 and 2014 including thaw dates, precipitation, irrigation, and average air and soil temperatures. Data presented during the pre-growing season (PreGS, January through April), the growing season (GS, May through October), and post-growing season (PGS, November and December).  z Data separated by “/” indicates either thaw period/rest of PreGS totals or fertigation period/irrigation period totals y Total water inputs = Total precipitation + Total irrigation x Soil temperature was measured at 2cm depth   Parameter 2013 2014PreGS GS PGS PreGS GS PGSDate of first thaw Jan. 9th  - - Jan. 10th  - -Date of last thaw Feb. 20th - - Mar. 4th - -Total precipitation (mm) z 38/55 137/104 36 22/43 74/116 71Total irrigation (mm) - 56/430 - - 82/423 -Total water inputsy 93 728 36 65 694 71Avg. soil temperaturex (OC) 3.1 17.5 0.6 3.7 18.2 1.6Avg. air temperature (OC) 3.5 17.2 -1.1 2.6 17.5 0.8No of days soil temperature < 0 OC 36 0 36 40 0 20No of days air temperature < 0 OC 29 0 37 40 0 23No of days soil temperature < -5 OC 7 0 1 0 0 2No of days air temperature < -5 OC 6 0 12 14 0 9  40  Table 3.2: F-values of repeated measures analyses of daily N2O emissions in the vineyard during the pre-growing season (PreGS, January through April) and during the growing season (GS, May through October) in 2013 and 2014.  z Irrigation occurred from May 8 to October 23 in 2013 and from May 21 to October 23 in 2014. Urea was fertigated from May 14 to June 25 in 2013 and May 28 to July 7 in 2014 while compost was applied in late May in both years. y *, **, ***,**** indicate differences between treatments significant at p ≤ 0.05, 0.01, 0.001, 0.0001 respectively.  x Bark mulch was surface applied in late May of 2013 only.     Effecty GSz GSyF Value Num DF F Value Num DF F Value Num DF F Value Num DFIrrigation (I) 3.86 1 1.15 1 0.64 1 16.42* 1N-source (N) 0.59 1 5.43* 1 0.48 1 0.00 1Floor mgmt (F)x 7.00** 1 7.35** 1 0.19 1 1.54 1Dates (D) 33.29**** 10 26.10**** 31 30.69**** 11 8.9**** 31I x D 2.35* 10 2.15*** 31 0.56 11 2.21*** 31N x D 0.87 10 1.93** 31 1.31 11 1.11 31F x D 3.66*** 10 1.18 31 3.29* 11 1.45* 31PreGS PreGS2013 2014  41  Table 3.3: Effects of irrigation type, nitrogen source and vineyard floor management on seasonal and annual N2O emissions during the pre-growing season (PreGS, January through April) and during the growing season (GS, May through October) in 2013 and 2014.  zMean N2O emission calculations for treatments were based on a 1.5m wide fertilized strip.  y Numbers in parentheses following mean are standard error of the mean, followed by different lowercase letters within columns indicating differences of least squares means using the Tukey-Kramer adjustment, at p<0.05. x *, **, ***, **** and ns indicate that means are significantly different at p ≤ 0.05, 0.01, 0.001, 0.0001 or are not significantly different, respectively.      Effect2013 2014PreGS GS Annual PreGS GS AnnualIrrigation(I)Sprinkler 0.27(0.03)ay 0.65(0.09) 0.95(0.11) 0.92(0.14) 0.43(0.06) 1.45(0.18)Dripper 0.76(0.19)b 0.97(0.17) 1.74(0.34) 0.97(0.14) 0.54(0.05) 1.63(0.19)N-source(N)Compost 0.41(0.09) 0.64(0.08)a 1.06(0.16)a 0.97(0.14) 0.46(0.05) 1.53(0.18)Urea 0.61(0.19) 0.99(0.17)b 1.63(0.34)b 0.93(0.14) 0.52(0.06) 1.55(0.19)Floor mgmt(F)Mulch 0.29(0.08)a 0.68(0.12)a 0.98(0.16)a 0.86(0.13) 0.49(0.06) 1.45(0.17)Clean 0.73(0.18)b 0.95(0.15)b 1.71(0.33)b 1.03(0.15) 0.49(0.06) 1.63(0.20)Pr > F xIrrigation(I) ** ns ns ns ns nsN-source(N) ns * * ns ns nsFloor mgmt(F) *** * ** ns ns nsI x N ns ns ns ns ns nsI x F ns ns ns ns ns nsN x F ns ns ns ns * nsI x N x F ns ns ns * ns *              N2O emission ((kg ha-1)z  42  Table 3.4: Effects of irrigation type, nitrogen source and vineyard floor management on 2-year mean seasonal and annual N2O emissions, 2-year mean N2O emissions factor (EF, N2O emissions per unit of total N applied), and 2-year mean yield-scaled N2O emissions (N2O/yield). Year was included as a repeated measure factor in the ANOVA.  zMean N2O emission calculations for treatments were based on a 1.5m wide fertilized strip  y Numbers in parentheses following mean are standard error of the mean, followed by different lowercase letters within columns indicating differences of least squares means using the Tukey-Kramer adjustment, at p<0.05. x *, **, ***, **** and ns indicate that means are significantly different at p ≤ 0.05, 0.01, 0.001, 0.0001 or are not significantly different, respectively.            Effect N2O emissions (kg ha-1 [season or year]-1 )z N2O Yield-emission scaledfactor N2OPreGS GS PGS Annual (%) ( g Mg-1) Irrigation(I)Sprinkler 0.60(0.08)y 0.54(0.08)a 0.06(0.01) 1.20(0.14) 2.53(0.13) 51.6(6.3)Dripper 0.86(0.17) 0.76(0.11)b 0.07(0.01) 1.69(0.27) 3.14(0.24) 54.9(5.8)N-source(N)Compost 0.69(0.11) 0.55(0.07) 0.06(0.02) 1.30(0.17) 2.65(0.15) 50.1(4.6)Urea 0.77(0.17) 0.75(0.12) 0.06(0.01) 1.59(0.27) 3.02(0.24) 56.5(7.2)Floor mgmt(F)Mulch 0.58(0.10)a 0.58(0.09) 0.06(0.01) 1.21(0.17)a 2.55(0.15)a 46.3(5.7)aClean 0.88(0.16)b 0.72(0.11) 0.06(0.01) 1.67(0.26)b 3.12(0.24)b 60.3(6.1)bPr > F xIrrigation ns ** ns ns ns nsN-source ns ns ns ns ns nsFloor mgmt ** ns ns * * *Year *** *** **** * * ns  43  Table 3.5: Effects of irrigation type, nitrogen source and vineyard floor management on mean extractable NO3--N, NH4+- N, salt-extractable organic carbon (SEOC), pH  and electrical conductivity (EC) of soil (0-15cm depth) during the growing season (May through October) in 2013 (n= 96) and 2014 (n=144) (April through October).   z Numbers in parentheses following mean are standard error of the mean, followed by different lowercase letters within columns indicating differences of least squares means using the Tukey-Kramer adjustment, at p<0.05.*, **, ***, **** and ns indicate that means are significantly different at p ≤ 0.05, 0.01, 0.001, 0.0001 or are not significantly different, respectively.     44   Figure 3.1: Spatial representation of a single vine functional unit in: (A) Drip irrigated plot, and (B) Micro-sprinkler irrigated plot. “x” and “y” represent examples of soil sampling locations for row chamber and alley chamber for one sampling event, respectively. Similar locations relative to other experimental vines (not shown) in the same plot were sampled for each soil sampling event in May, June, August, and October in 2013 and every month from April to August, and October in 2014.  4L/h  dripper Two adjacent 2L/h drippers         45   Figure 3.2: Air and soil temperature, precipitation and irrigation inputs for 2013 and 2014. “Fertig” in the top insert represent periods of fertigation, the application of nitrogen through the irrigation system.              46   Figure 3.3: Soil water-filled pore space (WFPS) and soil temperature over 2013 and 2014 across: (A) irrigation type (B) nitrogen source and (C) orchard floor management. Bars indicate standard error of the mean. *, **, *** indicate differences of least squares means of WFPS using the Tukey-Kramer adjustment, at p < 0.05, p < 0.01, p < 0.001, respectively. Differences of least squares means of temperature are not shown. “Fertig” in the top insert indicate fertigation, the application of nitrogen through the irrigation system.         47   Figure 3.4: Mean daily measurement of N2O emissions in 2013 and 2014 as averaged by: (A) micro-irrigation type and alley position (B) nitrogen source and (C) orchard floor management. Plus capped bars indicate standard error of the mean (n= 12). *, **, ***, **** indicate differences of least squares means using the Tukey-Kramer adjustment, at p < 0.05, p < 0.01, p < 0.001, p < 0.0001 respectively. “Fertig” in the top insert represent periods of fertigation, the application of nitrogen through the irrigation system.         48  Chapter 4: EFFECT OF DRIP IRRIGATION FREQUENCY, NITROGEN RATE AND MULCHING ON NITROUS OXIDE EMISSIONS IN AN APPLE ORCHARD A version of this chapter has been submitted to the Journal of Agriculture Ecosystems & Environment and is in review. The lead author is Mesfin M. Fentabil and the co-authors are Craig F. Nichol, Melanie D. Jones, Gerry H. Neilsen, Denise Neilsen and Kirsten D. Hannam.  4.1  Background There are limited data on N2O emissions from apple orchards (Pang et al 2009), yet these systems cover more than 5.2 million ha worldwide (FAO, 2013). In Canada, apple orchards cover 15,000 ha (FAO, 2013), with over 3,200 ha located in the semi-arid Okanagan region of British Columbia (Seymour, 2015). To improve water-use efficiency, many apple orchards in the Okanagan have converted from overhead and under-tree sprinklers to under-tree micro-irrigation (micro-sprinkler or drip). This trend is expected to continue as demand for irrigation water increases due to climate change. When using under-tree micro-irrigation, nitrogen can be applied through the irrigation system (fertigation) in order to increase nitrogen use efficiency. However, fertigation also increases the localized concentrations of N and water, especially in drip-irrigated systems (Smart et al., 2011), and may increase the potential for N2O emissions (Zebarth et al., 2008; Smart et al., 2011). The effects of under-tree micro-irrigation and fertigation on N2O emissions are not well known.  This chapter presents the results of a two-year study in an apple orchard under a semi-arid climate that investigated how N2O emissions were affected by: 1) drip irrigation frequency; 2) nitrogen application rate; and 3) orchard floor management (shredded bark and wood mulch versus bare soil). The chapter also presents the results of an assessment of N2O emissions as a         49  function of fruit yield, and present data collected over two full years including measurements taken before, during and after the growing season. 4.2 Materials and methods 4.2.1 Study site and experimental design This study was conducted at the Summerland Research and Development Centre (SRDC) of Agriculture and Agri-Food Canada (Lat. 49°34’N and Long. 119°38’W), located in the Okanagan Valley near Summerland, BC, Canada (Figure 1.1). The site is characterized by cool winters with a minimum daily average air temperature of -1.5 OC in January, a maximum daily average air temperature of 28.4 OC in July and low annual precipitation of 346 mm (Environment Canada 2014a, 2014b). The soil is classified as an Osoyoos loamy sand (Wittneben, 1986), which is glacio-fluvial in origin, has a low water-holding capacity, a cation exchange capacity (CEC) of 7.9 meq (100g) -1, a pH of 6.6, and a C:N ratio of 7.9 before treatment initiation. The soil at this site is typical of apple orchards in the Okanagan Valley. The experiment was conducted in an apple orchard (Malus domestica Borkh, cv. Ambrosia) planted in 2003. The apple trees were arranged in eight 53.1 m long rows with a 0.9 m tree spacing within rows and a 3.5 m spacing between rows (Figure 4.1 and B.1). The outer row on each side of the planting were ‘guard rows’, used to eliminate edge effects and were not directly involved in the experiment. Each row (block) consisted of twelve 5-tree plots with dimensions of 4.50 m x 2 m. The trees on each end of the individual plots were ‘guard trees’ and the middle three trees were “experimental trees”. A common orchard grass mix was grown in the 1.5 m wide inter-row (alley). The ‘row’ part of each plot was kept weed-free via the use of herbicides (primarily glyphosate).          50  This study, established in 2012, was designed as a split-plot experiment; the main plot units had a 2x2 factorial design with two irrigation frequencies and two nitrogen application rates. The split plot units within each main plot consisted of three randomly assigned orchard floor management treatments: herbicide treated bare soil (Clean), shredded bark and wood mulch (Mulch) and black plastic woven geotextile (Geotextile). The whole experiment was a randomised complete block experiment with all treatment combinations replicated in six blocks; only three of the six blocks were used in the present study. The remaining 3 blocks were dedicated for parallel studies of Carbon isotopes. Irrigation was designed to deliver 100% of the water lost to evapotranspiration via drippers (4L h-1) located 0.30 m on either side of each tree and suspended approximately 0.3 m above the soil surface. An atmometer (ETGage Co, Loveland, CO) was used to estimate potential evapotranspiration, which was then combined with a crop coefficient model (Neilsen et al., 2015); the system was controlled automatically by a CR10X datalogger (Campbell Scientific, Logan, UT). The irrigation treatments consisted of irrigating twice (morning and afternoon) every day, or irrigating twice (morning and afternoon) every second day. The irrigation season extended from May through October and all plots received the same quantity of water. Plots were fertigated with Ca(NO3)2 for six weeks, from mid-May to the end of June in 2013 and 2014; irrigation and fertigation were timed to coincide with apple development. Treatments were 20 N g tree-1 or 63 kg N ha-1 (Low N, LN) and 40 N g tree-1 or 127 kg N ha-1 (High N, HN). Only two orchard floor management treatments were assessed in this study: Clean and Mulch. The Geotextile treatment was excluded. Mulch was applied on a 2-m wide strip centered on the apple tree row. The organic mulch was composed of shredded bark and wood-chips (primarily from Pinus contorta var latifolia and Picea glauca) generated as waste from local         51  sawmills. It was surface applied in late May of every second year (2012, 2014) to maintain a total mulch depth of approximately 10 cm. New mulch was added of existing mulch without disturbing the underlying material. Mulch was not applied in 2013 because it was still sufficiently thick to suppress weed growth.  4.2.2 Soil sampling and analyses At each soil sampling time, soil cores from nine locations to a depth of 15 cm were collected from nine locations (Figure 4.1) in the row and the alley using a 2-cm diameter auger. Samples were composited to get a representative sample for the row and the alley part of each plot. Soil sampling was conducted in May, June, August, and October in 2013, and every month from April to August, and October in 2014 (n=10). Soil samples were kept frozen until extraction and analysis. Field-moist soils were thawed and extracted for determination of exchangeable nitrate-N and nitrite-N (hereafter referred to as NO3--N because concentrations of NO2--N were minimal) and ammonium-N (NH4+-N) with 2 M KCl using a 1:5 soil to extractant ratio and a 1-hr shaking time, followed by filtration through Whatman No. 40 filter paper. Extracts were frozen at -20 0C and thawed overnight prior to analysis for NO3--N and NH4+-N using a segmented flow analyzer (SFA, Model 305D, Astoria Pacific International, Clackamas, OR). Salt-extractable organic C (SEOC) was extracted using 2 M KCl at a 1:5 soil extractant ratio, followed by filtration through a 0.45-μm membrane filter (Millipore Corp, USA); extracts were stored at -20 C (Chantigny et al., 2008), and analysed using an Aurora 1030W OI Analytical TOC analyzer (OI Analytical, USA). Calibration standards were made from potassium hydrogen phthalate in 2 M KCl. Calibration standards and samples were diluted 1:12 with ultrapure water to prevent interference from the chloride ions during sample analysis. Two ml of the sample were reacted with 2 ml of 5% phosphoric acid and 2 ml of 10% sodium         52  persulphate at 98 oC to oxidize and liberate the organic carbon which was measured as CO2. Total C and N content were measured by combusting 15-20 mg finely ground air dried sample using a Costech 4010 Elemental Analyzer with thermal conductivity detection. CEC was measured using an ammonium acetate extract buffered to pH 7. Soil pH and EC were measured on extracts of 1:2 soil deionized water ratio: finely ground, sieved (2 mm) and air-dried soil using a pH meter (WTW inoLab pH 7200) and an EC meter (WTW inoLab Cond 7200).  In-situ volumetric soil water content (0-30 cm depth) and temperature (at 2 cm, 10 cm, 20 cm and 50 cm depth) for each plot were measured continuously at 1-hr intervals using 30 cm length time domain reflectometry (TDR) probes installed vertically (Campbell Scientific,CS616) and type-T thermocouples (Omega Engineering, Stamford, CT), respectively, monitored using a Campbell Scientific CR1000 data logger. TDR probes were installed ~20cm away from the location of drippers (Figure 4.1). Thermocouples were positioned directly adjacent to the TDR probes at 2 cm, 10 cm, 20 cm and 50 cm depths in each of the plots. All TDR probes and thermocouples were installed permanently at the center of each plot where N2O flux was measured, and kept undisturbed throughout the experiment. Only the 2 cm thermocouple data are discussed further because N2O emissions are known to be mainly affected by the temperature of the top 5 cm of soil. Soil bulk density samples were collected at two locations, 15 cm and 30 cm away from the dripper parallel to the tree row to represent the fertilized strip, and at two locations at the center of the alley. All soil bulk densities were sampled from 3-9 cm depth using 6 cm diameter copper collars fitted to a soil bulk density sampler. An estimate of the fraction of water-filled pore space (WFPS) was calculated as:                        WFPS = (θv /[1-(ρb/ρp)])                                        Equation 4.1                             Where ߠ௩ is the volumetric water content of the soil (cm3cm-3), ߩ௕ is the bulk density of the soil         53  (g cm-3) and ߩ௣ is the approximate mineral particle density (2.65 gcm−3). Measurement of volumetric water content was conducted in the growing season as well as winter period. However, during winter the Campbell CS616 sensors measured the portion of water in soil that existed only in liquid phase (i.e. excluding the ice phase portion of water in the soil). Therefore, the WFPS during winter were likely underestimated. Thus, only the WFPS data collected during the growing season of each year were used to determine the statistical differences in WFPS among treatments. 4.2.3 N2O flux measurements The regular monitoring schedule involved sampling twice a week during fertigation; once a week during irrigation, in the fall and in the spring; and once every second week in winter. Sampling frequency was increased to two or three times a week around short-term weather events and management events such as mulch application, irrigation initiation, spring thaw or intensive rainfall. Regular gas flux monitoring started in January 16, 2013 and ended on December 22, 2014. There were a total of 45 gas flux sampling days in 2013 and 48 gas flux sampling days in 2014. Fluxes were measured using non-flow-through non-steady-state      (NFT-NSS) chambers (Rochette and Eriksen-Hamel, 2007). Rectangular stainless steel frames (0.69 m x 0.40 m x 0.15 m depth) were installed to a depth of 0.13 m and left undisturbed for the duration of the experiment. At sampling, an insulated vented lid (with top closed, headspace ~0.11 m) was tightly fitted to the frames. The size and location of the two chambers within each plot were chosen to capture a representative emission footprint by taking into account the width of the alley and row (fertilized strip), the location of the apple trees, and the drippers (Figure 4.1). To prevent distortion of soil water distribution, which could be caused by placement of the chamber collar, the 4 L h-1 drippers adjacent to each chamber edge were replaced by two                 54  half-capacity smaller drippers (2 L h-1), one dripping into the chamber at the inside edge and the other dripping on the outside edge of the chamber. Four air samples (20 mL) were collected from each chamber headspace after chamber deployment using a 25-ml gas-tight glass syearinge (catalog No. 03 378 207, National Scientific, Rockwood, TN) and immediately transferred into pre-evacuated 12 ml double-wadded Exetainers (Part No: 737W, Labco Ltd) that were evacuated to 200 mTorr, flushed with helium, and re-evacuated to 200 mTorr. The samples were accompanied by three low and three high field standards (containing 0.220 ppm and 2.20 ppm N2O) which were prepared on the sampling day. Nitrous oxide concentrations were analyzed within one week of sampling using a gas chromatograph (Bruker 456 GC, Bruker) equipped with an electron capture detector (ECD) and a CTC Combi-Pal auto sampler (CTC Analytics AG, Zwingen, Switzerland). The soil-surface N2O fluxes were determined by calculating slope (dC/dT), via either linear or non-linear regression (as appropriate) using the equations of Rochette and Hutchinson (2005). Non-linear regression (Hutchinson and Livingston, 1993) was used when the accumulation rate of N2O decreased with time. Linear regression was used when the accumulation rate of N2O was consistent with time (Rochette and Eriksen-Hamel, 2007). Climate data (relative humidity, air temperature, and pressure) for flux calculations were obtained from a weather station located within 0.5 km (Environment Canada, 2014b) at the SDRC.  Orchard-scaled emissions factors (EFs), uncorrected for background emission for each treatment, were expressed as the percentage of the applied N emitted as N2O-N using:          EF = (∑N2Oweighted/Applied available Nsource) x 100%                          Equation 4.2                              Where: ∑N2Oweighted  is the area-weighted annual cumulative N2O emission of a field                 (kg N2O-N ha–1) calculated by weighting row and alley fluxes by the area covered by the row         55  and alley (accounting for 57%  and 43%, respectively, of the field); and Applied available Nsource is the cumulative amount of N fertilizer (kg N ha–1 ) applied over the year. 4.2.4 Yield-scaled N2O emissions Apple harvesting occurred on September 30 in 2013 and on September 28 in 2014. Apple yield was determined from the three central apple trees in every plot and extrapolated to a yield per hectare (kg ha-1). Treatment effects on apple yield and fruit quality will be presented in a subsequent paper (Hannam et al. in prep), but yield was used in this study to calculate yield-scaled N2O emissions (g N Mg-1) for each treatment by dividing the annual cumulative N2O emissions (g N ha–1 year–1) by yield (Mg ha–1 year–1).  4.2.5 Data and statistical analysis Data were analysed to capture fluxes during (i) the spring thaw in the pre-growing season (PreGS) (January through April); (ii) the growing season (GS) (May through October); (iii) the period when ground freezes (post-harvest or post-growing season [PostGS]: November and December); and (iv) annually (January through December). Area-scaled ∑ N2O for individual plots was calculated using linear interpolation of flux rates between sampling days (Millar et al, 2012) and by extrapolation to per hectare of the fertilized strip. To correct for both lack of normality and homogeneity of the error variance, all data were first transformed with Box-Cox transformations (Box and Cox, 1964) using the SAS transgress procedure, but all data are reported as untransformed means. Effects of treatments on N2O emissions and environmental variables were determined using the PROC MIXED procedure in SAS, with repeated measurements in the model option (Version 9.3; SAS® Institute, Inc., Cary, NC); block, and block-by-irrigation-by-nitrogen rate were treated as random effects. Pairwise means comparisons were performed using the PDIFF statement and Tukey-Kramer adjustment method. Unless         56  otherwise mentioned, a p-value <0.05 was used for fixed effects and means separation. The relationships between N2O emissions and measured soil parameters were tested using Pearson product moment correlations with the PROC CORR procedure in SAS. Correlations were determined by processing all of the two-year data together. 4.3 Results 4.3.1 Weather, soil temperature and WFPS  Environmental conditions in 2013 and 2014 were typical of the region. The mean daily air temperature in 2014 (9.8 oC) was slightly higher than 2013 (9.6 oC), which was the same as the 30-year average (9.6 oC) (Table 4.1). In both years, daily mean soil and air temperatures were mostly between 20 oC and 30 oC in July and August and below 0 oC from December through February (Figure 4.2). More precipitation fell through the year in 2013 than in 2014, and a greater proportion of the precipitation fell during the fertigation period. The largest individual rainfall events also occurred in 2013 (June 20, 26 mm; June 24, 32 mm). In 2014, a single event of 35 mm was noted on June 13 (Figure 4.2). Nevertheless, the total quantity of irrigation water applied each year was similar. The soil WFPS was similar among treatments (Figure 4.3). The WFPS during the 2-year GS averaged 48% across treatments and none of the treatments affected WFPS significantly (P>0.05). By contrast, irrigation frequency had a stronger effect than orchard floor management on the time soil was at a high WFPS. On average during the 2-year GS, the LF plots exceeded 60% WFPS only 3% of the time while the HF plots exceeded 60% WFPS 21% of the time, as shown in Appendix B (Figure B.2). Similar effects were caused by Floor Management; the Clean plots exceeded 60% WFPS only 8% of the time while the Mulch plots exceeded 60% WFPS 15% of the time, as shown in Appendix B (Figure B.3). The water content sensors were deployed to determine average water content over the top 30 cm for multiple         57  experimental goals; WFPS in the top 5 or 10 cm of the plots, or directly under the drippers and beneath mulch, may have been higher than reported. 4.3.2 Daily N2O emissions The temporal patterns of N2O emissions (Figure 4.4) for the years 2013 and 2014 were characterized by high N2O emission levels within two periods: during freeze-thaw cycles in the PreGS and during the irrigation or fertigation period in the GS of each year. Emissions during the PostGS were negligible. For each year, the highest emissions were recorded following either a week of intense rainfall (late June 2013) during the fertigation period or at the start of irrigation (mid-May 2014), i.e., a week prior to the start of fertigation. Treatment effects on daily N2O emissions were not consistent across seasons within a year and between years (Table 4.2). Sampling date (Date) consistently accounted for most of the variation in N2O emissions across seasons within a year and between years. During the PreGS in 2013, Floor Management and Date accounted for most of the variation in N2O emissions. Mulching significantly reduced N2O emissions on three of the 11 monitoring days (Figure 4.4C). During the GS in 2013, Date accounted for most of the variation in N2O emissions; there were 6-7 monitoring days (out of the 31) when either less frequent irrigation or mulching resulted in significantly lower N2O emissions (Figure 4.4 A and C). The N2O emissions under mulch were higher than Clean on only three days over the two years. In 2014 most of the main factors significantly affected the mean seasonal N2O emissions; however there were few significant interactions with Date. During the PreGS, mulching reduced N2O emissions in 4 of the 13 monitoring days (Figure 4.4C). During the GS, both LF irrigation and mulching caused significantly lower N2O emissions in 4-10 of the 29 monitoring days (Figure 4.4 A and C).         58  Correlation analysis of the combined two-year data showed that some ancillary variables had low N2O predictive power while others were not significantly correlated at all. Nevertheless, the N2O fluxes during the GS were positively correlated with WFPS (r = 0.30, P < 0.05). There was also an inverse correlation between N2O fluxes and soil NH4+-N concentrations (r = -0.39, P < 0.01) and between N2O fluxes and EC (r = -0.39, P < 0.01). There was no significant relationship between N2O fluxes and concentrations of soil NO3--N, SEOC or pH. 4.3.3 Seasonal and annual cumulative N2O emissions  Treatment effects on cumulative N2O emissions were variable across seasons and years (Table 4.3). In 2013, none of the main treatment factors (except mulching) caused significant differences in seasonal and annual ∑N2O emissions. Mulching reduced ∑N2O emissions during the PreGS. In 2014, the annual ∑N2O emissions were reduced by LF irrigation and Mulching. There was no detectable effect of Mulching on ∑N2O emissions in individual seasons. Treatment interactions were seldom significant in either year. There was no difference in annual ∑N2O between years. However, the relative contribution of seasons towards the total annual ∑N2O varied across years (Table 4.3). In 2013, the majority (83%) of the annual ∑N2O emissions occurred during the GS, followed by PreGS (17%); the emissions during the PostGS were negligible. In 2014, both PreGS (48%) and GS (48%) contributed equally while PostGS only accounted for 4% of the total N2O emissions (Figure 4.4 and Table 4.3). The average N2O emissions in all the treatments in the PreGS of 2014 (0.50 kg N ha-1) were higher than in 2013 (0.19 kg N ha-1) while the emissions in the GS of 2013 (0.90 kg N ha-1) were much higher than in 2014 (0.50 kg N ha-1).           59  Based on combined 2-year data, LF irrigation and mulching reduced N2O emissions by 30% and 20%, respectively, while no difference was found between nitrogen application rates (Table 4.4). The significance of mulching for reducing N2O emissions was also apparent on the combined PreGS and combined PostGS data. Similarly, LF irrigation reduced the N2O emissions during the GS and the annual N2O emissions. Emission factors (EFs) are N2O emissions per unit of total N, applied uncorrected for background emissions. Emission factors averaged 2.35% across the entire study period and ranged from 1.60% to 3.11% across treatments (Table 4.4). There were no significant differences caused by irrigation treatment or mulching. Emission factors were affected by nitrogen application rates; the EF in HN applications was approximately half that of the EF in the LN application rate. Yield-scaled N2O emissions (ratios of cumulative annual N2O emission to annual yield) were not affected by the treatments imposed. The N2O emissions during the same season but in different years and annual area-scaled and yield-scaled N2O emissions across years were significantly different (p<0.01, Table 4.4). 4.3.4 Soil nutrients and chemistry In 2013, soil NO3--N concentrations were reduced significantly by either mulching or LN applications and were approximately 50% of the Clean plots or the HN plots, respectively (Table 4.5). In 2014, soil NO3--N concentrations in Mulch plots were one-fifth those of the Clean plots. Soil NO3--N concentrations were not affected by the rate of N application in 2014. On average, soil NO3--N concentrations remained similar between years. Soil NH4+-N concentrations were similar across treatments in 2013 (Table 4.5), but were higher in Mulched plots in 2014. On average, soil NH4+-N concentrations doubled from 2013 to         60  2014. Soil NH4+-N concentrations were lower than soil NO3--N concentrations; the majority (68%) of mineral N was in the form of nitrate.  In both years, soil SEOC concentrations were not affected by irrigation frequency or N-application rate (Table 4.5). Mulching increased soil SEOC concentrations by 38% and 99% in 2013 and 2014, respectively. On average, soil SEOC concentrations doubled from 2013 to 2014. None of the management factors affected soil pH across years. The soil became slightly more acidic between 2012 and 2014 (p<0.0001); soil pH was 6.6 in 2012 before treatment initiation and averaged 6.5 and 6.3 across treatments, in 2013 and 2014, respectively. In 2013, soil EC was not affected by treatment. However, in 2014 mulching reduced soil EC by 22%. Fresh mulch was applied only in 2012 and 2014. On average, soil EC doubled from 2013 to 2014. 4.4 Discussion 4.4.1 Effect of irrigation frequency, nitrogen rate and mulching on N2O emissions Averaged over two years, this study shows that less frequent irrigation (every 2nd day) reduced N2O emissions by 30% compared with more frequent irrigation (every day). This difference may have been caused by recurrent periods of high WFPS in the more frequently irrigated plots. Soils maintained at higher volumetric moisture content over a longer period of time may result in higher N2O fluxes than soils subjected to wetting and drying cycles (Rolston et al., 1982). Denitrification is the only possible direct source of N2O from nitrate-based fertilizers (Russow et al., 2008) and higher WFPS over longer periods can promote greater rates of denitrification, resulting in increased N2O emission. The positive correlation between N2O emissions and WFPS in this study is indicative of the importance of denitrification (over nitrification) for explaining the observed differences in N2O emissions due to irrigation frequency. Higher irrigation frequency usually results in a smaller wetted soil volume and higher         61  mean soil water content (Wang et al. 2006). In this study, WFPS was measured at a single point ~20cm cm away from the location of the dripper. WFPS directly under the dripper was expected to be even higher, especially with more frequent irrigation. The observation of increased N2O under high frequency irrigation in this study is consistent with the findings of Rolston et al. (1982), who measured N2O emissions in a spray-irrigated perennial ryegrass field, but it differs from the findings of Abalos et al. (2014), who found no effect of irrigation frequency on N2O emissions in a drip-irrigated melon field. However, both studies were carried out only during the growing season and for less than 3 months; the longer-term effects of irrigation frequency on N2O emissions in those studies are unknown. This study indicated that irrigation frequency affects emissions during the growing season as well as total annual emissions. In general, nitrogen application rate did not have a significant effect on mean 2-year N2O emissions or on any of the measured soil parameters in either year. In 2013, however, the higher rate of nitrogen application increased soil NO3--N concentrations by 42%. Nevertheless, mean soil NO3--N concentrations were generally low and, as a result, the treatment difference observed in 2013 was unlikely to have caused differences in N2O emissions. In addition, soil NO3--N concentrations in 2014 were not affected by nitrogen application rate. Although higher nitrogen application rates usually result in higher N2O emissions, the rates of N applied in this study (63 kg N ha -1 or 127 kg N ha-1) were much lower than those applied in other studies conducted in orchards: e.g. 210 kg N ha-1 in Lin et al. (2012), 312 kg N ha-1 in Pang et al. (2009) and 579-597 kg N ha-1 in Lin et al. (2010). Thus, the N application rate applied in this study was only one-third to one-fifth of the N application rate applied in the studies listed above. The absence of an effect of nitrogen application rate on N2O emissions in this study may be a consequence of the nitrogen-efficient application methods. Thus, irrigation frequency and mulching may have been         62  more important in determining the availability of soil nitrogen, which, in turn, had a stronger influence on N2O emissions. Surface application of shredded bark and wood mulch reduced N2O emissions by 20%. Similar results were observed at a grape vineyard (chapter 3) located within 0.5 km of the current research site. Mulching also decreased soil NO3--N concentrations (by 61% in 2013 and by 80% in 2014) and increased soil SEOC in both years at that site. This is consistent with other studies (Homyak et al., 2008; Hannam et al., 2016), where lower soil nitrate was detected under mulch, likely due to increased microbial immobilization of mineral N in the presence of increased available carbon. In the current study, the lower soil NO3--N levels under mulch likely caused lower N2O emissions. Contrary to the effect of nitrogen application rate, the effect of mulching on soil NO3--N concentrations was stronger and more consistent across years. In addition, the higher WFPS and greater availability of labile carbon under the mulch may have created favourable anaerobic conditions for enhanced complete denitrification of NO3- to N2, further decreasing N2O emissions under mulch. Decreased N2O emissions have been observed in past studies where other plant residues were applied to the soil surface (Roslton et al., 1982; Lopez-Fernández et al., 2007; Livesley et al. 2010; Sanchez-Martin et. al, 2010; Steenwerth and Belina, 2010).  4.4.2 Seasonal N2O emissions: thaw and the pre-growing season The temporal patterns of N2O emissions in the two monitoring years were characterized by high N2O emissions during freeze-thaw cycles in the PreGS and during irrigation and fertigation in the GS. A significant portion (17% in 2013 and 48% in 2014) of the total N2O emissions occurred during the PreGS, particularly during the thaw period. During freeze-thaw events in the PreGS, it is likely that WFPS exceeded the WFPS threshold needed to create         63  anaerobic conditions. The true water and water-as-ice filled pore space in the PreGS was likely higher than reported in the WFPS measurements using dielectric sensor methods, which do not register frozen water content. WFPS in the PreGS appears low (<45%) during freeze-thaw cycles but the actual WFPS was likely higher in the top 0-5 cm of the soil because the majority of the 30 cm long TDR probe was located within frozen soil below 5 cm. Soil nitrogen availability was not measured during the PreGS; however, during this period plant growth was limited and, thus, trees were probably not actively competing with soil microbes for available soil nitrogen. This may have led to increased soil nitrogen concentrations which, together with elevated soil water content at the soil surface, could have created suitable conditions for enhanced denitrification that lead to N2O spikes in both years. Most previous studies in perennial cropping systems assumed that N2O emissions outside the GS were minor and, hence, did not account for N2O emissions during the winter (e.g. Smart et al., 2011; Suddick et al., 2011); such an assumption may be true on sites where soils are not frozen outside of the growing season. Nevertheless, this assumption requires confirmation. To the best of my knowledge, this study and the N2O emission study at the grape vineyard (Chapter 3) are the only two studies that found significant emissions during the PreGS for woody perennial crops in a semi-arid climate. The accumulation of easily degradable substrates from dead plant roots and microorganisms due to cell lysis from freezing (Herrmann and Witter 2002) may provide fresh substrates for surviving microbes, leading to enhanced N2O emissions during freeze-thaw cycles. This mechanism may enhance denitrification both by fueling denitrifiers and by depleting O2 (Mørkved et al, 2006).  The winter of 2014 was colder than the winter of 2013, and N2O emissions in the pre-GS were almost three-fold greater in 2014 than in 2013. The lower air temperatures that occurred prior to thaw in 2014 may have resulted in greater cell lysis during winter freeze. The 2014 thaw         64  period was also longer in duration and included multiple freeze-thaw cycles, which may have promoted additional cell lysis during the thaw period. Lower freezing temperatures have the potential to kill more soil microbes and damage roots, providing more easily degradable substrates for the surviving microbes during the following thaw event (Neilsen et al., 2001; Koponen and Martikainen, 2004). Together, this may explain why there was a significant increase in N2O emissions during the PreGS of 2014 compared with 2013. 4.4.3 Seasonal N2O emissions: growing season During the GS, the highest daily fluxes (19-30 g N ha-1day-1) occurred in 2013 following intense rainfall events during fertigation, while the highest daily fluxes in 2014                         (4-22 N ha-1day-1) occurred following the start of irrigation but prior to fertigation. The high fluxes in 2013 probably exceeded those observed in 2014 because the intense rain events in late June 2013 coincided with the end of fertigation, when soil nitrate was at a maximum and, therefore, conditions for denitrification were favourable. No similar spikes in N2O emissions were noted during the single intense rain event that occurred on June 13, 2014, well before the end of the fertigation period. The highest daily fluxes in 2014 occurred following the start of irrigation, probably due to increased mineralization of C and N following re-wetting of dry soil (Beare et al., 2009). 4.4.4 Yield-scaled N2O emission and N2O emission factor Effective management practices combine agricultural productivity with environmental sustainability. Expressing N2O emissions per unit of yield accounts for both productivity and environmental sustainability and may provide a useful metric for greenhouse gas inventories (Venterea et al., 2011). In this study, area-scaled N2O emissions were significantly decreased by both lower irrigation frequency (irrigation every 2nd day) and surface application of bark and         65  wood mulch. However, neither irrigation frequency nor surface application of mulch had an effect on the 2-year mean yield-scaled N2O emissions. Nevertheless, the lack of statistical significance and actual values of yield-scaled emissions reported here may not be representative. Yield-scaled emissions calculations are dependent on both the variability in area-scaled N2O emissions (n=3) and the variability in yield (n=3). While three replicates had sufficient statistical power to compare treatment effects on area-scaled N2O emissions, a reliable value for yield-scaled emissions may not be achievable with the statistical power afforded by 3 replicates. Nevertheless, this study represents the first report of yield-scaled N2O emissions for an apple crop. The N2O emissions during the same season, but in different years, as well as the annual area-scaled and yield-scaled N2O emissions across years, were significantly different, implying the importance of multi-year continuous monitoring for obtaining representative N2O emissions and yield data.  The average emission factor (2.4%) in this study was more than twice the 1% default direct emission factor provided by the Intergovernmental Panel on Climate Change (IPCC) for calculating N2O emission rates from agriculture regardless of fertilizer and soil type (IPCC, 2006). The 1% IPCC default direct emission factor may be overly simplistic for drip-fertigated orchards in semiarid regions. In these systems, water and nitrogen are usually concentrated in a smaller area of soil under the drippers compared to annual crops, where water and nitrogen are distributed more uniformly. The practice of fertigation is designed to maximize nitrogen- and water-use efficiency. However, the localized concentration of water and nitrogen creates favourable conditions for denitrification (Smart et al., 2011) and, therefore, results in a higher emission factor for applied nitrogen. The emission factor in this study was approximately 2 to 4 times higher than that reported in other studies on apple orchards (Pang et al., 2009; Shunfeng et         66  al., 2015); however, previous studies did not measure N2O emissions year round, nor were they conducted under irrigated conditions. A similar deviation from the default IPCC emission factor (1%) was observed in a nearby parallel experiment on drip-irrigated grapevines (Chapter 3), where the emission factor was 3.1%. Semiarid lands constitute a significant part of the global land base (Pang, et al., 2009) and irrigation is usually a necessity for efficient crop production. Therefore, the use of the IPCC (2006) methodology for national greenhouse gas inventories in countries containing micro-irrigated semiarid land may result in a significant underestimate of N2O emissions 4.5 Summary Over a period of two years in a drip-irrigated apple orchard growing in a semiarid climate, this study observed significantly lower N2O emissions when: 1) reducing drip irrigation frequency from every day to every second day, and 2) applying a surface mulch of shredded bark and wood. Hence, altering irrigation frequency and utilizing surface organic mulches may provide an opportunity to reduce N2O emissions from drip-fertigated orchards. Nitrogen application rate did not affect N2O emissions in this fertigated system. The prevalence of higher WFPS over longer periods in soils subjected to more frequent irrigation treatment likely promoted denitrification, resulting in higher N2O emissions. This mechanism could be even more important in orchards established on finer-textured soils because of the higher probability of water saturation. Under mulch, lower N2O emissions were likely caused by lower soil nitrate concentrations, which were caused by increased microbial immobilization (as a consequence of higher labile organic carbon) and enhanced complete denitrification. The mean fraction of applied N lost as N2O (N2O emission factor), uncorrected for background emissions, averaged 2.4% across all treatments, which is more than twice the 1% default direct emission factor         67  provided by the IPCC. The use of a 1% IPCC default direct emission factor could lead to significant underestimation of N2O emissions from drip-fertigated crops in semiarid regions. This study represents one of only a few long-term studies that have investigated the effects of irrigation frequency, nitrogen application rate and mulching on N2O emissions in perennial systems; further investigation over a longer term (>2 years) may be required to fully understand the N2O mitigation potential of bark and wood mulch.                                       68  Table 4.1: Summary of climate and soil data during experimental years 2013 and 2014 including thaw dates, precipitation, irrigation, and average air and soil temperatures. Data presented during the pre-growing season (PreGS, January through April), the growing season (GS, May through October), and post-growing season (PostGS, November and December).  z Data separated by “/” indicates either thaw period/remainder of PreGS totals or fertigation  period/irrigation period totals y Total water inputs = Total precipitation + Total irrigation x Soil temperature was measured at 2cm depth            Parameter 2013 2014PreGS GSPostGS PreGS GS PostGSDate of first thaw Jan. 9th  - - Jan. 10th  - -Date of last thaw Feb. 20th - - Mar. 4th - -Total precipitation (mm) z 38/55 137/104 36 22/43 74/116 71Total irrigation (mm) - 145/601 - - 148/538 -Total water inputsy 93 987 36 65 875 71Average soil temperature (OC)x 3.1 17.6 0.6 3.7 18.6 1.6Average air temperature (OC) 3.5 17.2 -1.1 2.6 17.5 0.8No of days soil temperature < 0 OC 36 0 36 40 0 20No of days air temperature < 0 OC 29 0 37 40 0 23No of days soil temperature < -5 OC 7 0 1 0 0 2No of days air temperature < -5 OC 6 0 12 14 0 9        69  Table 4.2: F-value of repeated measures analyses of daily N2O emissions in the apple orchard during the pre-growing season (PreGS, January through April) and during the growing season (GS, May through October) in 2013 and 2014.  z k, *, **, ***,**** indicates that differences between treatments are significant at p ≤  0.1, 0.05, 0.01, 0.001, 0.0001, respectively.  y Irrigation occurred from May 8 to October 23 in 2013 and from May 12 to October 23 in 2014. Ca(NO3)2 was applied via fertigation from May 14 to June 25 in 2013 and May 21 to July 2 in 2014. Bark and wood mulch was surface applied in May of 2012 and 2014.                   Effectz GSy GSyF Value Num DF F Value Num DF F Value Num DF F Value Num DFIrrigation (I) 1.46 1 4.21k 1 6.98* 1 7.85 1N-rate (N) 0.10 1 0.01 1 5.80* 1 0.56 1Floor Mgmt (F) 8.55* 1 1.18 1 6.29* 1 22.63**** 1Date (D) 8.43**** 10 21.71**** 30 54.01**** 12 11.25**** 28I x D 1.64 10 1.5* 30 1.7k 12 1.64* 28N x D 1.75k 10 1.31 30 1.55 12 0.73 28F x D 8.17* 10 2.39**** 30 1.85* 12 2.08** 28PreGS PreGS2013 2014        70  Table 4.3: Effects of irrigation frequency, nitrogen application rate and orchard floor management on seasonal and annual N2O emissions in the apple orchard during the pre-growing season (PreGS, January through April) and during the growing season (GS, May through October) in 2013 and 2014.  z Mean N2O emission calculations for treatments were based on a 2 m-wide fertilized strip. Means followed by different lowercase letters within columns indicate differences of least squares means between pairs of treatments using the Tukey-Kramer adjustment, at p<0.05. y k, *, **, ***, **** and ns indicate a significant treatment effect at p ≤ 0.10, 0.05, 0.01, 0.001,0.0001 or no significant effect, respectively.    Effect2013 2014PreGS GS Annual PreGS GS AnnualIrrigation Low Frequency (LF) 0.16 ± 0.05 0.75 ± 0.07 0.91 ± 0.11 0.42 ± 0.05 0.39 ± 0.04a 0.86 ± 0.07aHigh Frequency (HF) 0.21 ± 0.05 1.04 ± 0.15 1.26 ± 0.17 0.59 ± 0.11 0.62 ± 0.08b 1.26 ± 0.156bN-rateLow N (LN) 0.19 ± 0.05 0.89 ± 0.09 1.08 ± 0.11 0.52 ± 0.04 0.45 ± 0.05 1.01 ± 0.06High N (HN) 0.18 ± 0.05 0.90 ± 0.15 1.08 ± 0.18 0.48 ± 0.11 0.56 ± 0.08 1.11 ± 0.18Floor mgmt Mulch 0.08 ± 0.03a 0.89 ± 0.13 0.98 ± 0.16 0.42 ± 0.05 0.48 ± 0.08 0.93 ± 0.10aClean 0.29 ± 0.04b 0.90 ± 0.11 1.18 ± 0.14 0.59 ± 0.10 0.53 ± 0.06 1.18 ± 0.15bPr > F yIrrigation (I) ns ns ns k ** **N-rate (N) ns ns ns ns ns nsFloor mgmt (F) *** ns ns k ns *I x N ** ns ns ns * *I x F ns ns ns ns k kN x F ns ns ns k ns nsI x N x F ns ns ns ns ns ns              N2O emission (kg N ha-1)z        71  Table 4.4: Effects of irrigation frequency, nitrogen application rate and orchard floor management on 2-year mean seasonal and annual  N2O emissions, 2-year mean N2O emissions factor (EF, N2O emissions per unit of total N applied), and 2-year mean yield-scaled N2O emissions (N2O/yield) in an apple orchard. Year was included as a repeated measure factor in the ANOVA.     z Mean N2O emission calculations for treatments were based on a 2 m-wide fertilized strip. y N2O emission factor calculations for treatments considered both the fertilized strip and the alley. Pairs of means followed by different lowercase letters within columns indicate differences of least squares means using the Tukey-Kramer adjustment, at p<0.05. x k,*, **, ***, **** and ns indicate a significant treatment effect at p ≤ 0.10, 0.05, 0.01, 0.001, 0.0001 or no significant effect, respectively.    Effect N2O emissions (kg ha-1 [season or year]-1 )z N2Oy Yield-emission scaledfactor N2OPreGS GS PostGS Annual (%) ( g Mg-1) Irrigation Low Frequency (LF) 0.29 ± 0.04 0.57 ± 0.06a 0.03 ± 0.01 0.88 ± 0.06a 2.17 ± 0.20 24.3 ± 2.1High Frequency (HF) 0.40 ± 0.07 0.83 ± 0.09a 0.04 ± 0.01 1.26 ± 0.11b 2.54 ± 0.18 35.9 ± 3.5N-rateLow N (LN) 0.36 ± 0.05 0.67 ± 0.07 0.02 ± 0.01 1.04 ± 0.06 3.11 ± 0.11a 28.7 ± 2.2High N (HN) 0.33 ± 0.07 0.71 ± 0.09 0.04 ± 0.01 1.09 ± 0.12 1.60 ± 0.11b 31.5 ± 3.9Floor mgmtMulch 0.25 ± 0.04a 0.69 ± 0.09 0.02 ± 0.01a 0.95 ± 0.09a 2.23 ± 0.19 28.7 ± 3.4Clean 0.44 ± 0.06b 0.71 ± 0.07 0.05 ± 0.01b 1.18 ± 0.10b 2.48 ± 0.19 31.5 ± 2.8Pr > F xIrrigation ns * ns * k nsN-rate ns ns ns ns **** nsFloor mgmt ** ns * * ns nsYear **** **** **** **** ns **        72  Table 4.5: Effects of irrigation frequency, nitrogen application rate and orchard floor management on mean extractable NO3--N, NH4+- N, salt-extractable organic carbon (SEOC), pH and electrical conductivity (EC) of soil (0-15 cm depth) from the apple orchard during the growing season (May through October) in 2013 (n= 96, i.e., 24 plots x 4 sampling events) and April through October in 2014 (n=144, i.e., 24 plots x 6 sampling events).   z Pairs of means followed by different lowercase letters within columns indicate differences of least squares means using the Tukey-Kramer adjustment, at p<0.05. y Within columns k, *, **, ***, **** and ns indicate a significant treatment effect at p ≤ 0.10, 0.05, 0.01, 0.001, 0.0001 or no significant effect, respectively.   Year Effect NO3--Nz NH4+-N SEOC pH EC(mgkg-1) (mgkg-1) (mgkg-1) (μScm-1)2013 Irrigation Low Frequency (LF) 13.7 ± 3.1 3.7 ± 0.6 244 ± 26 6.56 ± 0.04 100 ± 3High Frequency (HF) 11.5 ± 1.6 3.3 ± 0.3 211 ± 18 6.44 ± 0.04 102 ± 5N-rateLow N (LN) 9.3 ± 1.0a 3.2 ± 0.3 230 ± 25 6.59 ± 0.04 103 ± 5High N (HN) 16.0 ± 3.3b 3.8 ± 0.6 226 ± 21 6.40 ± 0.03 99 ± 4Floor ManagementMulch 7.1 ± 0.8a 3.2 ± 0.2 264 ± 26a 6.51 ± 0.04 91 ± 1Clean 18.1 ± 3.2b 3.8 ± 0.6 192 ± 17b 6.49 ± 0.04 111 ± 5Pr > F yIrrigation ns ns ns ns nsN-rate ** ns ns ns nsFloor Management **** ns ** ns ns2014 IrrigatiIonLow Frequency (LF) 10.2 ± 1.6 8.3 ± 0.6 309 ± 34 6.33 ± 0.04 208 ± 21High Frequency (HF) 16.1 ± 3.5 8.9 ± 0.8 362 ± 40 6.26± 0.04 216 ± 20N-rateLow N (LN) 12.5 ± 2.5 8.1 ± 0.6 321 ± 37 6.31± 0.04 213 ± 19High N (HN) 13.7 ± 3 9.2 ± 0.8 350 ± 37 6.28± 0.04 211 ± 21Floor ManagementMulch 4.3 ± 0.4a 9.8 ± 0.9a 446 ± 41a 6.29± 0.03 186 ± 19aClean 21.9 ± 3.5b 7.5 ± 0.4b 224 ± 27b 6.3± 0.04 238 ± 21bPr > F yIrrigation ns ns ns ns nsN-rate ns ns ns ns nsFloor Management **** **** **** ns ***        73                             Figure 4.1: Spatial representation of a single apple tree functional unit in a drip-irrigated plot. “x” and “y”  represent soil sampling locations for row chamber and alley chamber, respectively.                 74   Figure 4.2: Air and soil temperature, precipitation and irrigation inputs for 2013 and 2014. “Fertig” in the top insert represents periods of fertigation, the application of nitrogen through the irrigation system. The two different sized blue columns are caused by two irrigation frequencies; the shorter column indicates the total mm of water applied as the result of daily irrigation and the longer column indicates the total mm of water applied as the result of irrigation every 2nd day (applied on even Julian days). Both strategies supply the same total volume, matched to estimated ET.   75   Figure 4.3: Water-filled pore space (WFPS) of the soil in 2013 and 2014 across: (A) irrigation frequency (B) nitrogen application rate and (C) orchard floor management. Vertical dashed lines are used to separate seasons. “Fertig” in the top insert represents periods of fertigation, the application of nitrogen through the irrigation system. Water Filled Pore Space (%)   76   Figure 4.4: Mean daily of N2O emissions in 2013 and 2014 by: (A) irrigation frequency and alley position (B) nitrogen application rate and (C) orchard floor management. Vertical dashed lines are used to separate seasons. Capped bars indicate standard error of the mean (n= 12). *, **, ***, **** indicate differences of least squares means using the Tukey-Kramer adjustment, at p < 0.05, p < 0.01, p < 0.001, p < 0.0001 respectively. “Fertig” in the top insert represents fertigation, the application of nitrogen through the irrigation system.  77  Chapter 5: FINE-SCALE SPATIAL DISTRIBUTION OF GREENHOUSE GAS EMISSIONS AND SOIL PARAMETERS AROUND DRIPPERS AND APPLE TREES 5.1 Background In drip-fertigated orchards, water and nitrogen are usually concentrated in a smaller area of soil under the drippers compared to annual crops grown with or without irrigation and with broadcast fertilizer where water and nitrogen are distributed more uniformly. The practice of fertigation in orchards is intended to maximize nitrogen and water use efficiency (Smart et al., 2011). However, this regularly imposed spatial application of water and fertilizer can have profound influence on GHG emissions, and adds another layer of complexity to the already existing natural spatial variability of GHG and soil physicochemical parameters. Knowledge of the effects of spatial distribution of physio-chemical parameters and GHG production will  allow better scaling of event-related GHG emissions (fertigation) since the density of drippers/sprinklers and trees in orchards is easily known (Alsina et al , 2013). Denitrification rates are temporally and spatially variable due to denitrification ′′hot moments′′, meaning high emissions over short time periods, and ′′hot spots′′, meaning that small areas of soil account for a very high percentage of areal denitrification within a given time period (McClain et al., 2003). The greatest modeling challenges are for hotspots. Given the increasing availability of high temporal frequency climate data, models are promising tools for evaluating the importance of hot moments such as freeze-thaw cycles and drying/rewetting events. Spatial hotspots are less tractable due to our inability to get high resolution spatial approximations of denitrification drivers (McClain et al., 2003). More data on the spatial variability will give us additional information on the factors controlling GHG emission and could eventually lead to generating knowledge on potential measures to counteract emissions (Allaire 2012; Alsina et al,   78  2013; Van den Heuvel et al. 2009). Within-site spatial variability with coefficients of variation up to 173% for CO2 in a forest system (Fang et al., 1998) and 493% for N2O in a maize field (Clemens et al., 1999) have been reported. Row-crop systems like orchards and vineyards are expected to have even higher spatial variability because of regularly imposed spatial application of water and fertilizer. Disregarding this spatial variability in chamber methodology may affect the precision of greenhouse gas measurements as the extent of spatial variability can dictate appropriate chamber size and deployment location. Few studies have investigated the spatial variability of N2O emissions in micro-irrigated row-crop systems. In irrigated systems, the spatial variability of N2O production is strongly dependent on the nature of the irrigation type and the fertilization management practices. In California under a Mediterranean climate, Smart et al. (2011) compared the effect of two irrigation types (stationary Fan-Jet® micro-sprinklers and drip irrigation) on the spatial distribution of N2O emissions from an almond orchard and a vineyard. Static chambers were spatially laid out along x- and y-transects with a dripper or sprinkler in the center (0, 0) position, and eight chambers along each axis. Emission patterns modeled using Gaussian distribution showed a distinct emission “plume” of high emissions formed centered on the dripper. Similarly, Smart et al. (2011) observed a “doughnut”- shaped emission pattern at the radial distance of 1m from their sprinklers. In a similar study in California, Alsina, et al. (2013) compared the effects of two irrigation types (stationary micro-sprinklers and drip irrigation) on spatial distribution of N2O and CH4 emissions from an almond orchard on a sandy loam soil. Ten static chambers were spatially laid out along the x- and y-transects with a dripper or micro-sprinkler in the center (0, 0) position. All 10 chambers were along the positive and negative “x” and “y” axis for sprinklers while all the chambers for drip were along the negative “x” axis and positive “y” axis, and none   79  were in the space between the axes. They observed circular wetted-area of 1 m in diameter for the drip and of 5 m diameter for the sprinkler systems. Using a Gaussian distribution to model the spatial variability, Alsina, et al. (2013) estimated the maximum N2O flux to be underneath the dripper. For sprinkler, the distribution pattern showed a minimum at the sprinkler head (0, 0), followed by an increase of the N2O emissions rate up to a maximum value at approximately 1.5 m from the midpoint of the grid, and then an exponential decline as the radius increased. The above studies gave insights on how regularly imposed irrigation affected spatial variability of N2O emissions by deploying 5 chambers in a distance 0.9 to 2.5m along one axis (x or y). Further studies at finer-scale is needed, as hot spots could be observed at the scale from 1-10 cm up to 1-10 km (Groffman et al., 2009). The objectives of this study were (i) to characterize spatial distribution of N2O and CO2 emissions and soil properties at a sub-plot (<0.75m) scale and (ii) to analyze the relationship between N2O and CO2 emissions and soil properties (WFPS, NO3-N, NH4-N, and SEOC) with respect to distances from the trees and drippers at a sub-plot scale during three events (before irrigation, during and after fertigation) in an apple orchard in semiarid region of the Okanagan valley, British Columbia. The implications of the spatial variability of GHG fluxes detected on GHG chamber methodology is also discussed.  5.2 Materials and methods 5.2.1 Study site and experimental design This study was conducted at the Summerland Research and Development Centre of Agriculture and Agri-Food Canada (Lat. 49°34’N and Long. 119°38’W), located in the Okanagan Valley near Summerland, BC Canada. The soil is classified as a Osoyoos loamy sand (Wittneben, 1986), which is glacio-fluvial in origin, has low water holding capacity and a cation   80  exchange capacity (CEC) of 7.9 meq/100g, a pH of 6.6, and a C:N ratio of 7.9 before treatment initiation. Mean daily air temperatures were obtained from a weather station located within 0.5 km (Environment Canada, 2014) and soil temperature was measured in-situ using 30-cm long time domain reflectometry (TDR) probes installed vertically (Campbell Scientific, CS616).  Details of the overall experimental design, soil and gas sampling protocols and analysis of 2 years of long term monitoring data are provided in Chapter 4. In brief, this experiment was conducted by using only some treatments of a split-plot experiment; the main plot units had a 2x2 factorial design with two irrigation frequencies and two nitrogen rates. The sub plot units consisted of three randomly-distributed orchard floor managements: herbicide-treated bare soil (Clean), shredded bark and wood mulch (Mulch) and black plastic geotextile (Black Plastic) established in 2010. The whole experiment was a randomized complete block experiment with all treatment combinations replicated in six blocks. Irrigation was designed to deliver 100% of the water lost to evapotranspiration via drippers (4L h-1) located 0.30 m on either side of each tree suspended about 0.3 m above the soil surface. The irrigation treatments consisted of irrigating twice (morning and afternoon) every day, or irrigating twice (morning and afternoon) every second day. The irrigation season extended from May through October in both 2013 and 2014 and all plots received the same quantity of water. All the plots were fertigated with Ca(NO3)2 for six weeks from approximately mid-May to end of June from 2012 to 2014, timed to coincide with apple development. Treatments were 20 N g tree-1 season-1 or 63 kg N ha-1 season-1 (Low N, LN) and 40 N g tree-1 season-1 or 127 kg N ha-1 season-1  (High N, HN). Only two orchard floor managements (Clean or Mulch), one nitrogen rate (40 N g tree-1 season-1), and two drip irrigation frequencies (every day or every second day) were assessed in this study (Table 5.1). Three replicates of each treatment were available as three replicates were   81  reserved for other parts of the larger study. The treatments were applied in 5-tree plots with dimensions of 4.50 m x 2 m (Figure B.1). Individual trees were separated by a distance of 0.9 m from each other and trees on each end of the individual plots were ‘guard trees’ while the middle three trees were “experimental trees”. Assuming consistent distribution of GHGs and soil parameters around each tree, a “representative quadrant” was defined by using one randomly selected tree out of the three middle trees of each plot as a center point (Figure 5.1). For each selected tree, two transects were established forming axes crossing at the tree, which was designated as the origin (0, 0). One transect (x-axis) was established perpendicular to the tree row and extended into the orchard alley, and the other (y-axis) was parallel to the trees in the tree row. Spatial variability of GHG fluxes and soil parameters was studied by installing 9 mini- chamber collars (L= 16 cm, W=15 cm and H = 8 cm) in one of the quadrants formed by the x-y axes. The mini-chambers were made of stainless steel restaurant pans; one pan with the bottom cut used as collar and one full pan used as lid. The collars had sides that extended 6 cm into the soil while 2 cm were left above the surface to attach a chamber lid (L= 16 cm, W=15 cm and H = 8 cm). They were installed one week before the beginning of the experiment. In mulched plots, the mulch was temporarily removed to facilitate chamber installation and replaced back after chamber installation. The basis underlying the choice of chamber size was that water and nutrient availability, and root density would differ enough between chambers to capture potential spatial differences in GHG emissions. The 9 mini-chambers in each quadrant were installed in staggered pattern to account for the distortion of soil water distribution that may be caused by placement of the chamber collar. In addition, the 4 L h-1 drippers in selected  quadrants adjacent to each mini-chamber were replaced by two half-capacity smaller drippers (2 L h-1) that were adjusted to drip into the inside edge and the other dripping on the outside edge of the mini-  82  chamber. Prior work had indicated that the bulb of increased apple root development under a dripper can be approximately 30 cm in diameter (Neilsen et al, 1997); the innermost chamber located under the dripper therefore encompassed one-half of the diameter of the expected wetted area. 5.3 Gas sampling Targeted gas sampling campaigns were conducted on two consecutive days during three sampling events in 2013: (i) prior to the start of irrigation (May 7 and 8), (ii) during fertigation (June 11 and 12) and (iii) during irrigation after fertigation (August 14 And 15). In plots irrigated morning and afternoon each day, this represented two rounds of sampling under similar conditions. In plots irrigated morning and afternoon every second day, one sampling day was the irrigation day, and the other day was non-irrigation day. Gas samples were collected using pre-evacuated 12 mL double -wadded Exetainers (Part No: 737W, Labco Ltd) that were evacuated to 200 mTorr, flushed with helium, and re-evacuated to 200 mTorr. During chamber deployments, an insulated vented lid (with top closed, headspace ~10cm) was tightly fitted to the frames. Then, four samples were taken at different sampling intervals (0, 10, 20, and 30 min) and Exetainers were over-pressurized by injecting 20 mL of the gas sample using  a27 gauge, ½” needle that was connected to a 25-mL gas-tight syringe (catalog No. 03 378 207, National Scientific, Rockwood, TN). All chambers in each plot were deployed simultaneously and all three replicates in each treatment were samples in less than 2 hours to minimize diurnal variability. The samples were analyzed within one week by gas chromatography (Brucker 456) equipped with a CTC Combi-Pal auto sampler (CTC Analytics AG, Zwingen, Switzerland), a thermal conductivity detector (TCD), a flame ionization detector (FID), and an electron capture detector (ECD) for simultaneous analysis of CO2, CH4, and N2O, respectively. The emission of CH4 was negligible   83  in our system, so its spatial distribution was not investigated. The soil-surface N2O fluxes were determined by calculating slope (dC/dT), via either linear or non-linear regression (as appropriate) using the equations of Rochette and Hutchinson (2005). Non-linear regression (Hutchinson and Livingston, 1993) was used when the accumulation of N2O decreased with time. Linear regression was used when the accumulation of N2O was consistent with time (Rochette and Eriksen-Hamel, 2007). 5.3.1 Soil moisture and soil sampling In-situ volumetric soil water content measurements and soil sampling were conducted at end of the second day of gas sampling in each of the three events in 2013: (i) prior to start of irrigation (May 8), (ii) during fertigation (June 12), and (iii) during irrigation after fertigation (August 15). To avoid bias in the GHG measurement as a result of soil disturbance, soil sampling and volumetric soil water content measurements were avoided in the first day of GHG monitoring of each of the three events. Mulch was removed from the soil surface before moisture measurements or soil sampling were conducted; mulch removal was achieved by carefully scraping away the ~10 cm layer of mulch until the top soil was visible. The same mulch was replaced back after soil and moisture sampling. Volumetric soil water content to a depth of 5 cm was measured using a Decagon ProCheck hand-held moisture meter equipped with a 5 cm long, three-prong GS3 moisture probe (Decagon Devices, Inc., Pullman, WA). Measurements were collected near the four corners within the mini-chamber and at the center, and averaged to get representative volumetric soil water content values for each mini-chamber. Soil bulk density samples were collected at 15 cm and 30 cm away from the dripper and parallel to the tree row; these were used to represent a whole plot. All soil bulk densities were sampled from 3-9 cm depth using 6-cm diameter copper collars fitted to soil bulk density sampler. An estimate of the   84  fraction of water-filled pore space (WFPS) was calculated as:                        WFPS = (θv /[1-(ρb/pρ)])                      Equation 5.1                              Where ߠ௩ is the volumetric water content of the soil (cm3cm-3), ߩ௕ is the bulk density of the soil (g cm-3) and ߩ௣ is the approximate mineral particle density (2.65 g cm−3).  Soil cores to a depth of 15 cm were collected at the center of each mini-chamber using a 2-cm diameter auger following volumetric soil water content measurement. Soil samples were kept frozen until extraction and analysis. Field moist soils were extracted for exchangeable nitrate-N and nitrite-N (here after NO3--N , as concentrations of NO2--N was minimal ) and ammonium-N (NH4+-N) with 2 M KCl using a 1:5 soil to extractant ratio and a 1- h shaking time followed by filtration through Whatman No. 40 filter paper. Extracts were frozen at -20 OC and thawed overnight prior to analysis for NO3--N and NH4+-N using a segmented flow analyzer (SFA, Model 305D, Astoria Pacific International, Clackamas, OR). The analysis for salt-extractable organic carbon (SEOC) was conducted at the Analytical Laboratory of the B.C. Ministry of Environment. SEOC was extracted using 2 M KCl at a 1:5 soil extractant ratio, filtered through a 0.45-μm membrane filter (Millipore Corp, USA), stored at -20 C (Chantigny et al., 2008), and analyzed by Aurora 1030W OI Analytical TOC analyzer (OI Analytical, USA). Soil total C and N contents were measured by combusting 15-20 mg finely-ground, air-dried samples using a Costech 4010 Elemental Analyzer with thermal conductivity detection. CEC was measured using an ammonium acetate extraction buffered to pH 7. Soil pH was measured by pH-meter (WTW inoLab pH 7200) on an extract from 2:1 deionized water to finely-ground air-dried soil which was sifted through a 2.00 mm sieve. The soil samples were grounded using porcelain mortar and pestle. Additional soil sampling and nutrient availability across treatments over a period of two years in the growing season have been provided in Chapter 4.    85  5.3.2 Data presentation and statistical analysis All the data were log transformed (common logarithm, log10) to correct for both lack of normality and heterogeneity of the error variance. Effects of treatments, chamber location and management event on N2O and CO2 emissions were determined using the PROC MIXED procedure in SAS (Version 9.3; SAS® Institute, Inc., Cary, NC) with block, and block-by-irrigation treated as random effects. The sampling event before start of irrigation was excluded from the analysis because the effects of irrigation frequency cannot be assessed without the occurrence of irrigation. Pairwise means comparisons were performed using the PDIFF statement and Tukey-Kramer adjustment method. Multiple linear regression was performed on the transformed data to investigate the relationships between each soil parameter, distance from the tree (hereafter referred as DT), distance from the dripper (hereafter referred as DD) and N2O and CO2 emissions using the PROC REG procedure in SAS. All the data from the three events (before irrigation, during and after fertigation) for each treatment (or for combined treatments with no significant difference of GHG emission) were regressed together. Linear regression was first carried out using all of the measured GHG fluxes, soil parameters and distances for each of the fits. After the initial fit, the parameters that were not statistically significant (p > 0.05) were removed and the fit was run again using only the significant parameters. The spatial distribution of soil parameters and N2O and CO2 emissions (averaged over two days) was plotted in SigmaPlotR (Version 12.3; Systat Software, Inc., San Jose, CA) using raw untransformed data. 5.4 Results and discussion 5.4.1 Overview of initial results and data analysis   The mean air temperature was warmest on the sampling days after fertigation (August 14 and 15 of 2013) and coldest on the sampling days during fertigation (June 11 and 12 of 2013)   86  (Table 5.2). The mean soil temperature, however, was highest on the sampling days before start of irrigation (May 8 and 9 of 2013) and lowest on the sampling days during fertigation (June 11 and 12 of 2013). There were no rain events during gas and soil sampling. Irrigation amounts are shown in Table 5.2. As indicated previously only two orchard floor managements (Clean or Mulch) and two drip irrigation frequencies (every day or every second day) all receiving 40 N g tree-1 season-1 by fertigation were assessed in this study (Table 5.1). The correlation of soil parameters and GHG emissions in Mulch plots were much less strong than Clean plots (data not shown). This was also apparent in regression outputs and inconsistent spatial patterns of soil properties and GHG fluxes among replicated plots (e.g.; Figure C.3 and C.4, Appendix C). In the initial steps of determining the relation between GHG flux, soil parameters and distances from the tree or dripper, exclusion of data from mulched plots improved the correlation coefficient and model fitting for CO2 significantly (R2 ~0.5 with mulch included  and R2 > 0.7 with mulch excluded). The temporary removal of mulch during chamber installation and soil sampling may have disturbed the soil and caused higher variability in Mulch plots. It was difficult to recognize the exact interface between mulch and soil surface, therefore leaving some highly degraded mulch immediately at the interface. Consequently, only the data from Clean plots were considered for further analysis in this chapter; however, the results of the spatial distribution of soil parameters and GHG fluxes from all individual treatment have been provided in Appendix C (Figures C.1 to C.4). Only chamber location (L) and sampling event (E) and their interaction (L x E) significantly affected the magnitude of N2O and CO2 fluxes (Table C.1 in the appendix). Irrigation frequency did not significantly affected the N2O and CO2 fluxes and so data from both irrigation frequencies were merged for further analysis. Thus, the spatial relation of GHG   87  emission and soil parameters were determined by processing all data together (without mulch data). 5.4.2 Spatial patterns of WFPS, nutrients and GHG fluxes: Before irrigation Prior to the start of scheduled irrigation, the spatial distribution of WFPS, NO3--N, and N2O fluxes was generally uniform in the quadrant (Figure 5.2 and 5.3). WFPS was low (8-16%). As expected, the concentrations of nutrients and carbon were also low; NO3--N was 3-10 mg kg-1 and NH4+- N was less than 3 mg kg-1. Generally, NH4+- N was distributed uniformly in the quadrant. There was slight enhanced concentration of NH4+- N (~5 mg kg-1) around the dripper; this may have been caused by the application of small amount of water during irrigation system checkup by field crew a few days prior to start of scheduled irrigation. Net N mineralization increases with increasing moisture in semiarid ecosystems (Burke et al. 1997) and addition of water on the dry soil might have led to this slightly increased NH4+- N concentration around the dripper. The concentration of SEOC in most of the quadrant was below 200 mg kg-1, but there was enhanced concentration of SEOC (200-500 mg kg-1) in a radius of 20-30 cm around the stem of the apple tree. Nitrous oxide fluxes were evenly distributed and N2O-N fluxes were mostly below 10 µg m2 h-1. Carbon dioxide flux had a similar spatial pattern to SEOC. The CO2 flux was below 150 in mg m2 h-1 within most of the quadrant, with enhanced CO2 flux                           (200-250 mg m2 h-1) in a radius of 20-30 cm around the stem of the apple tree. The increased concentrations of SEOC and CO2 flux in the immediate vicinity of the apple tree could be a result of rhizodeposition and the consequent proliferation of microorganisms in the surrounding soil (Jones et al., 2004) following the start of the growing season.    88  5.4.3 Spatial patterns of WFPS, nutrients and GHG fluxes: During fertigation During fertigation, the concentrations of nutrients and carbon in the soil changed dramatically, especially in the vicinity of the trees and drippers (Figure 5.2 and 5.3). On average, the concentrations of: NO3--N increased ten times more; NH4+- N increased four times more; WFPS increased three times more; and SEOC, CO2 and N2O increased two times more (Table 5.3). Similarly, fluxes of CO2 and N2O increased two times more (Table 5.3). The dramatic increases of nutrients, carbon and GHG fluxes are not surprising given the application of water and nitrogen through the fertigation system and consequent growth of roots and heightening of microbial activity. The values of all soil variables (except WFPS) and GHG fluxes increased in a general circular pattern with the center at the tree. The concentrations of NO3--N, NH4+-N, and SEOC were depleted in the immediate vicinity of the stem of tree in a concentric circle of 10-20 cm radius. This may be because of relatively higher root density in that area and the demand for mineral N to meet high growth requirements in the season and for N and labile C for microbial activity. The distribution patterns of WFPS centered at the dripper with elevated WFPS occurring within a radius of 20-30 cm.  5.4.4 Spatial patterns of WFPS, nutrients and GHG fluxes: After fertigation (during irrigation) After fertigation (during irrigation), the spatial patterns of WFPS (Figure 5.2) remained generally the same as during the fertigation period; increased WFPS was observed in a 20-30cm  radius of the dripper. After fertigation, the WFPS ranged between 15% and 40% with a mean value of 26%; this was slightly lower (~3%) than the values of WFPS during fertigation. The lower mean WFPS may be because of higher near-surface evaporative loss due to higher daily soil temperature after fertigation (22.3 0C on August 15) than during fertigation (18.5 0C on June   89  12) (Table 5.2). The spatial patterns of NO3--N reversed after fertigation ceased; areas in the vicinity of dripper showed lower NO3--N while the concentration was higher at the edges of the quadrants away from the dripper and the tree. This was likely because of termination of N application and lateral movement and leaching of the nitrate that was applied during fertigation while irrigation by non-fertigation water was continued. Nitrate is a negative ion and does not adsorb to clay minerals and organic colloids that are negatively charged; hence irrigation can result in transport of nitrate away from the dripper. On average the concentrations of NO3--N decreased three times less after N fertigation was terminated (Table 5.3). The concentrations of NH4+-N also dropped five times less after N fertigation was terminated, reaching a uniform concentration (~2 mg kg-1) similar to the  background NH4+-N concentration measured before the start of irrigation  in May. The depletion of NH4+ after fertigation, despite net N mineralization because of wet soil conditions (Burke et al. 1997), may be because of continued N requirements for plant growth. Similar to before irrigation, there were higher concentrations of SEOC close to the apple tree after fertigation; however, the SEOC decreased two times less while the magnitude of CO2 fluxes around the apple tree remained same (Table 5.3).  The magnitude of N2O flux also decreased ~200% compared to during fertigation. The N2O flux “hot spot” was observed around the center of the quadrant, where both nitrogen and water were present at moderate levels compared to near the dripper where WFPS was higher while Mineral N was lower.  5.4.5 Spatial correlation between soil parameters and GHG fluxes Multiple linear regression (Table 5.4A) over the three events indicated that CO2 fluxes correlated strongest with distance from the tree followed by N2O fluxes and WFPS (r2=0.84; Table 5.4A). Areas close to the stem of tree likely had highest root densities, which can support various microorganisms that generate CO2. The areas with elevated CO2 fluxes generally   90  recorded higher N2O fluxes and were associated with increased levels of WFPS, an important influence on metabolic activity and the transport of nutrients for microbial proliferation. Graphs that show the relationships between distance from tree/dripper and CO2 flux and N2O flux have been provided in Appendix C (Figure C. 5 and C.6). Fluxes of N2O (log (N2O)) correlated strongly with NH4+, CO2 fluxes and SEOC (r2=0.88; Table 5.4B). The soil variable with the most significant correlation with N2O flux was NH4+. High concentrations of NH4+ and NO3--N usually increase N2O fluxes from soils as they are the primary nutrients required for the microbial processes of nitrification and denitrification (Davidson et al., 2000). In contrast to concentrations of NH4+, the concentration of NO3--N was not correlated significantly with N2O fluxes; this lack of correlation may be because of the abundance of NO3-in available in the soil. The average concentration of NO3--N in the soil was six fold higher than concentrations of NH4+ (Table 5.3). Flux of CO2 correlated negatively with distance from the tree. Averaged over the growing season, there was a mild positive correlation of N2O flux with distance away from the dripper. In addition, areas with elevated levels of SEOC recorded lower N2O flux (Figure 5.3 and Table 5.4B) possibly because of enhanced complete denitrification to N2 in the presence of labile organic carbon. This is consistent with the long-term N2O study (Chapter 4) where higher SEOC was associated with lower N2O flux. The effects of various managements on seasonal N2O production and consumption pathways at the sub-plot scale are being further investigated by studying fluxes and soil parameters together with the changes in the abundances of genes coding for enzymes associated with nitrification and denitrification as listed in Appendix D (Voegel and Geetkamal, personal communication).    91  5.4.6 Implications of spatial variability of GHG for chamber design and deployment location around dripper and tree In order to investigate the implications of spatial variability of GHG for chamber methodology, a chamber with arbitrary dimension (L=64cm and W= 22cm, hereafter called “medium chamber”) was used to model estimates of GHG that would have been measured by placing it at three locations (A, B and C) around the apple tree (Figure 5.5 A). In addition, the GHG emissions were estimated for two larger sized chambers (Figure 5.5 B): one with dimensions covering 100% of the quadrant (hereafter called “ideal chamber”) and another covering 82% of quadrant (hereafter called “long term-chamber” as its size is equal to the size of the chamber that was used for the 2-year GHG monitoring in the same orchard). Estimates of GHG fluxes for the three different sized chambers (medium, ideal, and long term) at different locations were calculated by area-weighed fluxes of the mini-chambers they would cover around the apple tree (Table 5.5, Figure 5.5). The dimensions of the mini-chamber used for calculations were based on coverage area of each chamber size and modeled location of deployment. Out of the modeled locations, deploying the medium chamber at the tree row (location “B” in Figure 5.5 B) would result in overestimation of fluxes of N2O by 36% and CO2 by 42%, with respect to the flux that would be measured by the ideal chamber. The best estimate, out of the modeled locations, for “actual flux” would be obtained by placing the medium chamber at location “C” in Figure 5.5 B. The long term-chamber overestimated the fluxes of N2O only by about 4%, while it estimated CO2 flux accurately.  The results of this GHG emission estimation demonstrate that chambers with a larger footprint provide more accurate results for row-crop systems like orchards and vineyards. The   92  use of this ideal chamber size may not be practical because of various factors including design cost, inconvenience of its size and weight for handling during gas sampling and potential damage it may cause tree roots because installation would require installation close to the tree stem. Thus, when the use of smaller chambers becomes necessary, deployment must take into account sufficient representation of the spatial variability around the trees and drippers. When possible, a preliminary fine-scale spatial study of the distribution of greenhouse gases and soil parameters is suggested before designing chamber methodology for long-term monitoring in micro-irrigated row-crop systems.  5.5 Summary Over three management events (before irrigation, during fertigation and after fertigation) in a drip-irrigated apple orchard, this study demonstrated the occurrence of large spatial variability of soil properties and GHG emissions with respect to distance from both the tree and dripper locations. Prior to the start of scheduled irrigation, the spatial distribution of WFPS,  NO3-, NH4+, salt-extractable organic carbon (SEOC), CO2 flux and N2O flux were generally uniform within the plots. However, during fertigation the values of all parameters, except WFPS, increased in a general circular pattern with the either the lowest of highest values centered at the tree. By contrast WFPS was elevated within 20 to 30 cm radius of the dripper. After fertigation (during irrigation), the shape of the spatial patterns of WFPS and CO2 remained generally the same as during the fertigation period. The spatial pattern of NO3- reversed and areas in the vicinity of dripper showed lower NO3- while the concentration was higher at the edges of the quadrants away from the dripper and the tree. The spatial pattern of NH4+ and SEOC became similar to the patterns observed before the start of irrigation. The N2O flux “hot spots” moved to the center of the quadrant, where both nitrogen and water appeared at moderate levels in contrast   93  to near the dripper where WFPS was higher while mineral N was lower. Modeled scenarios based on various chamber sizes and deployment locations indicated that a serious underestimation or overestimation of GHGs flux could result by disregarding this spatial variability when designing and locating sampling chambers especially for widely-spaced row-crop systems like orchards and vineyards where regularly imposed spatial application of water and fertilizer adds another layer of complexity to the already existing natural spatial variability.                                   94  Table 5.1: Subsets of treatments considered for spatial study in a drip-irrigated apple orchard located in the Okanagan Valley, British Columbia, Canada.   ZThe irrigation treatments consisted of irrigating twice (morning and afternoon) every day, or irrigating twice (morning and afternoon) every second day.                                  Irrigation Floor N Rate Treatment frequencyz management (g N tree-1 season-1) numberEvery day Clean 40 1Every day Mulch 40 2Every 2nd  day Clean 40 3Every 2nd  day Mulch 40 4  95  Table 5.2: Mean soil and air temperatures and irrigation amounts during gas and soil sampling days within three management events in a drip-irrigated apple orchard located in the Okanagan Valley British Columbia, Canada. ZThe irrigation treatments consisted of irrigating twice (morning and afternoon) every day, or irrigating twice (morning and afternoon) every second day.                          Events Date Mean  air Mean  soil temp. (°C) temp. (°C)Everyday Every 2nd day Before irrigation 7-May-13 20.1 22.8 - -8-May-13 19 23.1 - -Mean 19.6 23.0 - -During fertigation 11-Jun-13 19.2 19.6 5.8 11.112-Jun-13 16 18.5 5.2 0.0Mean 17.6 19.0 5.5 5.6After fertigation 14-Aug-13 24.1 22.2 7.6 14.8(during irrigation) 15-Aug-13 24.1 22.3 6.7 0.0Mean 24.1 22.2 7.1 7.4Irrigationz(mm)  96  Table 5.3: Mean values of extractable NO3--N, NH4+- N, salt-extractable organic carbon (SEOC) of soil (0-15 cm depth), water-filled pore space (WFPS) of soil (0-5 cm depth) and fluxes of CO2 and N2O measured inside 9 mini-chambers in the apple orchard during three events in 2013: prior to start of irrigation (BI, May 7 & 8), during fertigation (DF, June 11 & 12), and after fertigation during irrigation (AF, August 14 & 15). Soil variables were measured only on May 8, June 12 and August 15.  zThe mean of each variable was calculated from measurements taken from 9-mini-chambers deployed in the two irrigation frequencies of three replicates across an event (n = 9 x 2 x 3 = 54 for soil variables and n=108 for CO2 and N2O flux because fluxes were measured on two consecutive days.                              Eventz NO3--N NH4+-N SEDC WFPS CO2 N2O-N(mg kg -1) (mg kg -1) (mg kg -1) (%) (mg m-2h-1) (µg m-2h-1)Before irrigation 6.1 2.7 213.9 11.4 164.8 10.9During ferigation 63.4 10.0 380.9 29.3 266.3 26.0After fertigation 18.9 2.2 168.4 26.1 266.5 14.1  97  Table 5.4: Multiple linear regression and correlation of (A) N2O flux and (B) CO2 fluxes with soil properties, distance from tree (DT) and distance from dripper (DD). Only those regressions with p<0.05 are shown. Data shown in Figure 5.4.  zMultiple regression was performed using all variables ( WFPS, NO3--N, NH4+- N, SEOC, DT, DD) and either N2O or CO2  fluxes. r2 was 0.84 for CO2 and 0.88 for N2O  and p < 0.0001 for  both CO2 and N2O.     Parameter estimate SE p Value(regression coefficients)(A) Y=log (CO2 Flux, mg m2h-1)zIntercept 1.9963 0.1217 <.0001log (WFPS, %) 0.2295 0.0862 0.0139log N2O (µg m2 h-1) 0.1918 0.0686 0.0103log (DT, cm) -0.0034 0.0009 0.0012(B) Y=log (N2O Flux, µg m2h-1)zIntercept 0.8794 0.8539 0.3142log (NH+4 , mg kg-1) 1.1192 0.1955 <.0001log (SEOC, mg kg-1) -1.1322 0.3244 0.0021log (DD, cm) 0.0048 0.0023 0.0473log (CO2 mg m2 h-1) 0.8753 0.2139 0.0005  98  Table 5.5: Estimates of the accuracy of GHG fluxes measured using different chamber sizes and locations with respect to apple tree and dripper. Gas sampling in the mini-chambers occurred at three events in 2013: prior to start of irrigation (May 8), during fertigation (June 12), and during irrigation after fertigation (August 15).  zMini-chamber selection for flux estimation were based on coverage area of ideal chamber, medium chamber and long-term camber at the modeled location of deployment (Figure 5.5).  yErrors of N2O and CO2 fluxes were calculated as the % deviation between the Ideal Chamber and flux estimates each chamber size and location based on area-weighed fluxes measured by the mini-chambers.   Chamber type Size Location Coverage zFlux Estimate N2O Flux yN2O Flux CO2 Flux CO2 Flux Sampling date in 2013 of Based on Estimate Error Estimate ErrorzQuadrant mini-chamber # (µg m2h-1) (%) (mg m2h-1) (%)Ideal chamber 45cm x 75cm Fig. 5.5B 100% 1 to 9 16.0 NA 222.0 NA May 7-8; June 11-12; Aug 14-15Medium chamber 22cm x 64cm A in Fig. 5.5A 42% 1 to 3 20.8 29.7 253.4 14.2 May 7-8; June 11-12; Aug 14-1622cm x 64cm B in Fig. 5.5A 42% 4 and 5 21.8 36.0 316.0 42.4 May 7-8; June 11-12; Aug 14-1722cm x 64cm C in Fig. 5.5A 42% 7 to 9 14.1 -12.2 200.9 -9.5 May 7-8; June 11-12; Aug 14-18Long-term chamber 40cm x 69cm Fig. 5.5B 82% 1 to 9 16.6 3.6 221.9 0.0 May 7-8; June 11-12; Aug 14-19  99   Figure 5.1: Spatial representation of a single apple tree and mini-chambers locations (n=9) in a drip-irrigated plot. Two transects forming axes were designated, starting at the tree as the origin (0, 0); one axis (x-axis) was perpendicular to the tree row and into orchard alley, and the other (y-axis) directly in the tree row.    100   Figure 5.2:  Average spatial distributions of WFPS (%), NO3--N (mg kg-1), and NH4+- N (mg kg-1) measured inside 9 mini-chambers at three events in 2013: prior to start of irrigation (BI, May 8), during fertigation (DF, June 12), and after fertigation during irrigation (AF, August 15). Each parameter is color plotted using same scale across events (BI, DF, AF); contour lines are added when values fall outside of the color scale. For each parameter measured inside each mini chamber n=6 (i.e averages of three plots irrigated twice per day every day and three plots irrigated twice per day every other day).    101   Figure 5.3: Average spatial distribution of salt-extractable organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux (µg m2 h-1) measured inside 9 mini-chambers during three events in 2013: prior to start of irrigation (BI, May 7 & 8), during fertigation (DF, June 11 & 12), and after fertigation during irrigation (AF, August 14 & 15). SEOC was measured only on May 9, June 12 and August 15. Each parameter is color plotted using the same scale across events (BI, DF, AF). For SEOC measured inside each mini chamber, n=6. For CO2 and N2O flux, n=12; plotted values are the average of measurements on two consecutive days in three plots irrigated twice per day every day and three plots irrigated twice per day every other day.   102   Figure 5.4: Results of multiple linear regression used to identify relationships between (A) CO2 flux and (B) N2O flux with soil properties and distances from tree and dripper in an apple orchard (see Table 5.4 for fitting parameters). Averages of 6 measurements for each parameter (flux and soil parameter) were used in the regression analysis.    103    Figure 5.5: Spatial representation of a single apple tree in a drip-irrigated plot and locations of (A) mini-chambers (n=9) and medium chamber (n=3) locations and (B) mini-chambers (n=9), a long-term chamber and an “ideal chamber”. Two transects forming axes crossing at the tree and designated as the origin (0, 0); one transect (x-axis) was perpendicular to the tree row and into orchard alley, and the other (y-axis) directly in the tree row   104  Chapter 6: CONCLUSIONS AND RECOMMENDATION 6.1 Conclusions Agricultural production contributes to global warming through emission of greenhouse gases CO2, N2O, and CH4, the most potent of which is N2O. There is little information on how various agricultural management practices affect the long-term N2O emissions in woody perennial crops as most previous studies focus on intensively-managed annual cropping systems. This research assessed the effect of beneficial water-conserving management practices on greenhouse gas emissions (GHG) using three distinct experiments. The two long term experiments showed that treatment effects need to be studied over multiple years, and across all seasons. The effects of different water management strategies needs to be studied over the long term as these effects may not be apparent over short periods (days or months) during the growing season. For example micro-irrigation decreased N2O emissions during the growing season but not in all the seasons over the two years. Also, a considerable portion of the annual cumulative emission occurred during the pre-growing season, particularly within the thaw period. The seasonal and annual examination of N2O emissions, along with variable effects of treatments across seasons in a year and between years in this research, highlight the necessity of continuous long-term monitoring to capture enhanced N2O emission events (such as during spring thaw and rain events). Short-term N2O emission monitoring only during the growing season could lead to erroneous conclusions of treatment comparisons and underestimation of annual fluxes. The long-term N2O monitoring experiment at the apple site showed that lower drip irrigation frequency reduced N2O emissions significantly without affecting yield. Thus, orchardists can use micro-irrigation scheduling not only to effectively provide nutrients to   105  crops through the irrigation system via fertigation but also to mitigate N2O emissions. The use of micro-irrigation also allows growers to conserve water by matching water supply to plant demand and applying water only at or near the root zone where plants can use it most efficiently. Mulch of shredded bark and wood consistently reduced N2O emissions at the apple and grape sites without affecting productivity. This N2O-mitigation potential of mulch is a new addition to other well-known benefits of mulch such as weed control, reduction of water evaporation, prevention of soil erosion and buffering of soil temperature and pH. Thus, growers are advised to use mulch whenever local supplies are easily available.       Contrary to expectation, nitrogen application rate at the apple site did not cause significant differences in either area-scaled or yield-scaled N2O emissions, maybe due to the nitrogen-efficient application method (fertigation), which was synchronized with the nitrogen needs of the apple trees. Higher nitrogen application using inefficient application methods (such as broadcast) has a greater potential to exceed immediate N needs of plants thereby making the excess nitrogen vulnerable to losses. Consequently, higher nitrogen application rates using inefficient application methods may result in higher N2O emissions. Similarly, using compost instead of urea in the grape experiment did not result in significant differences in area-scaled and yield-scaled N2O emissions. Thus, from an N2O emission and yield stand point, there is no limitation for growers who want to adopt organic farming practices by using compost instead of urea.   The short-term spatial N2O and CO2 monitoring experiment at the apple site found large spatial variability of soil properties and GHG emissions with respect to distance from   106  the apple tree and dripper locations over three measurement events during different management stages (before irrigation, during and after fertigation). Both nitrification and denitrification may be variable spatially. Sub-plot scale mechanisms in N2O production and consumption pathways are being investigated by linking the flux and soil data with changes in the abundances of genes coding for enzymes associated with nitrification and denitrification as listed in Appendix D (Voegel and Geetkamal, personal communication). The short-term spatial N2O and CO2 monitoring experiment for apple confirmed that the chamber design and deployment location used for the long-term experiment was suitable for capturing the GHG footprint by accounting for spatial viability with respect to distances from drippers and apple trees.  6.2 Limitations and recommendations for future work The mechanism by which shredded bark and wood bark mulch reduces N2O emissions was not directly investigated and further investigation using isotopic and/or molecular techniques is warranted. Further investigation of the N2O mitigation potential of mulch in other crops and climates is suggested to test whether the effects of mulching are consistent under various experimental conditions. In addition, the extent to which mulch increases carbon emissions or sequestration in the vineyard and the orchard is not known. In woody perennial crops, permanent structures such as vines and trees may provide an opportunity for carbon sequestration. Management practices such as mulching may increase temporary carbon storage in permanent vine and tree structures. However, the longer-term fate of sequestered carbon depends on what is done with the vine and tree structures at the end of the vineyard or orchard lifecycle (Carlisle et al, 2011). Therefore, further work is required to develop life cycle carbon budgets for vineyards and the orchards.    107  This study also did not measure the relative contribution of leaching versus gaseous losses of nitrogen from the system. Leaching losses are expected to dwarf losses of nitrogen through N2O. Nitrogen leaching losses can account for 20-30% or more of applied nitrogen in sandy soils (Neilsen et al., 2008). Future experiments comparing the effects of irrigation type and frequency, mulching, nitrogen source and rate application on leaching will be important.  This research has one of the longest monitoring durations for experiments examining the effects of various management practices in a fruit production system. However, two years may still be a short time to fully understand long-term changes in C and N status of the soil over time, particularly in response to bark mulch decomposition. Longer-term monitoring of N2O emission may be needed for higher certainty. Future spatial experiments can be improved by monitoring GHG emissions on more days (>2) to improve data quality and would also benefit from inclusion of other spatial parameters such as measurements of bacterial abundance and activity, and root distribution.   6.3 Originality and contributions  Micro-irrigation, fertigation and mulching have been proposed to improve the nutrient and water-use efficiency of crop production. This research contributed by studying the long-term effect of these managements in a vineyard and orchard. With two-year continuous monitoring at a Merlot Grape (Vitis vinifera) vineyard and an Ambrosia apple orchard (Malus domestica Borkh), this research found surface application of shredded bark and wood consistently reduced N2O emissions. Other agroecosystems in various climates may benefit from similar effects of mulching on N2O emission.   108   The grape vineyard research also found that micro-sprinkler irrigation reduced N2O emissions by about 29% over the irrigation season relative to drip irrigation. The apple experiment showed that drip irrigation frequency can alter emissions.   This research elicited significantly lower N2O emissions by reducing drip micro-irrigation frequency from every day to every second day. The water-conserving benefit of micro-irrigation techniques can be extended to achieve N2O mitigation benefits by altering irrigation frequency used in woody perennial crops and other crops.   This research also found average direct emission factors (amount of N emitted as N2O per unit of total applied N) of 2.8% at the grape site and 2.4% at the apple site, which means the use of a 1% direct emission factor, which is IPCC default, could lead to significant underestimation of N2O emissions from micro-irrigated woody perennial crops in semiarid regions. The emission factors from the grape vineyard and apple orchard obtained in this research will be added to databases of emissions factors and can be used to improve quantitative models that forecast useful information to farmers, environmental regulators and policy makers.  The results of this research were used by other researchers working on the same site to study nutrient dynamics and  the changes in the abundances of genes coding for enzymes associated with nitrification (amoA) and denitrification (nirS, nosZ) in response to the various agricultural management practices over two years. The reference listings of the studies where, the data from this research were used, are provided in Appendix D.  109   BIBLIOGRAPHY Abalos, D., Sanchez-Martin, L, Garcia-Torres, L, Van Groenigen, J.W., Vallejo, A., 2014. Management of irrigation frequency and nitrogen fertilization to mitigate GHG and NO emissions from drip-fertigated crops. Sci. Total Environ. 490, 880-888. Addiscott, T.M., 2005. Nitrate, agriculture, and the environment. CABI Publishing, Wallingford, Oxfordshire, UK. Allaire, S.E, Lange, S.F., Lafond, J.A., Pelletier, B., Cambouris, A.N., Dutilleul, P., 2012. Multiscale spatial variability of CO2 emissions and correlations with physico-chemical soil properties. Geoderma. 170, 251-260. Alluvione, F., Bertora, C., Zavattaro, L., Grignani. C., 2010. Nitrous oxide and carbon dioxide emissions following green manure and compost fertilization in corn. Soil Sci. Soc. Am. J. 74, 384-395.  Alsina, M. M., Fanton-Borges, A. C., Smart, D. R., 2013. Spatiotemporal variation of event related N2O and CH4 emissions during fertigation in a California almond orchard. Ecosphere 4, (1):1.  Ambus, P., Zechmeister-Boltenstern, S., and Butterbach-Bahl, K. 2006. Sources of nitrous oxide emitted from European forest soils. Biogeosciences 3, 135–145, in: Braker, G. and Conrad, R. 2011. Diversity, structure and size of N2O-producing microbial communities - what matters for their functioning? Adv. Appl. Microbiol. 75, 33-70. Aragüés, R., Medina, T.E., Clavería, I., 2014. Effectiveness of inorganic and organic mulching for soil salinity and sodicity control in a grapevine orchard drip-irrigated with moderately saline waters. Span. J. Agric. Res. 12, 501-508 Armstrong, N.M., 2015. The fate of fertilizer nitrogen in raspberry production as affected by nitrogen inputs and irrigation regime: an experiment in the Fraser Valley, British Columbia. M.Sc. Thesis, Earth & Environmental Sciences and Physical Geography, University of British Columbia Okanagan, Kelowna, BC.  Asgedom, H., Tenuta, M., Flaten, D.N.,Gao, X., Kebreab, E., 2013. Nitrous oxide emissions from a clay soil receiving granular urea formulations and dairy manure. Agron. J. 105, 1-13. Baggs, E., Smales, C. and Bateman, E., 2010. Changing pH shifts the microbial source as well as the magnitude of N2O emission from soil, in: Braker, G. and Conrad, R. 2011. Diversity, structure and size of N2O-producing microbial communities - what matters for their functioning? Advances in Appl. Microbiol. 75, 33-70.     110  Barnard, R., Leadley, P. W., Hungate, B. A., 2005. Global change, nitrification, and denitrification: A review. Global Biogeochem. Cycles 19, GB1007. Bateman, E.J., Baggs, E.M., 2005. Contributions of nitrification and denitrification to N2O emissions from soils at different water-filled pore space. Biol. and Fertil. Soils 41, 379-388.  BCGA, 2014. British Columbia Grapegrowers Association. B.C. Wine Grape Acreage Report, [Online] (accessed 12.03.2016) Available: http://grapegrowers.bc.ca/pdf/2014AcreageSurveyReport.pdf  Beare, M.H., Gregorich, E.G., St-Georges, P., 2009. Compaction effects on CO2 and N2O production during drying and rewetting of soil. Soil Biol. Biochem. 41, 611-621. Bock, E., Koops, H.P., Moller, U.C., Rudert, M., 1990. A new facultatively nitrite oxidizing bacterium, Nitrobacter-vulgaris sp-nov. Arch. Microbiol. 153, 105-110. Bollmann, A. and R. Conrad, 2004. Influence of O2 availability on NO and N2O release by nitrification and denitrification in soils, in: Braker, G. and Conrad, R. 2011. Diversity, structure and size of N2O-producing microbial communities - what matters for their functioning? Adv. Appl. Microbiol. 75, 33-70. Boone, RD., Nadelhoffer, K.J., Canary, J.D., Kaye, J.P., 1998. Roots exert a strong influence on the temperature sensitivity of soil respiration. Nature. 396, 570-572. Bowden, R.D., Nadelhoffer, K.J, Boone, R.D., Melillo, J.M., Garrison, J.B., 1993. Contributions of aboveground litter, belowground litter, and root respiration to total soil respiration in a temperate mixed hardwood forest. Can. J. Forest Res. 23, 1402-1407. Box, G. E. P. and Cox, D. R., 1964. An analysis of transformations, J. Roy. Statist. Soc. Ser. B, 26, 211-252.  Braker, G. and Conrad, R., 2011. Diversity, structure and size of N2O-producing microbial communities - what matters for their functioning? Adv. Appl. Microbiol. 75, 33-70. Brown C. Manure Sulphur − Digging Deeper, 2012. [Online] (accessed 12.03.2016). Available: http://www.regionalscia.org/Croptalknov12.pdf. Brown, C., 2012, Manure Sulphur - Digging Deeper. Ministry of Agriculture Food and Rural Affairs, Ontario. http://www.omafra.gov.on.ca/english/crops/field/news/croptalk/2012/ct-1112a2.htm (accessed 10.01.2015).  Burke, I. C., Lauenroth, W. K., Parton, W. J., 1997. Regional and temporal variation in net primary production and nitrogen mineralization in grasslands. Ecology 78, 1330-1340.   111  Carlisle, E., Smart, D., Williams, L.E., Summers, M., 2010. California vineyard greenhouse gas emissions: Assessment of the Available Literature and Determination of Research Needs. California sustainable wine growing alliance publication, San Francisco, CA. http://www.sustainablewinegrowing.org/docs/GHGreport.pdf (accessed 07.07.2014). Chantigny, M.H. Angers, D.A., Kaiser, K., Kalbitz, K, 2008. Extraction and characterization of dissolved organic matter, in: Carter, M.R., and Gregorich E.G. Soil Sampling and Methods of Analysis (Eds.), Canadian Society of Soil Science, CRC Press, Boca Raton, Florida, 617-635. Chantigny, M.H., Rochette, M.H. P., Angers, D.A., Bittman, S., Buckley,K., Massé, D., Belanger, G., Eriksen-Hamel, N. and Gasser, M.O., 2010. Soil nitrous oxide emissions following band-incorporation of fertilizer nitrogen and swine manure. J. Environ. Qual. 39, 1545-1553.  Clemens, J., Schillinger, M.P., Goldbach, H., Huwe, B., 1999. Spatial variability of N2O emissions and soil parameters of an arable silt loam - a field study. Biol. Fert. Soils 28, 403-406. Cochran V.L, Sparrow, E.B., Schlentner, S.F., Knight, C.W., 1997. Long-term tillage and crop residue management in the subarctic: fluxes of methane and nitrous oxide. Can. J. Soil Sci. 77, 565-570. Čuhel, J., Simek, M., Laughlin, R.J., Bru, D., Cheneby, D., Watson, C.J, Philippot, L., 2010. Insights into the effect of soil pH on N2O and N2 emissions and denitrifier community size and activity. Appl. Environ. Microbiol. 76, 1870-1878.  Davidson, E. A., Keller, M., Erickson, H. E., Verchot, L. V., and Veldkamp, E., 2000. Testing a conceptual model of soil emissions of nitrous and nitric oxides, Bioscience, 50, 667-680. Environment Canada, 2014a. Canadian climate normals or averages, for Summerland and Penticton, BC. http://climate.weather.gc.ca/climate_normals/ results_1981_2010_e.html (accessed 13.02.2014). Environment Canada, 2014b. Canadian climate data on-line customized search. Canada’s National Climate Archive, for Summerland and Penticton, BC. http://www.climate. weatheroffice.gc.ca/advanceSearch/searchHistoricData_e.html (accessed 13.02.2014).  Environment Canada, 2015. Reducing Canada’s greenhouse gas emissions. https://www.ec.gc.ca/dd-sd/default.asp?lang=En&n=AD1B22FD-1 (accessed 28.11.2015). Fang, C., Moncrief, J.B., Gholz, H.L., Clark, K.L., 1998. Soil CO2 efflux and its spatial variation in a Florida slash pine plantation. Plant and Soil. 205, 135-146.   112  FAO, Food and Agriculture Organization of the United Nations Statistics Division, 2013. http://faostat3.fao.org/download/Q/QC/E (accessed 15.12.2015).  Fentabil, M.M., Nichol, C., Neilsen, G., Hannam, K.D., Neilsen, D., Forge, T., Jones, M. 2016. Effect of micro-irrigation type, N-source and mulching on nitrous oxide emissions in a semi-arid climate: An assessment across two years in a Merlot grape vineyard. Agri. Water Manag. (in press) DOI: 10.1016/j.agwat.2016.02.021 Forge, T.A., Hogue, E.J., Neilsen, G., Neilsen, D., 2008. Organic mulches alter nematode communities, root growth and fluxes of phosphorus in the root zone of apple. Appl. Soil Ecol. 39, 15-22. Forge, T.A., Temple, W., Bomke, A.A., 2013. Using compost as mulch for high bush blueberry, Acta Horti. (ISHS), 1001, 369-376. Gagnon, B., Ziadi, N., Rochette, P., Chantigny, M.H., Angers, D.A., 2011. Fertilizer source influenced nitrous oxide emissions from a clay soil under corn. Soil Sci. Soc. Am. J. 75, 595-604 Gale, E.S., Sullivan, D.M., Cogger, C.G., Bary, A.I., Hemphill, D.D., and Myhre, E.A, 2006. Estimating plant-available nitrogen release from manures, composts, and specialty products. J. Environ. Qual. 35, 2321-2332.  Garcia, J.L. and Tiedje, J.M., 1982. Denitrification in rice soils. p. 187-200, in: Dommergues, Y., Diem, H. (eds.) Microbiology of tropical soils and plant productivity. The Hague/Boston/London. Martinus Nijhoff publishing. Garland, G.M., Suddick, E., Burger, M., Horwath, W.R., Six, J., 2014. Direct N2O emissions from a Mediterranean vineyard: Event-related baseline measurements. Agric. Ecosyst. Environ. 195, 44-52. Garland, G.M., Suddicka, E., Burgerb, M., Horwathb, W.R., Six, J., 2011. Direct N2O emissions following transition from conventional till to no-till in a cover cropped Mediterranean vineyard (Vitis vinifera). Agric. Ecosyst. Environ. 144, 423-428.  Groffman, P.M., Butterbach-Bahl, K., Fulweiler, R.W., Gold, A.J., Morse, J.L., Stander, E.K., Tague, C., Tonitto, C., Vidon, P., 2009. Challenges to incorporating spatially and temporally explicit phenomena (hotspots and hot moments). Biogeochemistry 93, 49-77.  Halvorson, A.D., Del Grosso, S.J., Alluvione,F., 2010. Nitrogen source effects on nitrous oxide emissions from irrigated no-till corn, J. Environ. Qual., 39, 1554-1562.  Hannam, K.D., Kehila, D., Millard, P., Midwood, A.J., Neilsen, D., Neilsen, G.H., Forge, T.A., Nichol, C., Jones, M.D., 2015. Bicarbonates in irrigation water contribute to carbonate formation and CO2 production in orchard soils under drip irrigation. Geoderma, 266, 120-126.   113  Hannam, K.D., Neilsen, G.H., Forge, T., Neilsen, D., Losso, I., Jones, M.D., Nichol, C., Fentabil, M.M. 2016. Irrigation practices, nitrogen amendments and mulches affect nutrient dynamics in a young Merlot (Vitis vinifera L.) vineyard. Can. J.of Soil Sci. (in press). DOI: 10.1139/cjss-2014-0118.  Hanson, B.R., Šimůnek, J. and Hopmans, J.W. 2006. Evaluation of urea–ammonium–nitrate fertigation with drip irrigation using numerical modeling. Agr. Water Manag. 86, 102-113. Herrmann, A., Witter, E., 2002. Sources of C and N contributing to the flush in mineralization upon freeze-thaw cycles in soils. Soil Biol Biochem. 34, 1495-1505. Holst, J., Liu, C., Yao, Z., Brüggemann, N., Zheng, X., Giese, M., Butterbach-Bahl, K., 2008. Fluxes of nitrous oxide, methane and carbon dioxide during freezing-thawing cycles in an Inner Mongolian steppe. Plant Soil 308, 105-117. Homyak, P.M., Yanai, R.D., Burns, D.A., Briggs, R.D., Germain, R.H., 2008. Nitrogen immobilization by wood-chip application: Protecting water quality in a northern hardwood forest. For. Ecol. Manage. 255, 2589-2601.  Hutchinson, G.L. and Livingston, G.P., 1993. Use of chamber systems to measure trace gas fluxes. In: Rolston, D.E and others (EDs), Agro-ecosystem effects on trace gases and climate change, ASA Spec. Publ. 55, ASA, CSSA, and SSSA, Madison, WI, pp. 63-78. IPCC, 2006. 2006 IPCC guidelines for national greenhouse gas inventories, prepared by the National Greenhouse Gas Inventories Programme. Institute for Global Environmental Strategies, Japan. IPCC, 2007. Climate change 2007: The physical science basis: Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New York.  IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Work-ing Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, USA.  Izaurralde, R. C., Lemke, R. L., Goddard, T. W., McConkey, B., Zhong, Z., 2004. Nitrous oxide emissions from agricultural toposequences in Alberta and Saskatchewan. Soil Sci. Soc. Am. J. 68, 1285-1294.  Jones, D.L., Hodge, A., Kuzyakov, Y., 2004. Plant and mycorrhizal regulation of rhizodeposition. New Phytologist. 163, 459-480.    114  Kester, R.A., Meijer, M.E., Libochant, J.A., Boer, W.D. and Laanbroek, H.J., 1997. Contribution of nitrification and denitrification to the NO and N2O emissions of an acid forest soil, a river sediment and a fertilized grassland soil, in: Braker, G. and Conrad, R. 2011. Diversity, structure and size of N2O-producing microbial communities - what matters for their functioning? Adv. Appl. Microbiol. 75, 33-70.  Ki-moon, B. June 23, 2009. Secretary-general’s comments at a joint press conference on climate change. New York, United Nations. http://www.un.org/apps/news/infocus/sgspeeches/statments_full.asp?statID=525 (accessed 21.08.2009).  Kellman, L., Kavanaugh, K., 2008. Nitrous oxide dynamics in managed northern forest soil Profiles: is production offset by consumption. Biogeochem. 90, 115-128. Knowles, R., 1982. Denitrification, Microbiol. Rev. 46, 43-70. Koponen, H. T. and Martikainen, P. J., 2004. Soil water content and freezing temperature affect freeze-thaw related N2O production in organic soil. Nutr. Cycl. Agroecosys. 69, 213-219. Laidlaw, J. W., 1993. Denitrification and Nitrous Oxide Emission in Thawing Soil. M.Sc. Thesis, Department of Soil Science, University of Alberta; Edmonton, AB. Lemke, R. L., Izaurralde, R.C., Nyborg, M., Solberg, E.D., 1999. Tillage and N source influence soil-emitted nitrous oxide in the Alberta Parkland region. Can. J. Soil Sci. 79, 15-24. Li, H., Qiu, J., Wang, L., Yang, L., 2011. Advance in a terrestrial biogeochemical model-DNDC model. Acta Ecologica Sinica 31, 91-96. Lin, S., Iqbal, J., Hu,R. Feng, M., 2010. N2O emissions from different land uses in mid-subtropical China. Agric. Ecosyst. Environ. 136, 40-48. Lin, S., Iqbal, J., Hu,R., Ruan, L., Wu, J., Zhao,J., Wang, P., 2012. Differences in nitrous oxide fluxes from red soil under different land uses in mid-subtropical China. Agric. Ecosyst. Environ. 146, 168-178.  Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., Schabenberger, O., SAS for Mixed Models, second ed., 2006, SAS Institute, Inc., Cary, NC. Livesley, S.J., Dougherty, B.J., Smith, A.J., Navaud, D., Wylie, L.J., Arndt, S.K., 2010. Soil-atmosphere exchange of carbon dioxide, methane and nitrous oxide in urban garden systems: impact of irrigation, fertiliser and mulch. Urban Ecosystems 13, 273–293.    115  Lopez-Fernandez, S., Diez, J.A., Hernaiz, P., Arce A., Garcia-Torres, L., Vallejo, A., 2007. Effects of fertiliser type and the presence or absence of plants on nitrous oxide emissions from irrigated soils. Nutr. Cycl. Agroecosyst. 78, 279-289.  Maharjan, B., Venterea,R.T., Rosen, C., 2014. Fertilizer and Irrigation Management Effects on Nitrous Oxide Emissions and Nitrate Leaching. J. Am. Soc. Agron. 106, 703-714 Maier, R.M., Pepper, I.L. and Gerba, C.P., 2009. Environmental microbiology, second ed. Elsevier, Amsterdam.  McClain, M.E., Boyer, E.W., Dent, C.L., Gergel, S.E., Grimm, N.B., Groffman, P.M., Hart, S.C., Harvey, J.W., Johnston, C. a., Mayorga, E., McDowell, W.H., Pinay, G., 2003. Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems 6, 301-312. Millar, N., Robertson, G.P., Diamant, A., Gehl, R.J., Grace, P.R., Hoben, J.P., 2012. Methodology for Quantifying Nitrous Oxide (N2O) Emissions Reductions by Reducing Nitrogen Fertilizer Use on Agricultural Crops 2012, American Carbon Registry, Winrock International, Little Rock, Arkansas. Miller, A., Cramer, M., 2005. Root nitrogen acquisition and assimilation. Plant Soil, 274,     1-36. Mørkved P.T., Dörsch P., Henriksen T.M., Bakken, L.R., 2006. N2O emissions and product ratios of nitrification and denitrification as affected by freezing and thawing. Soil Biol. Biochem 38, 3411-3420. Mosier, A.R., Halvorson, A.D., Peterson, G.A., Robertson, G.P., Sherrod, L., 2005 Measurement of net global warming potential in three agroecosystems. Nutr. Cycl. Agroecosys. 72, 67-76. Moyano, F.E., Atkin, O.K., Bahn, M., Bruhn, D., Burton, A.J., Heinemeyer, A., Kutsch W.L., and Wieser, G., 2009. Respiration from roots and the mycorrhizosphere, in: Kutsch, W.L., Bahn, M., Heinemeyer, A. (eds.), Soil carbon dynamics: An integrated methodology. Cambridge University Press, Cambridge, UK, pp. 127-156. Müller, C., Martin, M., Stevens, R.J., Laughlin, R.J., Kammann, C., Ottow, J.C.G., Jäger, H.J., 2002. Processes leading to N2O emissions in grassland soil during freezing and thawing, Soil Biol. Biochem. 34, 1325-1331. Neilsen, C. B., Groffman, P. M., Hamburg, S. P., Driscoll, C. T., Fahey, T. J. and Hardy, J. P., 2001. Freezing effects on carbon and nitrogen cycling in northern hardwood forest soils. Soil Sci. Soc. Am. J. 65, 1723-1730.    116  Neilsen, D., Neilsen, G. H., Gregory, D., Forge, T., Zebarth, B. J., 2008. Drainage losses of water, N and P from micro-irrigation systems in a young, high density apple planting. Acta Hort. 792, 483-490 Neilsen, D., Van Der Gulik, T, Cannon, A., Taylor, B., and Fretwell, R. 2015. Modeling future water demand for current and future climate in the Okanagan basin, B.C, Canada. Acta Hort. 1063, 211-218. Neilsen, G.H., Forge, T.A., Angers, D.A., Neilsen, D., Hogue, E.J., 2014. Suitable orchard floor management strategies in organic apple orchards that augment soil organic matter and maintain tree performance. Plant Soil, 378 (1-2), 325-335. Neilsen, G.H., Parchomchuk, P., Berard, R., Neilsen, D., 1997. Irrigation frequency and quantity affect root and top growth of fertigated ‘McIntosh’ apple on M.9, M.26 and M.7 rootstock, Can. J. Plant Sci. 77(1), 133-139. Nyamadzawo G., Shi Y., Chirinda N., Olesen J.E., Mapanda, F., Wuta, M., Wu, W., Meng, F., Oelofse, M., de Neergaard, A., Smith, J., 2014. Combining organic and inorganic nitrogen fertilisation reduces N2O emissions from cereal crops: a comparative analysis of China and Zimbabwe. Mitig. Adapt. Strateg. Glob. Nyborg, M., Laidlaw, J. W., Solberg, E. D., Mahli, S. S., 1997. Denitrification and nitrous oxide emissions from a Black Chernozemic soil during spring thaw in Alberta. Can. J. Soil Sci.77, 153-160. Panek, J., Matson, P., Ortiz-Monasterio, I. and Brooks, P., 2000. Distinguishing nitrification and denitrification sources of N2O in a Mexican wheat system using 15N, in: Braker, G. and Conrad, R. 2011. Diversity, structure and size of N2O-producing, microbial communities - what matters for their functioning? Adv. Appl. Microbiol, 75, 33-70.  Pang, J., Wang, X., Mu, Y., Ouyang, Z., Liu, W., 2009. Nitrous oxide emissions from an apple orchard soil in the semiarid Loess Plateau of China. Biol. Fertil. Soils 46, 37-44. Paul, J.W., 2013. Feasibility of using compost in raspberry production: A preliminary review and economic analysis of materials and processes available in the Fraser Valley. Agri. and Agri-Food Canada. http://www.transformcompost.com/docs/ Compost%20for%20Raspberries%20Feasibility%20Report%20Feb%2014%20final.pdf (accessed 21.08.2014) Pelster,D.E., Chantigny,M.H., Rochette, P., Angers, D.A., Rieux, C., Vanasse, A., 2012. Nitrous oxide emissions respond differently to mineral and organic nitrogen sources in contrasting soil types. J. Environ. Qual. 41, 427- 435.  Prosser, J.I. and Embley, T.M., 2002. Cultivation-based and molecular approaches to characterisation of terrestrial and aquatic nitrifiers. Antonie Van Leeuwenhoek. 81, 165-179.   117  Rafferty, J.P., 2011. Climate and climate change. Britannica Educational Publishing in association with Rosen Educational Services, New York, NY.  Reay, D.S., Davidson, E.A., Smith, K.A., Smith,P., Melillo, J.M. ,  Dentener, F. and Crutzen. P.J., 2012. Global agriculture and nitrous oxide emissions. Nature Clim. Change. 2, 410-416. Risk, N., Snider, D., Wagner-Riddle, C., 2013. Mechanisms leading to enhanced soil nitrous oxide fluxes nduced by freeze-thaw cycles. Can. J. Soil Sci. 93, 401-414. Rochette, P. and Hutchinson, G.L., 2005. Measuring soil respiration in situ: Chamber techniques, in: Carter, M.R., Gregorich, E.G.(Eds), Soil Sampling and Methods of Analysis, Can. Soc Soil Sci. CRC Press, Boca Raton, FL, pp. 851-861. Rochette, P., Eriksen-Hamel, N.S., 2007. Chamber measurements of soil nitrous oxide flux: Are absolute values reliable? Soil Sci. Soc. Am. J. 72, 331-342.  Rogner, H.H., Zhou, D, Bradley, R., Crabbé, P., Edenhofer, O., Hare, B., Kuijpers, L., Yamaguchi, M., 2007: Introduction, in: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A. (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Rolston, D.E, Sharpley, A.N., Toy, D.W., Broadbent, F.E., 1982. Field measurement of denitrification: III. Rates during irrigation cycles. Soil Sci. Soc. Am. J. 46, 289-296. Rolston, D.E, Sharpley, A.N., Toy, D.W., Broadbent, F.E., 1982. Field measurement of denitrification: III. Rates during irrigation cycles. Soil Sci. Soc. Am. J. 46, 289-296. Rothstein, D.E., Cregg, B.M., 2005. Effects of nitrogen form on nutrient uptake and physiology of Fraser fir (Abies fraseri). For. Ecol. Manag. 219, 69-80.  Ruiz-Romero, E., Alcántara-Hernández, R., Cruz-Mondragon, C., Marsch, R., Luna-Guido, M.L., Dendooven, L., 2009. Denitrification in extreme alkaline saline soils of the former lake Texcoco. Plant Soil. 319, 247-257. Russow, R., Spott, O., Stange, C.F., 2008. Evaluation of nitrate and ammonium as sources of NO and N2O emissions from black earth soils (Haplic Chernozem) based on 15N field experiments. Soil Biol Biochem. 40, 380-91. Sanchez-Martin, L., Arce, A., Benito, A., Garcia-Torres, L., Vallejo, A., 2008. Influence of drip and furrow irrigation systems on nitrogen oxide emissions from a horticultural crop. Agric. Ecosyst. Environ. 40, 1698-1706.     118  Sanchez-Martin, L., Meijide, A., Garcia-Torres, L., Vallejo, A., 2010. Combination of drip irrigation and organic fertilizer for mitigating emissions of nitrogen oxides in semiarid climate. Agric. Ecosyst. Environ. 137, 99-107.  Schellenberg, D.L., Alsina, M.M., Muhammad, S., Stockert, C.M., Wolff, M.W., Sanden, B.L., Brown, P.H., Smart, D.R., 2012.Yield-scaled global warming potential from N2O emissions and CH4 oxidation for almond (Prunus dulcis) irrigated with nitrogen fertilizers on arid land. Agric. Ecosyst. Environ. 155, 7-15.  Senbayram, M.,Chen, R., Mühling, K.H. and  Dittert, K., 2009. Contribution of nitrification and denitrification to nitrous oxide emissions from soils after application of biogas waste and other fertilizers, in: Braker, G. and Conrad, R. 2011. Diversity, structure and size of N2O-producing microbial communities - what matters for their functioning? Adv. Appl. Microbiol. 75, 33-70. Seymour, R., 2015. Okanagan grape growers say apples the future. The Daily Courier. http://www.kelownadailycourier.ca/news/article_c3fe38c4-51f8-11e5-8efa-fb1c06161fde.html (accessed 15.10.2015). Shunfeng, G.E., Yuanmao, J., Shaochong, W., 2015. Gross nitrification rates and nitrous oxide emissions in an apple orchard soil in northeast China. Pedosphere. 25, 622-630. Siciliano, S.D., Ma, W.K. Ferguson, S. and Farrell, R.E., 2009. Nitrifier dominance of arctic soil nitrous oxide emissions arises due to fungal competition with denitrifiers for nitrate, in: Braker, G. and Conrad, R. 2011. Diversity, structure and size of N2O-producing microbial communities - what matters for their functioning? Adv. Appl. Microbiol. 75, 33-70. Smart, D.R., Alsina, M.M., Wolff, M.W, Matiasek, M.G., Schellenberg, D.L., Edstrom, J.P., Brown, P.H., Scow, K.M., 2011. N2O emissions and water management in California perennial crops, in: Guo, L., Gunasekara, A.M., McConnell, L.L. (Eds.), Understanding greenhouse gas emissions from agricultural management. ACS Symposium Series, Am. Chem. Soci., Washington, DC, pp. 227-255.  Smith, P., Daniel, M., Zucong, C., Daniel, G., Henry, J., Pushpam, K., Bruce, M., Stephen, O., Frank, O, Charles, R., Bob, S., Oleg, S., Mark, H., Tim, M., Genxing, P., Vladimir, R., Uwe, S., Sirintornthep, T., Martin, W., Jo, S., 2008. Greenhouse gas mitigation in agriculture. Philosophical Transactions of the Royal Society B: Biological Sciences 363, 1492, 789-813. Steenwerth K.L., Belina, K.M., 2010.Vineyard weed management practices influence nitrate leaching and nitrous oxide emissions. Agric. Ecosyst. Environ. 138, 127-131.      119  Suddick, E. C., Steenwerth, K. Garland, G. M., Smart, D. R., Six, J., 2011. Discerning agricultural management effects on nitrous oxide emissions from conventional and alternative cropping systems: A California case study, in: Guo, L., Gunasekara, A.M., McConnell, L.L. (Eds.), Understanding Greenhouse Gas Emissions from Agricultural Management. ACS Symposium Series, Am. Chem. Soci., Washington, DC, pp. 203-226.  Tatti, E., Goyer, C., Chantigny, M.H., Wertz, S., Zebarth, B.J., Burton, D.L., Filion, M., 2014. Influences of over winter conditions on denitrification and nitrous oxide-producing microorganism abundance and structure in an agricultural soil amended with different nitrogen sources. Agric. Ecosyst. Environ., 183, 47-59.  The Guardian, 2015. COP 21: UN climate change conference, Paris. Paris climate summit: world leaders told to iron out differences before talks end. http://www.theguardian.com/environment/2015/nov/28/paris-climate-summit-world-leaders-talks-france (accessed 28.11.2015).  Van den Heuvel, R.N., 2010. Nitrous oxide emission hotspots and acidic soil denitrification in a riparian buffer zone. Ph.D. thesis, Utrecht University, Faculty of Science. Van Dongen, L., Jetten, M., Van Loosdrecht, M., 2001. The SHARON®-Anammox® process for the treatment of ammonium rich wastewater. Water Sci. Technol. 44, 153-160. Venterea, R.T., Maharjan, B., Dolan, M.S., 2011. Fertilizer source and tillage effects on yield-scaled nitrous oxide emissions in a corn cropping system. J. Environ. Qual. 40, 1521-1531. Verhoeven, E. C., 2012. Nitrous oxide emissions in response to physical and chemical properties of biochar amended soils. M.Sc. Thesis, Soils and Biogeochemistry graduate group, University of California, Davis, CA. Verhoeven, E., Six, J., 2014. Biochar does not mitigate field-scale N2O emissions in a Northern California vineyard: An assessment across two years. Agric. Ecosyst. Environ. http://dx.doi.org/10.1016/j.agee.2014.03.008. Wang, F., Kang, Y., Liu, S., 2006. Effects of drip irrigation frequency on soil wetting pattern and potato growth in North China Plain. Agr. Water Manage. 79, 248-264. Wei, X.R., Hao, M.D., Xue, X.H., Shi, P., Horton, R.,Wang, A. Zang, Y.F., 2010. Nitrous oxide emission from highland winter wheat field after long-term fertilization. Biogeoscience, 7, 3301-3310.  Wittneben, U. 1986. Soils of the Okanagan and Similkameen Valleys. Ministry of Environment Technical Report 18. British Columbia Soil Survey, Report 52, Victoria, BC. http://sis.agr.gc.ca/cansis/publications/surveys/bc/bc52/bc52_report.pdf (accessed 16.05 2015).   120  Wrage, N., Velthof, G. L., van Beusichem, M. L., Oenema, O., 2001. Role of nitrifier denitrification in the production of nitrous oxide, Soil Biol. Biochem. 33, 1723-1732. Xie, B., Yu, J., Zheng, X., Qu, F., Xu, Y., Lin, H., 2014. N2O emissions from an apple orchard in the coastal area of Bohai Bay, China. Hindawi Publishing Inc. Vol. 2014, Article ID 64732, 8 p.  Zebarth, B.J., Rochette, P., Burton, D.L., 2008. N2O emissions from spring barley production as influenced by fertilizer nitrogen rate. Can. J. Soil Sci. 88, 197-205.  121  APPENDIX A: SUPPLEMENTAL INFORMATION ON THE GRAPE EXPERIMENT   Figure A.1: Grape experiment plot layout. Capital letters inside each plot represent soil amendment: C = compost, U = urea, BM = bark mulch, and PKB = phosphorus, potassium and boron. Note that PKB plots were not used in this study. There were four chambers in each row from row #5 to #10. One chamber was deployed at a random location in each alleyway between rows #5 to #11.    122   Figure A.2: Frequency of measured values of WFPS in the vineyard experiment during the growing season of 2013 for (A) Drip irrigation and (B) Micro-sprinkler irrigation. Shaded areas on the left of the graphs indicate the times spent at and above the 60% WFPS threshold for enhanced denitrification.    123   Figure A.3: Frequency of measured values of WFPS in the vineyard experiment during the growing season of 2014 for (A) Drip irrigation and (B) Micro-sprinkler irrigation. Shaded areas on the left of the graphs indicate the times spent at and above the 60% WFPS threshold for enhanced denitrification.    124  APPENDIX B: SUPPLEMENTAL INFORMATION ON THE APPLE EXPERIMENT   Figure B.1: Apple experiment plot layout.  125   Figure B.2: Frequency of measured values of WFPS in the orchard experiment during the growing season of 2013 and 2014 for plots that were drip-irrigated (A) twice per day every day and (B) twice per day every other day. Shaded areas on the left of the graphs indicate the times spent at and above the 60% WFPS threshold for enhanced denitrification.   126   Figure B.3: Frequency of measured values of WFPS in the orchard experiment during the growing season of 2013 and 2014 for (A) Clean plots and (B) Mulch plots. Shaded areas on the left of the graphs indicate the times spent at and above the 60% WFPS threshold for enhanced denitrification.   127  APPENDIX C: SUPPLEMENTAL INFORMATION ON THE SPATIAL EXPERIMENT IN THE APPLE SITE  Figure C.1: Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux (µg m2 h-1) in Clean plots that were drip-irrigated twice per day every day. Gas samples were taken from 9 mini-chambers at three events in 2013: prior to start of irrigation (May 8 & 9), during fertigation (June 11 & 12), and during irrigation after fertigation (August 14 & 15). Soil samples were taken only on May 8, June 12 and August 15. Each parameter is color plotted at the same scale across events; contour lines are added when values fall outside the color scale. Averages of three measurements (n=3) for each soil parameter and six measurements (n=6) for GHG fluxes were used for plotting.   128      Figure C.2:  Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux (µg m2 h-1) in Clean plots that were drip-irrigated twice per day every other day. Gas samples were taken from 9 mini-chambers at three events in 2013: prior to start of irrigation (May 8 & 9), during fertigation (June 11 & 12), and during irrigation after fertigation (August 14 & 15). Soil samples were taken only on May 8, June 12 and August 15. Each parameter is color plotted at the same scale across events; contour lines are added when values fall outside the color scale. Averages of three measurements (n=3) for each soil parameter and six measurements (n=6) for GHG fluxes were used for plotting.    129    Figure C.3:  Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux (µg m2 h-1) in Mulch plots that were drip-irrigated twice per day every day. Gas samples were taken from 9 mini-chambers at three events in 2013: prior to start of irrigation (May 8 & 9), during fertigation (June 11 & 12), and during irrigation after fertigation (August 14 & 15). Soil samples were taken only on May 8, June 12 and August 15. Each parameter is color plotted at the same scale across events; contour lines are added when values fall outside the color scale. Averages of three measurements (n=3) for each soil parameter and six measurements (n=6) for GHG fluxes were used for plotting.    130    Figure C.4:  Spatial representation of WFPS (%), NO3--N (mg kg-1), salt-extractable organic carbon (SEOC, mg kg-1), CO2 (mg m2 h-1) flux and N2O flux (µg m2 h-1) in Mulch plots that were drip-irrigated twice per day every other day. Gas samples were taken from 9 mini-chambers at three events in 2013: prior to start of irrigation (May 8 & 9), during fertigation (June 11 & 12), and during irrigation after fertigation (August 14 & 15). Soil samples were taken only on May 8, June 12 and August 15. Each parameter is color plotted at the same scale across events. Averages of three measurements (n=3) for each soil parameter and six measurements (n=6) for GHG fluxes were used for plotting.     131   Figure C.5: Results of linear regression used to identify relationships between distance from tree and (A) CO2 flux and (B) N2O flux across three events (before irrigation, during and after fertigation). Averages of 6 measurements of flux were used in the regression. Linear fitted lines applied to correlations when p< 0.05.    132   Figure C.6: Results of linear regression used to identify relationships between distance from dripper and (A) CO2 flux and (B) N2O flux during fertigation, (C) CO2 flux and (D) N2O flux after fertigation. Averages of 6 measurements of flux were used in the regression. Linear fitted lines applied to correlations when p< 0.05.  133  Table C.1: Effects of irrigation frequency, sampling event, chamber location on N2O flux (µg m2 h-1) and CO2 (mg m2 h-1) flux in the apple orchard. The sampling event before the start of irrigation was excluded from the analysis because the effect of irrigation frequency cannot be assessed without the occurrence of irrigation. Gas sampling during fertigation occurred June 11 & 12. Gas sampling after fertigation (during irrigation) occurred August 14& 15.    z Means N2O and CO2 flux followed by different lowercase letters within columns indicate differences of least squares means between pairs of treatments using the factors Tukey-Kramer adjustment, at p<0.05. y *,, **, ***, **** and ns indicate a significant treatment effect at p ≤ 0.05, 0.01, 0.001, 0.0001 or no significant effect, respectively. Effect N2O-Nz CO2 DT DD(µg m-2h-1) (mg m-2h-1) (cm) (cm)Irrigation Low Frequency (LF) 13.9 247.7High Frequency (HF) 26.1 285.1Event During Fertigation 29.2a 323.9aAfter Fertigation 17.7b 357.2bLocationmini-chamber #1 34.5 459.5a 21.0 9.0mini-chamber #2 29.7 371.0a 44.0 14.0mini-chamber #3 15.7 331.2b 67.0 37.0mini-chamber #4 27.0 425.0c 19.0 35.5mini-chamber #5 24.4 301.5bc 30.6 19.9mini-chamber #6 15.1 272.1b 50.7 25.5mini-chamber #7 31.9 310.0b 43.4 39.1mini-chamber #8 18.1 281.1b 58.1 40.5mini-chamber #9 15.0 312.7bd 77.0 53.0Pr > F yIrrigation (I) ns nsEvent (E) **** ****Location (L) ns ****I x E ns nsI x L ns nsE x L ** nsI x E x L ns ns  134  APPENDIX D: CONTRIBUTIONS OF THIS RESEARCH TO OTHER STUDIES ON THE SAME SITE  The foundation experiments to study the effect of management practices on fruit quantity and quality, water usage, and nitrogen usage were designed by Dr. Denise Nielsen, Dr. Gerry Nielsen, Dr. Kirsten Hannam and Dr. Tom Forge of AAFC. Three AGGP projects were added to those pre-existing experiments by UBC researchers to: (1) quantify fluxes CO2, N2O and CH4 (undertaken by Dr. Craig Nichol’s lab ) (2) partition CO2 flux into autotrophic respiration and heterotrophic respiration (undertaken by Dr. Melanie Jones’ lab) (PI). (3) determine abundance and activities of denitryfing and nitrifiying microbial populations      (undertaken by Dr. Louise Nelson’s lab). This thesis summarizes the findings from Dr. Nichol’s lab. Dr. Nelson’s lab and Dr. Jones’ used data from this thesis to support their studies listed below. Geetkamal K. Hans, Tanja M. Voegel, Mesfin M. Fentabil, Craig F. Nichol, Louise N. Nelson. Abundance of ammonia-oxidizing Archaea and Bacteria and their relation with nitrous oxide fluxes in woody perennial cropping systems. The joint scientific conference of the Canadian Society of Soil Science (CSSS) and the Pacific Regional Society of Soil Science (PRSSS), May 14-19, 2016 in Kamloops, BC. Hannam, K.D., M.M. Fentabil, T.A. Forge, D. Neilsen, G.H. Neilsen, L. Nelson, C.F. Nichol, T. Vogel, M.D. Jones. 2016. Influence of compost and wood chip mulch on greenhouse gas emissions, soil carbon and nutrient dynamics. Compost Matters in British Columbia. Compost Council of Canada meeting. March 1, 2016, Summerland, BC.  Hannam, K.D., M.M. Fentabil, T.A. Forge, D. Neilsen, G.H. Neilsen, C.A.S. Smith. 2015. Building resilient horticultural ecosystems using integrated management practices. Friends of the Summerland Gardens, March 16, 2015, Summerland, BC.  Hannam, K.D., M.M. Fentabil. 2013. Greenhouse gas emissions in perennial horticultural systems. BC Institute of Agrology Professional Development Day. October 18, 2013, Summerland, BC.   135  Kelcey Reed and Louise Nelson. 2014. A Spatial Study of the Abundance of Nitrifying and Denitrifying Bacteria in Grape Plots under Different Management Practices. Directed study thesis.  Tanja M. Voegel, Mesfin M. Fentabil, Kirsten D. Hannam, Melanie D. Jones, Craig F. Nichol, Denise Neilsen, and Louise M. Nelson. 2016. The effects of mulch, compost and micro irrigation type on the abundances of nitrifiers and denitrifiers in a Merlot vineyard in British Columbia. In preparation. Tanja M. Voegel, Mesfin M. Fentabil, Kirsten D. Hannam, Melanie D. Jones, Craig F. Nichol, Denise Neilsen, and Louise M. Nelson. 2016. The effects of mulch, nitrogen fertilizer input and irrigation frequency on the abundances of nitrifiers and denitrifiers in an apple orchard in British Columbia. In preparation. Tanja Voegel, Kirsten Hannam, Craig Nichol, Tom Forge, Mesfin M. Fentabil, Louise Nelson, Denise Neilsen, Gerry Neilsen and Melanie Jones 2015. Agricultural management practices reduce greenhouse gas emissions and improve vineyard soil. Ontario Wine Grape Growers Newsletter, December edition. Tanja Voegel, Kirsten Hannam, Craig Nichol, Tom Forge, Mesfin M. Fentabil, Louise Nelson, Denise Neilsen, Gerry Neilsen and Melanie Jones 2015. Agricultural management practices reduce greenhouse gas emissions and improve vineyard soil. BC Wine Grape Growers Newsletter, September edition. Tanja Voegel, Mesfin M. Fentabil, Craig Nichol and Louise Nelson. 2015. Nitrifier and denitrifier abundances in vineyard soil in response to agricultural management practices. Oral presentation at Soil Interfaces for Sustainable Development, July 2015, McGill University. Montreal, Canada. Tanja Voegel, Mesfin M. Fentabil, Kirsten Hannam, Craig Nichol, Melanie Jones, Denise Neilsen, Gerry Neilsen, Tom Forge and Louise Nelson. 2015. The effects of compost and mulch amendments on nitrous oxide emitting bacteria in an Okanagan vineyard. Poster presentation at 16th annual Enology and Viticulture Conference, July 2015, Penticton, Canada. Tanja Voegel, Mesfin M. Fentabil, Kirsten Hannam, Craig Nichol, Melanie Jones, Denise Neilsen, Gerry Neilsen, Tom Forge and Louise Nelson. 2015. The effects of compost and mulch amendments on nitrous oxide emitting bacteria in an Okanagan vineyard. Poster presentation at 1st annual Graduate Student Research Symposium, September 2015, Kelowna, Canada. Tanja Voegel, Mesfin M. Fentabil, Kirsten Hannam, Melanie Jones, Craig Nichol, Denise Neilsen and Louise Nelson. Nitrifier and denitrifier abundances in apple orchard soil in response to agricultural management practices. The joint scientific conference of the Canadian Society of Soil Science (CSSS) and the Pacific Regional Society of Soil Science (PRSSS), May 14-19, 2016 in Kamloops, BC.  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0228793/manifest

Comment

Related Items