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The effects of nitrogen fertilizer rates and planting date on greenhouse gas emissions and potato production… Chizen, Chantel 2020

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THE EFFECTS OF NITROGEN FERTILIZER RATES AND PLANTING DATE ON GREENHOUSE GAS EMISSIONS AND POTATO PRODUCTION IN  DELTA, BRITISH COLUMBIA  by  Chantel Chizen  B.Sc., The University of British Columbia, 2018  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Soil Science)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  July 2020  © Chantel Chizen, 2020 ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled:  The Effects of Nitrogen Fertilizer Rates and Planting Date on Greenhouse Gas Emissions and Potato Production in Delta, British Columbia  submitted by Chantel Chizen in partial fulfillment of the requirements for the degree of Master of Science in Soil Science  Examining Committee: Dr. Maja Krzic, Associate Professor, Faculty of Land and Food Systems & Faculty of Forestry Supervisor  Dr. Sean Smukler, Associate Professor, Faculty of Land and Food Systems Supervisory Committee Member  Dr. T Andrew Black, Professor, Faculty of Land and Food Systems Supervisory Committee Member Dr. Cindy Prescott, Professor, Faculty of Forestry Additional Examiner   iii  Abstract Crop production is a known source of CO2, N2O, and CH4 emissions, yet greenhouse gas (GHG) emissions data from crops in the Lower Fraser River Valley (LFRV) are limited. Potatoes are the most prominent cash crop in the LFRV and are often associated with excess nitrogen (N) fertilizer use, a primary source of N2O emissions. Soils in this region have poor drainage, which is exacerbated by precipitation in the spring and fall. Consequentially, the variable soil workability impacts the timing of potato planting and harvest. The objective of this study was to assess how N fertilizer rates (0, 90, and 120 kg N ha-1) and planting date (typical and late) influence GHG emissions, potato production, and soil properties. The field experiment was established in 2018 at two fields in Delta, British Columbia (BC); one was classified as productive and the other as unproductive. Yield increased with N fertilizer rate at the productive field, but a cost-benefit analysis showed that the increase in yield between the 90 and 120 kg N ha-1 treatments did not significantly outweigh the additional fertilizer costs. The yield at the unproductive field did not respond to N fertilizer, likely due to pre-existing salinity and drainage issues. Planting date did not affect yield at either of the fields. Over the growing season (May to October), there were no differences in total GHG emissions with either N fertilizer rate or planting date. November sampling at the productive field, following a precipitation event, showed N2O emissions increase with N fertilizer treatments. To supplement the field experiment, a soil incubation experiment was conducted to better understand the interactive effects of temperature (4ºC and 20ºC) and soil water content (20% and 40% volumetric water content) on GHG emissions and soil N dynamics in soils fertilized with inorganic N. Nitrous oxide emissions were near zero when temperature was 4ºC or volumetric water content was 20%. This study iv  emphasizes the importance of reviewing N fertilizer management at fields with signs of soil degradation that will, most likely, respond differently to N fertilizers than productive fields. v  Lay Summary In the Lower Fraser River Valley (LFRV), potatoes are an economically important crop, but they often require intensive management. The objective of my study was to assess how greenhouse gas (GHG) emissions, potato production, and soil properties are influenced by nitrogen (N) fertilizer rate and planting date at a productive and an unproductive field. Through the combination of field and incubation experiments, I found that the effect of N fertilizer on GHG emissions occurs outside of the growing season, when the majority of precipitation occurs in the LFRV. Temperature and soil water content influence the production of N2O when soil N is available. Delayed planting did not affect GHG emissions or crop yields. The findings from this study will support the development of management practices in the LFRV to reduce agricultural GHG emissions and improve N fertilizer use efficiency. vi  Preface This thesis represents unpublished work which I conducted with assistance from undergraduate students and advisors. Initial field preparation and soil parameter analysis was completed in May 2017 by Lewis Fausak and Dr. Sean Kearney. Assistance with field and lab work was provided by Skylar Kylstra, Isabelle Philpott, Alex Kramer, Hannah Friesen, and Lewis Fausak. Several other undergraduate students also assisted with processing samples in the laboratory. The UBC Sustainable Agricultural Landscapes (SAL) laboratory coordinators Katie Neufeld, Paula Porto, and Carson Li provided assistance and support with field and laboratory protocols included in this study. Members of the UBC Biometeorology and Soil Physics Group providing support and guidance on measurement and processing protocols for the GHG emissions data in Chapters 2 and 4.  Dr. Maja Krzic was the supervisory author on this project and was closely involved in all aspects of the studies included in this thesis. The project was completed in collaboration with Dr. Sean Smukler and Dr. T. Andrew Black, who helped with experimental design, project development, and interpretation of results. Dr. Rachhpal Jassal also assisted with data analysis and presentation.   vii  Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................. xiii List of Abbreviations ................................................................................................................ xvii Acknowledgements .................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Agricultural Crop Production and Greenhouse Gas Emissions ...................................... 1 1.1.1 Soil Processes that Influence Greenhouse Gas Emissions ...................................... 3 1.1.2 Strategies for Mitigating Greenhouse Gas Emissions from Croplands .................. 7 1.1.3 Greenhouse Gas Emissions from Crop Production in Canada ............................... 9 1.2 Potato Production in the Lower Fraser River Valley .................................................... 10 1.2.1 Nitrogen Fertilizer Practices in Potato Production ............................................... 11 1.2.2 Drainage and Irrigation in Potato Production ....................................................... 12 1.3 Summary ....................................................................................................................... 13 1.4 Research Objectives and Hypotheses ........................................................................... 14 Chapter 2: Greenhouse Gas Emissions and Potato Yield under Different N Fertilizer Application Rates and Crop Management ................................................................................18 2.1 Introduction ................................................................................................................... 18 2.2 Materials and Methods .................................................................................................. 21 viii  2.2.1 Site Description and Experimental Design ........................................................... 21 2.2.2 Sampling and Analysis ......................................................................................... 22 2.2.3 Statistical Analysis ................................................................................................ 26 2.3 Results ........................................................................................................................... 27 2.4 Discussion ..................................................................................................................... 29 2.5 Conclusions ................................................................................................................... 34 Chapter 3: Effects of N Fertilizer Rate and Planting Date on Soil Nitrogen Dynamics and Potato Yield ..................................................................................................................................43 3.1 Introduction ................................................................................................................... 43 3.2 Materials and Methods .................................................................................................. 45 3.2.1 Site Description and Experimental Design ........................................................... 45 3.2.2 Sampling and Analysis ......................................................................................... 46 3.2.3 Statistical Analysis ................................................................................................ 49 3.3 Results ........................................................................................................................... 50 3.4 Discussion ..................................................................................................................... 54 3.5 Conclusions ................................................................................................................... 57 Chapter 4: Combined Effect of Temperature and Soil Water Content on GHG Emissions and Nitrogen Transformations in the Short-Term Following Urea Application ...................66 4.1 Introduction ................................................................................................................... 66 4.2 Materials and Methods .................................................................................................. 68 4.2.1 Experimental Design ............................................................................................. 68 4.2.2 Sampling and Analysis ......................................................................................... 69 4.2.3 Statistical Analysis ................................................................................................ 71 ix  4.3 Results ........................................................................................................................... 72 4.4 Discussion ..................................................................................................................... 75 4.5 Conclusions ................................................................................................................... 78 Chapter 5: Conclusions, Management Implications, and Future Research Recommendations ........................................................................................................................85 5.1 General Conclusions ..................................................................................................... 85 5.2 Study Limitations .......................................................................................................... 87 5.3 Management Implications and Recommendations for Further Research ..................... 87 Bibliography .................................................................................................................................91 Appendices ..................................................................................................................................101 Appendix A Locations of the Productive and Unproductive Fields in Delta, BC .................. 101 Appendix B The 2018 Monthly Averaged Maximum, Mean, and Minimum Temperatures (ºC) as well as Mean Precipitation (mm) that were Measured at the Vancouver International Airport ..................................................................................................................................... 102 Appendix C Experimental Field Maps with the Nitrogen Fertilizer Rate and Planting Date Treatments for the Productive and Unproductive Fields ........................................................ 103 Appendix D Permanganate Oxidizable Carbon Analysis Procedure ...................................... 104 Appendix E Plant Available Nitrogen ANOVA Output for the 2018 Field Experiment ....... 105 Appendix F Properties of the Soil Used in the Incubation Experiment .................................. 106 Appendix G ANOVA Output for Total GHG Emissions in the Incubation Experiment ....... 107  x  List of Tables Table 2.1 Baseline soil properties of the productive and unproductive fields, measured in May 2018. The standard error of the mean is shown in brackets (n = 18 for productive field; n = 9 for unproductive field). ....................................................................................................................... 36 Table 2.2 The N2O emission factor (EF) and the emission intensity (EI) of the total GHG emissions in CO2 equivalents for the productive and unproductive fields for GHG measurements taken from May to October 2018. The standard error of the mean is shown in brackets (n = 3). At both fields, there was no interaction between the N fertilizer and planting date treatments for either of the measures. There were no significant treatment differences in N2O EF or EI between the treatments (α = 0.05)............................................................................................................... 41 Table 2.3 The agronomic efficiency (AEN) and cost efficiency (CE) of different treatments in the productive and unproductive fields. The standard error of the mean is shown in brackets. At both fields, there was no interaction between the N fertilizer and planting date treatments and no significant treatment differences for either AEN or CE (α = 0.05). Comparisons cannot be made between the productive and unproductive fields. ......................................................................... 42 Table 3.1 Soil properties of the productive and unproductive fields, measured prior to planting in May 2018. The standard error of the mean is shown in brackets (n = 18 for productive field; n = 9 for unproductive field). .............................................................................................................. 60 Table 3.2 Total soil C and N concentrations (%), as well as permanganate oxidizable carbon (POXC), measured at harvest at the productive and unproductive field. Error bars represent the standard error of the mean (n = 3). For total C at the 15-30 cm depth of the productive field, different letters indicate statistically significant differences with planting treatment (α = 0.05). xi  There were no other significant differences with the N fertilizer or planting treatments for total soil C or N at either of the other depths or fields (α = 0.05). For POXC, at the unproductive field, different letters indicate significant differences between planting treatments while there were no significant differences with N fertilizer rate (α = 0.05). Letters cannot be compared between depths or fields. Significant treatment effect is indicated by an asterisk (α = 0.05). ................... 62 Table 3.3 Total C and N concentrations (%) in the above-ground biomass at mid-season and at vine-killing, and potato tubers at harvest from the productive and unproductive fields. The standard error of the mean is shown in brackets (n = 3). Different lowercase letters indicate a significant difference between N fertilizer × planting date treatments (α = 0.05). Different capital letters indicate a significant difference when the interaction between N fertilizer × planting date was not significant. Total C at tuber harvest differed with planting date while total N at vine-killing differed with N fertilizer treatments (α = 0.05). Letters cannot be compared between the productive and unproductive field. Significant treatment effects are indicated in bold (α = 0.05)........................................................................................................................................................ 64 Table 3.4 Total C and N content (kg ha-1) of above-ground biomass at mid-season and vine-killing, and potato tubers at harvest from the productive and unproductive fields. The standard error of the mean is in brackets (n = 3). Different lowercase letters indicate a significant difference between N fertilizer rate × planting date treatments (α = 0.05). Different capital letters indicate a significant difference between N fertilizer rate or planting date treatments when the N fertilizer rate × planting date interaction was not significant. Letters cannot be compared between the productive and unproductive fields. Significant treatment effects are indicated in bold (α = 0.05). ............................................................................................................................................. 65 xii  Table 4.1 Total soil C and N concentrations measured at the end of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). Different letters indicate a significant difference between treatments (α = 0.05). ............................................................... 84  xiii  List of Figures Figure 2.1 Total CO2 emissions (kg C ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error bars represent the standard error of the mean (n = 3). There was a significant interaction between the fertilizer and planting treatments in the productive field, but not in the unproductive field. There were no significant differences between treatments (α = 0.05). .................................................................................. 37 Figure 2.2 Total N2O emissions (kg N ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error bars represent the standard error of the mean (n = 3). For both fields, the interaction between fertilizer and planting date was not significant and there were no significant differences between treatments (α = 0.05). ........... 37 Figure 2.3 Total CH4 emissions (kg C ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error bars represent the standard error of the mean (n = 3). There was a significant interaction between the fertilizer and planting treatments in the productive field, but not in the unproductive field. For the productive field, different letters indicate a statistically significant difference between treatments (α = 0.05). In the unproductive field there were no significant differences between treatments (α = 0.05). ........... 38 Figure 2.4 Total N2O emissions (kg N ha-1) from the treatments in the productive field using measurements in 2018 from May to October (October end date) (shown in Figure 2.2) and May to November (November end date). Error bars represent the standard error of the mean (n = 3). There were no significant differences between treatments (α = 0.05). ........................................ 38 Figure 2.5 Total GHG emissions in CO2 equivalents (CO2e) (Mg CO2 ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error xiv  bars represent the standard error of the mean (n = 3). There was a significant interaction between the fertilizer and planting treatments in the productive field, but not the unproductive field. In both fields, there were no significant differences between treatments (α = 0.05). ....................... 39 Figure 2.6 Total GHG emissions in CO2 equivalents (CO2e) (Mg CO2 ha-1) from the productive field calculated using the measurements from late May to November 2018. Error bars represent the standard error of the mean (n = 3). There were no statistically significant differences between the treatments (α = 0.05)............................................................................................................... 39 Figure 2.7 Potato yield (Mg ha-1) at the productive and unproductive fields. Error bars represent the standard error of the mean (n = 3). There was no significant interaction between fertilizer and planting treatments. In the productive field, there was a significant difference between fertilizer treatments as indicated by different letters (α = 0.05). In the unproductive field, planting date had a significant effect on yield but there were no significant differences between treatments (α = 0.05). ............................................................................................................................................. 40 Figure 3.1 Soil NH4+-N (mg N kg-1) at 0-15 cm and 15-30 cm depths for the treatments at the productive and unproductive fields, from late May to October 2018. The vertical lines indicate the time of planting (day 0) and harvest (day 109). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05). ........................................................................... 61 Figure 3.2 Soil NO3--N (mg N kg-1) at depths of 0-15 cm and 15-30 cm for the treatments at both the productive and unproductive fields, from late May to October 2018. The vertical lines indicate the time of planting (day 0) and harvest (day 109). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05). ............................................................. 61 xv  Figure 3.3 Dry weight of above-ground plant biomass at mid-season and vine-killing, and tuber biomass at harvest from the productive and unproductive fields. Error bars represent the standard error of the mean (n = 3). Different lowercase letters indicate a significant difference between N fertilizer × planting date (α = 0.05). Different capital letters indicate a significant difference between N fertilizer treatments when planting date did not have a significant effect (α = 0.05). There were no instances were planting date solely resulted in a significant difference between treatments. Letters cannot be compared between the productive and unproductive field. ........... 63 Figure 4.1 Daily CO2 flux measured over the duration of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, no significant differences between treatments were observed (α = 0.05). ............................................................................. 79 Figure 4.2 Total CO2 emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). There were no significant differences across treatments (α = 0.05). ............................................................................................................................................. 79 Figure 4.3 Daily N2O flux measured over the duration of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, an asterisk denotes that there is a significant difference between N fertilizer rate × soil water content (SWC) × temperature treatments (α = 0.05). ................................................................................................ 80 Figure 4.4 Total N2O emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). Different letters indicate significant differences between treatments (α = 0.05). .................................................................................................................... 80 Figure 4.5 Daily CH4 flux measured over the duration of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, no significant differences between treatments were observed (α = 0.05). ............................................................................. 81 xvi  Figure 4.6 Total CH4 emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). Different letters indicate significant differences between treatments (α = 0.05). .................................................................................................................... 81 Figure 4.7 Total CO2 equivalent emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). There were no significant differences across treatments (α = 0.05). .................................................................................................................... 82 Figure 4.8 Measurements of NH4+-N over the duration of the incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05)............................................................................. 82 Figure 4.9 Measurements of NO3- - N over the duration of the incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05)............................................................................. 83   xvii  List of Abbreviations AEN Nitrogen agronomic efficiency AGGP Agricultural Greenhouse Gas Program BC British Columbia C Carbon CE Cost efficiency CH4 Methane CO2 Carbon dioxide DMW Dry matter weight EF Emission factor EI Emission intensity GHG Greenhouse gas GWP Global warming potential LFRV Lower Fraser River Valley N Nitrogen N2O Nitrous oxide NH4+ Ammonium NO3- Nitrate PAN Plant available nitrogen PD Planting date POXC Permanganate oxidizable carbon SWC Soil water content VWC Volumetric water content   xviii  Acknowledgements I would like to sincerely thank my supervisor, Dr. Maja Krzic, for her genuine support and guidance during my MSc degree. Her enthusiasm and commitment to soil science has been an inspiration. I would also like to thank my advisory committee, Dr. Sean Smukler and Dr. Andrew Black for their thoughtful feedback on this project.  I am immensely grateful for the research assistants and volunteers who helped conduct this research including Skylar Kylstra, Alex Kramer, Hannah Friesen, Isabelle Philpott, and Kelly Wang. Their dedication in the field and laboratory was essential to the success of this research and it was a pleasure to work with them. Thank you to the members of the AGGP research group at the Faculty of Land and Foods Systems as well as Lewis Fausak for their insight and technical support. This research was made possible by funding from Agriculture and Agri-Food Canada (The Agricultural Greenhouse Gas Program) and support from The Delta Farmland and Wildlife Trust. Lastly, I would like to thank my friends and family for their support. I am grateful for the friendship from my fellow soil science students who made the past two years truly enjoyable. Thank you to my parents and brother for empowering me to pursue my passions and always being there through difficult times or to celebrate successes whether big or small. Finally, thank you to my partner, Keavan Andreassen, for his encouragement, patience, and constant support.1  Chapter 1: Introduction 1.1 Agricultural Crop Production and Greenhouse Gas Emissions  Agriculture is considered to be one of the largest anthropogenic contributors to greenhouse gas (GHG) emissions as a result of land-use change and soil management practices (Paustian et al. 2016). Globally, agricultural practices contribute 9-14% of total GHG emissions (not including land use and land cover change) as estimated using measurements from 2007 to 2016 (IPCC 2019a). Approximately, one-third of these agricultural emissions are derived from crop production (IPCC 2019a). Crop production specifically results in emissions of the following GHGs: carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). The management practices associated with crop production contribute to net GHG emissions by influencing the amount of GHGs being exchanged between the atmosphere, plants, and soil.  Depending on the balance of GHG production and storage in the soil, soils are capable of acting as a GHG source or sink. Soils are the largest terrestrial sink of carbon (C) and are capable of storing CO2 from the atmosphere through C sequestration. Approximately, 170 petagrams (Pg) of C is stored in croplands soils, globally (Paustian et al. 2000). Most agricultural CO2 emissions are caused by the conversion of native ecosystems to agricultural production followed by cultivation (IPCC 2019a). The disturbance to the soil that occurs from this land-use change, together with the accompanying cultivation practices, stimulate the mineralization of soil C which results in the release of CO2 (Paustian et al. 2000). It is estimated that 50 Pg of soil C has been lost due to agricultural cultivation over time (Paustian et al. 2000). Crop management practices that continue to contribute CO2 emissions include the removal or incorporation of plant residues, liming, inorganic fertilizers, and tillage (Paustian et al. 2000). These practices influence 2  the availability of soil organic matter (SOM) for decomposition as well as soil microbial and root respiration rates. The primary GHG emitted by crop production is N2O (IPCC 2019a). Arable land accounts for approximately 50% of global N2O emissions or 4.2 teragrams (Tg) of N2O-N per year. In addition to being a potent GHG, N2O contributes to stratospheric ozone depletion, which enables greater amounts of UV radiation to reach the Earth’s surface and lowers air temperature stability (Butterbach-Bahl et al. 2013). Inorganic nitrogen (N) fertilizers are the primary source of agricultural N2O emissions (IPCC 2019a). From 1961 to 2017, there has been an increase by nearly 800% in inorganic N fertilizer use for agricultural production (IPCC 2019a). There is an exponential relationship between the amount of N fertilizer that is applied to soil and the consequential N2O emissions (Shcherbak et al. 2014). It is estimated that 10 - 50 kg of N are lost as N2O with every 1000 kg of N fertilizer application (Shcherbak et al. 2014). Other crop management practices that contribute to N2O emissions include manure applications, cultivation practices on organic soils, and decomposition of crop residues (IPCC 2019a). The main opportunities for regulating soil N2O emissions are through lowering excess N inputs to the soil and inhibiting nitrification (Paustian et al. 2016).  The third most common GHG emission from agriculture is CH4. Agricultural CH4 emissions are mainly derived from rice production, enteric fermentation in ruminant livestock, and anaerobic decomposition of livestock manure (IPCC 2019a). Aside from rice fields, which are often flooded, crop production contributes little to CH4 emissions. Soils that are well-drained and aerated will take up CH4 from the atmosphere and act as a CH4 sink (Paustian et al. 2016). More often, soils under crop production have variable or suppressed CH4 sink capabilities due to 3  management practices such as tillage and N fertilizer application that can limit CH4 oxidization (Paustian et al. 2016).  The extent to which these GHGs contribute to the greenhouse effect by warming the Earth’s surface is determined using the global warming potential (GWP). Global warming potential is calculated for a particular GHG relative to CO2 over a certain period of time (Myhre et al. 2013). The GWP of CO2 is 1, whereas N2O has a GWP of 298, and the GWP of CH4 is 34 (for a 100-year time period; Myhre et al. 2013). It is important to note that these GWP values take into account the climate-carbon feedback of the respective GHGs (Myhre et al. 2013). If the climate-carbon feedback were not included then the GWP for each GHG would be lower: 265 for N2O and 28 for CH4 (Myhre et al. 2013). With GWP we are able to calculate the total emissions across different GHGs as CO2 equivalents. This measure allows us to evaluate the extent that agriculture contributes to climate change relative to other geographical regions or industries. To understand the general production of GHG emissions from arable land it is important to consider the underlying soil processes and their relationships with climate factors.   1.1.1 Soil Processes that Influence Greenhouse Gas Emissions   The movement of GHGs between the atmosphere and the soil is driven by diffusion gradients created by the formation and consumption of these gases both within the soil and the external environment. Soils can act as either a sink or a source of GHGs depending mainly on climate variables, soil aeration, and soil microbial communities. Whether the soil acts as a sink or source depends on the net formation and consumption of a GHG in the soil. Soils under crop management practices experience a variety of disturbances due to cultivation and harvesting as 4  well as additions of various organic amendments and mineral fertilizers, which increase the complexity of the interactions that govern GHG emissions. Carbon Dioxide and Methane Emissions  Decomposition of SOM is the primary process for CO2 and CH4 production. Other sources of CO2 emissions are the applications of lime, urea, and other C-based fertilizers, but the amount of CO2 that they produce is relatively small in comparison to the emissions from SOM decomposition (Environment and Climate Change Canada 2019; IPCC 2019a). Under crop production, SOM inputs include crop residues, livestock manure, organic amendments (e.g., compost or biochar), and exudates from roots and soil micro-organisms (IPCC 2019a). The rate of decomposition is dependent on environmental conditions such as temperature, soil moisture, oxygen availability, and pH (Paustian et al. 2000). Other factors such as the microbial community and type of SOM also influence decomposition rates (Paustian et al. 2000). Through the decomposition process, C in organic molecules undergoes oxidation resulting in intermediary organic molecules, as well as CO2 and energy as products of microbial respiration under aerobic conditions. Under anaerobic conditions, methanogenic bacteria and archaea produce CH4 during the decomposition of SOM (Topp and Pattey 1997; Liu and Greaver 2009).  Carbon dioxide is the most abundant GHG in the atmosphere and can be sequestered in soils, along with CH4, in the form of organic C (Ontl and Schulte 2012). Methane is consumed under aerobic conditions by methanotrophic bacteria as it undergoes oxidation to form CO2 (Liu and Greaver 2009). The sequestration of CO2 is facilitated by plants through photosynthesis (Ontl and Schulte 2012). The amount of C stored in soils is a function of inputs from plant biomass and microbial biomass, and outputs from decomposition processes (Schmidt et al. 2011). A number of soil properties can enhance the stability of SOC over time. SOM 5  stabilization can be facilitated through aggregation, adsorption to mineral particles, and complexation (Schmidt et al. 2011). Through these processes of stabilization, the SOC is protected from further decomposition by microbial enzymes (Schmidt et al. 2011). With consistent management practices and time, a stable SOM content is reached as SOM inputs and losses reach an equilibrium (Janzen et al. 1998).  Nitrous Oxide Emissions  The extent of soil N2O emissions is driven by the formation and consumption of N2O in the soil, which occur simultaneously. Nitrous oxide formation in arable soils primarily comes from the combination of nitrification and denitrification processes, which are facilitated by micro-organisms. Soil moisture, temperature, aeration, and pH control the rates of these processes (Hu et al. 2015). Under aerobic conditions, nitrification proceeds as ammonium (NH4+) is converted to nitrate (NO3-). Typical sources of NH4+ for nitrification in agriculture include the mineralization of SOM, synthetic N fertilizers, and manure. During nitrification, a specific group of soil microbes are capable of converting nitrite (NO2-), which is an intermediary compound in the nitrification pathway, to N2O. This process is known as nitrifer denitrification and a varying amount of N2O is produced from this process, depending on the soil and climatic conditions. The NO3- that is produced during nitrification is susceptible to being lost as N2O through denitrification. Unlike nitrification, denitrification occurs under anaerobic conditions where NO3- is converted to NO, N2O, or N2. The end product of denitrification depends on climate variables and the soil microbial community (International Plant Nutrition Institute n.d.). Research has confirmed a number of other microbial metabolic pathways that also contribute to N2O formation such as nitrate ammonification and ammonium-nitrate decomposition (Butterbach-Bahl et al. 2013; Hu et al. 2015). Nitrification and denitrification still remain the 6  primary N2O formation processes in soils (Butterbach-Bahl et al. 2013). While soils do not act as a sink for N2O, recent evidence supports the microbial consumption of N2O under conditions of low mineral N and oxygen pressure in the soil (Liu and Greaver 2009).  Soil N2O flux measurements are often afflicted with high spatial and temporal variability due to the complex interactions involved in its production. Under crop management, “hot-spots” of N2O are frequently detected in locations of N fertilizer application and are considered as direct emissions from the management practice (Butterbach-Bahl et al. 2013; Environment and Climate Change Canada 2019). Processes of volatilization, soil erosion, and leaching can cause the movement of N substrates and result in N2O emissions at distance from where N fertilizers were applied (Butterbach-Bahl et al. 2013; Berhe et al. 2014). The N2O emissions that occur after the transport and deposition of N from its applied location are defined as indirect (Environment and Climate Change Canada 2019). Additionally, the majority of N2O measurements on arable land focus on the upper 30 cm of soil, there is evidence that N2O formation and consumption processes occur at depth (Shcherbak and Robertson 2019). Complexities Among Soil GHG Emissions  The relationships between a particular substrate and its associated GHG emission, such as NH4+ and N2O, are well supported by research evidence (Liang et al. 2015). However, there are underlying complexities in these relationships as GHG production and consumption are also influenced by both soil C and N availability (Liu and Greaver 2009; Jain et al. 2013). It is generally understood that C and N levels influence CO2, N2O, and CH4 emissions because together they are key for plant growth and decomposition (Jain et al. 2013). For instance, available soil N permits plant growth and development, which consequently increases CO2 sequestration from the atmosphere to plant biomass (Liu and Greaver 2009). Research by Karhu 7  et al. (2014) found that increasing soil C:N ratios enhanced the positive relationship between temperature and CO2 emissions from microbial decomposition. Soil N also influences the production and consumption of CH4. Under moderate reducing conditions, NO3- is oxidized more readily than CO2, causing a decrease in CH4 production (Liu and Greaver 2009). Additionally, NH4+ and CH4 are substrates for the same enzyme, methane monooxygenase, and greater concentrations of soil NH4+ can inhibit CH4 consumption (Liu and Greaver 2009). Ultimately, C and N contribute to the regulation of soil micro-organism metabolic activity rates yet it is still important to consider how other environmental factors also influence these metabolic processes (Liang et al. 2015). Our improved knowledge of the mechanisms that govern GHG emissions can be applied in the development and evaluation of effective agricultural practices that mitigate GHG emissions.   1.1.2 Strategies for Mitigating Greenhouse Gas Emissions from Croplands   The development of GHG mitigation strategies for crop production involves adoption of practices that promote soil C sequestration and reduce the existing emissions. Extensive research has focused on identifying and assessing crop management strategies to reduce GHG emissions. Potential methods involve lowering N2O and CH4 emissions that are induced from N fertilizer and manure applications, sequestering C in cropland soils, and preventing of further losses of soil C (IPCC 2019a).  Intensive management practices such as excessive N fertilizer application are often utilized to maintain crop yields on degraded soils or on sites with marginal production suitability. Even on productive soils, excess N fertilizer is used which leads to the formation of N2O (Huffman et al. 2008). Well-researched solutions to minimizing N2O emissions include proper N 8  fertilizer placement and timing, reducing N fertilizer rates to ones that still provide economically profitable yields, and precision management of N2O “hot-spots” in the field (Hénault et al. 2012; Paustian et al. 2016). In cropping systems where manure is used as a source of N, similar management considerations associated with timing and placement can mitigate CH4 emissions (IPCC 2019a). Nitrous oxide emissions can also be lowered through the use of nitrification or urease inhibitors to slow the formation of N2O following the application of N fertilizers or manure (IPCC 2019a).  The mitigation of CO2 emissions from crop production relies on soil C sequestration and preventing the loss of soil C during decomposition. Soil amendments such as compost and biochar increase the soil C pool because they are more likely to be protected from microbial decomposition (Paustian et al. 2016). Similarly, no-till or reduced tillage practices promote soil aggregate formation which enables the stabilization of SOM and prevents CO2 emissions (Paustian et al. 2000). The C sequestration capacity of soil is determined by its texture and structure but it is also dependent on climate and vegetation type (IPCC 2019a). The IPCC (2019) estimated that mitigation practices to sequester C will offset soil CO2 emissions for 20 to 30 years, before reaching the saturation point.  In order to achieve effective and long-term agricultural GHG mitigation, crop management practices will need to simultaneously address CO2, N2O, and CH4 emissions. Additionally, the implementation of crop production practices that aim to sequester C will need to be used continuously over time to preserve the C that is sequestered (IPCC 2019a).  9  1.1.3 Greenhouse Gas Emissions from Crop Production in Canada  As of 2017, agriculture contributed 10% of the total gas emissions in Canada (Environment and Climate Change Canada 2019). The major contributors to these agricultural GHG emissions are livestock and N fertilizer application (Environment and Climate Change Canada 2019). Focusing on crop production, the use of inorganic N fertilizers resulted in 23% of the total agricultural GHG emissions primarily in the form of N2O (Environment and Climate Change Canada 2019). Agriculture and Agri-Food Canada (AAFC) has prioritized the development of fertilizer-use efficiency practices, which can contribute to reducing water pollution and the mitigation of GHG emissions (Sarkar et al. 2018). The implementation of other conservation practices such as the growth of perennial crops and grassland set-asides, can offset their own GHG emissions and also potentially the emissions from other economic sectors.   Historical studies estimate that Canadian croplands were sources of GHG emissions until 1997. Since then, croplands have sequestered up to 12 Tg of CO2 equivalents per year but the ability for croplands to act as a sink has consistently decreased since 2011 (Environment and Climate Change Canada 2019). It is estimated that cropland across Canada sequestered 6.8 Tg of CO2 equivalents in 2017. This data shows that there is opportunity to further reducing soil GHG emissions from crop production practices and improving the sink strength of croplands.  In British Columbia, crop production contributed 0.4 Tg of CO2 equivalents during 2017 (Environment and Climate Change Canada 2019). These emissions constitute 3% of the province’s agricultural emissions and 0.6% of the province’s total emissions (Environment and Climate Change Canada 2019). The majority of the 2017 GHG emissions in BC by crop production were from direct and indirect N2O emissions from the soil (Environment and Climate 10  Change Canada 2019). The remaining emissions were CO2 from the application of lime, urea, and organic amendments (Environment and Climate Change Canada 2019).   1.2 Potato Production in the Lower Fraser River Valley  The Lower Fraser River Valley (LFRV) is one of the most productive agricultural regions of BC, because of its climate and fertile soils, characterized by medium to fine texture (Luttmerding 1981). Some soils in the region also have inherently poor drainage (Luttmerding 1981). The modified Mediterranean climate allows for a growing season from early May to late October. The region receives approximately 1,180 mm in annual precipitation, of which 75% occurs from October to April (Environment and Climate Change Canada n.d.).  Potatoes are one of the region’s major crops with approximately 50% of the province’s total acres in potato production found in Delta, which is a municipality located in the LFRV (British Columbia Agriculture & Food Climate Action Initiative 2013a). Potatoes, as a high revenue crop, are one of the major crops grown in the LFRV. They are also well suited for cold storage, which allows producers to delay sales during periods of low market prices. Depending on the variety, they can be sold as seed potatoes, table potatoes, or for processing. The price of fresh potatoes in BC during fall 2018 ranged from $0.62 to $0.66 per kg, which is considerably higher than the national average of approximately $0.37 per kg (Statistics Canada 2020a).   Potato production in BC accounts for 1.9% of Canada’s total harvested potato area (Statistics Canada 2020b). In comparison, the majority of Canada’s potatoes are grown in Prince Edward Island, which accounts for 25% of the country’s total potato production area (Statistics Canada 2020b). Other provinces with high potato production are Alberta, Manitoba, and New Brunswick (Statistics Canada 2020b). Globally, Canada is the 5th largest exporter of seed 11  potatoes but its overall potato production is small relative to China and the United States. The United States has over 2.5 times the amount of harvested area under potato production in comparison to Canada (Statistics Canada 2020b, 2020c). Nonetheless, potatoes generated $1.19 billion in Canadian farm receipts during 2017, making it the country’s main vegetable crop.  1.2.1 Nitrogen Fertilizer Practices in Potato Production  In 1991, 90 kg N ha-1 was the average inorganic N fertilizer rate for potato production in the LFRV (Brisbin and Runka 1995). More recent estimates focused on Humic Gleysols, such as those found in the LFRV, show N fertilizer rates from 40 to 314 kg N ha-1 per year (Huffman et al. 2008). The current recommended rate of N fertilizer for potato production is 70 kg N ha-1 (British Columbia Ministry of Agriculture 2012a). Other major sources of N inputs for crop production in the LFRV are livestock manure and compost.  The BC Ministry of Agriculture has developed a number of resources, including crop production guides and soil nutrient testing guidelines, to promote effective N fertilizer management in the LFV. In 2011, approximately 16% of farms reported the use of nutrient management planning (British Columbia Agriculture & Food Climate Action Initiative 2013b). Factors that limit the adoption of these recommendations are the amount of time required for planning, the associated costs, and the necessary input from local nutrient management experts (British Columbia Agriculture & Food Climate Action Initiative 2013b). Effective research 12  extension and outreach are key to improving the implementation of efficient N fertilizer practices.  1.2.2 Drainage and Irrigation in Potato Production  Due to their poor natural soil drainage, agricultural fields in the LFRV delta are susceptible to ponding and soil erosion. These drainage issues are most prominent in the spring and fall during periods of high precipitation (Thiel et al. 2015). Consequently, the trafficability and workability of the soil is hindered during these periods of time. If heavy machinery is used during poor soil trafficability, permanent damage can be caused to the soil structure. The current solutions to address drainage issues in the region are the installation of drainage systems that vary in their degree of permanency. Less permanent measures used by producers in Delta, BC include mole drains and subsoiling (Thiel et al. 2015). Laser-levelling, drainage tiles, and private open ditches are more permanent systems that are also utilized (Thiel et al. 2015). The Corporation of Delta manages additional drainage infrastructure for the area such as sloughs, dykes, and flood gates to assist with existing on-farm strategies (Thiel et al. 2015).  The majority of vegetable and berry crops in Delta are irrigated (British Columbia Ministry of Agriculture 2012b). In 2010, 81% of potato fields in Delta, BC were irrigated primarily by sprinkler or giant gun systems (British Columbia Ministry of Agriculture 2012b). Additionally, the proximity of some of these agricultural fields to the ocean increases the possibility of ocean water intrusion into irrigation channels and groundwater. This in turn leads 13  to the accumulation of salt in the soil and consequently, crop yield reduction and soil degradation (Thiel et al. 2015).  Rising sea levels, together with more frequent precipitation during wet part of the year, have been predicted as the most likely outcomes of climate change in Canada (Li et al. 2018). The implications of these potential environmental changes for agriculture include shifts in the growing season and changes in the climatic suitability of particular crops for a given region (Li et al. 2018; IPCC 2019a). In coastal regions, higher sea levels increase the risk of soil salinization and can result in the loss of agricultural land (IPCC 2019a). Cumulatively, these climate change impacts will limit crop productivity in the LFRV and contribute to the pressure on agricultural drainage systems (British Columbia Agriculture & Food Climate Action Initiative 2013a).   1.3 Summary   Croplands offer an opportunity to reduce total GHG gas emissions through C sequestration in the soil and adopting management practices that produce fewer emissions. There is a foundational understanding to how crop management influences GHG emissions, yet areas of uncertainty exist in the complex systems that govern soil GHG production and consumption. In Canada, inorganic N fertilizer use is the leading source of N2O emissions and improved N fertilizer use efficiency in crop production is a promising mitigation strategy for this GHG. Potato production is often associated with high N fertilizer rates and intensive tillage, which are known to contribute to GHG emissions. Another factor that enhances GHG emissions is a moderate to high soil water content which stimulates the production of N2O and CH4. In the LFRV potatoes are an economically important crop, but the reportedly substantial use of N 14  fertilizers makes them a major contributor to N2O emissions. Regional potato production can reduce N2O emissions induced from inorganic N fertilizer through lower N fertilizer application rates and improved application timing. The poor soil drainage in the LFRV, combined with intense and unpredictable spring and fall precipitation, not only impacts GHG emissions, but also adds timing pressure on the implementation of crop planting and harvesting. Potato production requires the use of heavy machinery during spring and fall, when trafficability and workability conditions are suboptimal due to the precipitation and poor soil drainage. If the machinery is used under these conditions, there is a substantial risk of soil compaction, causing permanent structural damage to the soil. With the predicted effects of climate change, such as irregular precipitation patterns, more extreme precipitation events, and rising sea levels, the current drainage and production issues experienced in Delta, BC will likely intensify.   An extensive body of research has examined the response of GHG emissions to tillage, N fertilizer rates, and N fertilizer application timing in potato production (Ruser et al. 1998; Venterea and Parkin 2012; Snowdon et al. 2013; Gao et al. 2017). Yet, very few of these investigations have taken place in Western Canada. The findings of this study will contribute to the better understanding of agricultural GHG emissions in the LFRV through evaluation of the effects of delayed planting on GHG emissions and potato production.   1.4 Research Objectives and Hypotheses  The goal of this study is to examine the response of GHG emissions and soil properties to potato crop management practices and drainage issues that are commonly observed in the LFRV. This study is a part of a 5-year project, initiated in 2016 and funded by the Agricultural 15  Greenhouse Gas Program to quantify GHG emissions and develop best management practices for the mitigation of GHG emissions from the three main crops (i.e., potatoes, blueberries, and forage) grown in the LFRV. The following research objectives and associated hypotheses guided my study.  Objective 1: Evaluate how N fertilizer application rates (0, 90, and 120 kg N ha-1) affect GHG emissions (CO2, N2O, and CH4) and potato yield.  Hypothesis 1: The N2O emissions and yield will increase with N fertilizer rate. The CO2 emissions will be unaffected by N fertilizer rate. If the CH4 emissions are negligible in the control, then they will be unaffected by the fertilizer treatments. Potato yield will increase with greater rates of N fertilizer application.   Objective 1 will be addressed in Chapter 2 of this thesis.  Objective 2: Assess the impact of a 3-week delay in planting on GHG emissions (CO2, N2O, and CH4) and potato yield, compared to a typical planting time. Hypothesis 2: A delay in potato planting time will have no effect on GHG emissions, since the typical planting time will be associated with a higher soil water content in the spring while the late planting time will be associated a higher soil water content in the fall, which will stimulate the production of GHGs. Yield will be unaffected by the timing of planting, assuming that soil workability conditions in the fall will not impede the harvest of the late planting treatment.   Objective 2 will be addressed in Chapter 2 of this thesis. 16  Objective 3: Consider the relationships and tradeoffs between N fertilizer application rates (0, 90, and 120 kg N ha-1), planting date, GHG emissions (CO2, N2O, and CH4), and potato yield through a cost-benefit analysis. Hypothesis 3: Parameters used for cost-benefit analysis will show that the difference in revenue from crop yield between the moderate and high N fertilizer rates do not outweigh the cost of the additional fertilizer used. Planting date will not affect the cost-benefit analysis parameters.  Objective 3 will be addressed in Chapter 2 of this thesis.  Objective 4: Evaluate the effects of N fertilizer application rates (0, 90, and 120 kg N ha-1) and the timing of potato planting on plant available nitrogen (PAN), total soil C and N, permanganate oxidizable carbon (POXC), and potato plant biomass and quality. Hypothesis 4: Total soil C, POXC, and plant biomass C will not significantly change between treatments over the course of the growing season. With increasing N fertilizer application rates, PAN, total soil N, plant biomass, and plant biomass N will increase. Potato planting timing will not affect these parameters.  Objective 4 will be addressed in Chapter 3 of this thesis.  Objective 5: Quantify the GHG emissions, PAN, and total soil C and N in response to N fertilizer application rates (0, 90, and 120 kg N ha-1), soil water content (20% and 40% VWC), and temperature (4ºC and 20ºC) through an 18-day soil incubation experiment. Hypothesis 5: GHG emissions will increase with soil saturation due to the displacement of air in the pore-space by water. The N2O emissions will be greatest when the soil is 17  near saturation and at 20ºC as denitrification is expected to be the dominant conversion process under saturated, anaerobic conditions and microbial activity is lower at 4ºC. The high soil water content and temperature will result in increases in PAN and total N over the incubation period, while total C will remain unchanged.  Objective 5 will be addressed in Chapter 4 of this thesis.  18  Chapter 2: Greenhouse Gas Emissions and Potato Yield under Different N Fertilizer Application Rates and Crop Management    2.1 Introduction  Since 1961, the global use of inorganic nitrogen (N) fertilizers for crop production has increased by 800% (IPCC 2019a), substantially increasing yields but also contributing to environmental pollution. Inorganic N fertilizers are a major source of global N2O emissions (IPCC 2019a). For example, in Canada 23% of agricultural greenhouse gas (GHG) emissions, primarily as N2O and to lesser extent as CO2 and CH4, were induced by inorganic fertilizers (Environment and Climate Change Canada 2019). Of the three GHGs, N2O is the most potent, with 298-times the global warming potential (GWP) of CO2 (Myhre et al. 2013). The production of N2O in soils is highly dependent on climatic and environmental conditions including temperature, soil moisture, soil organic carbon (C), and N availability (Hu et al. 2015). Inorganic N fertilizers increase soil N availability, which in turn can affect the production and uptake of CO2 and CH4. The effect of inorganic N fertilizers on CO2 emissions is considered site dependent as there are inconsistent findings on whether they stimulate or hinder soil respiration (Al-Kaisi et al. 2008; Liu and Greaver 2009; Gagnon et al. 2016). In arable soils, N fertilizers can increase CH4 production or decrease CH4 uptake resulting in greater net emissions (Bodelier and Laanbroek 2004; Liu and Greaver 2009; Jassal et al. 2011).  Based on Intergovernmental Panel on Climate Change (IPCC) recommendations, many countries estimate annual crop production N2O emissions from the amount of N fertilizer applied and an emission factor (EF) of 0.01 kg N2O-N kg-1 N (IPCC 2019b). Hutchinson et al. (2007) found that using the default EF to estimate N2O emissions from synthetic N fertilizers in Canada 19  had an associated uncertainty of 28 to 57%, depending on the province, that can be attributed to environmental and crop management factors (Rochette et al. 2018). This uncertainty emphasizes the need for data to better estimate GHG emissions from crop production at a regional scale (Rochette et al. 2018). The GHG emissions data from cropping systems in British Columbia (BC) are limited and existing data focuses on organic amendments (e.g., dairy and poultry manure), rather than inorganic N fertilizers (Bhandral et al. 2008; Maltais-Landry et al. 2018; Hunt et al. 2019).  Among the field vegetable crops grown in BC, potatoes are the most economically important, accounting for nearly a third of the province’s total field vegetable farm gate value in 2013-2017 period (Province of British Columbia 2019). Approximately 50% of the province’s total production area is found within the municipality of Delta, which is located in the Lower Fraser River Valley (LFRV). The recommended annual N fertilizer rate for potato production in the region is 70 kg N ha-1 (British Columbia Ministry of Agriculture 2012a), but 90 kg N ha-1 is the average amount of fertilizer applied (Brisbin and Runka 1995). The application of excess N fertilizer provides a buffer against yield losses and lowers the economic risk of production (Zebarth and Rosen 2007). The balance between N fertilizer rate and yield can be assessed using cost-benefit measures such as cost effectiveness.  A crop production challenge in the LFRV is poor soil drainage, brought by a combination of the medium to fine soil texture and high water table, which is compounded by heavy rainfall in the spring and fall. More frequent and variable precipitation, during the wet seasons is predicted in Canada due to climate change (Li et al. 2018). Poor soil drainage often impedes the timing of crop planting and harvesting as the soil workability is low. This issue raises the trade-off of planting under conditions where the use of heavy machinery leads to the destruction of soil 20  structure and compaction versus a delayed planting date which may result in lower yields due the possibility of a shorter growing season (Kawakami et al. 2005; Haverkort and Verhagen 2008). In addition, there is an added risk of soil salinization due to saltwater intrusion into irrigation channels during the summer. The combined issues of structure destruction, compaction and salinization can lead to serious soil degradation. The poor soil drainage in degraded soils could also increase the production of N2O and CH4 due to anaerobic conditions, while CO2 production would lower due to decreased decomposition rates.  The primary objective of this study was to quantify the effect of N fertilizer rate (0, 90, and 120 kg N ha-1) and potato planting date (typical and late) on total GHG emissions (CO2, N2O, and CH4) and potato tuber yield in Delta, BC. I hypothesized that (i) the highest application rate of N fertilizer will result in the greatest N2O emissions and the highest yield in comparison with the other treatments; (ii) the N fertilizer rates will not have a considerable impacts on CO2 and CH4 emissions; and (iii) the planting date will not influence GHG emissions or potato yields. The secondary objective was to assess the costs and benefits associated with increasing N fertilizer rates and different planting dates in terms of GHG emissions and crop yield. For this objective I hypothesized that (i) the total amount of GHG emissions would increase with higher N fertilizer rates; (ii) the difference in revenue from crop yield between the moderate and high N fertilizer rates do not outweigh the cost of the additional fertilizer used; and (iii) the planting date would not influence the cost-benefit parameters.  21  2.2 Materials and Methods 2.2.1 Site Description and Experimental Design The experiment was conducted at two operational farm fields in the Municipality of Delta, BC from May to November 2018 (Appendix A). A study by (Lussier et al. 2019) categorized one of the field sites (49.065087, -123.137965) as productive, and the other (49.057207, -123.123570) as unproductive, based on soil properties such as electrical conductivity, sodium concentration, and the amount of aboveground biomass.  The LFRV receives approximately 1,180 mm in annual precipitation of which 75% occurs from October to April (Appendix B). The field sites are situated on the Fraser River Delta, and have nearly level slope (< 3%) with average elevation of 2 metres above sea-level (Luttmerding 1981). The soil in this region is formed on fine to medium-textured deltaic deposits from the Fraser River. Both of the field sites have a silt loam soil texture (Table 2.1) (Fausak 2019). The soil at the productive field is classified as either Blundell Rego Gleysol and Crescent Orthic Gleysol, while the soil at the unproductive field is classified as Ladner Humic Luvic Gleysol (Luttmerding 1981; Fausak 2019). All three of the soil series found at the experimental sites feature poor to very poor drainage (Luttmerding 1981). The field experiment was established in May 2018 at both fields as a randomized block design with three N fertilizer application rates and three replicates of each treatment. Each fertilizer treatment plot was divided into typical planting and late planting treatments (Appendix C) The following fertilizer rates were selected based on recommended and existing management practices for BC: control (0 kg N ha-1), moderate (90 kg N ha-1), and high (120 kg N ha-1). An N application rate of 90 kg ha-1 is estimated as the average amount of N fertilizer used in the LFRV (Brisbin and Runka 1995); however, more current fertilizer application rates are reported to be 22  100 to 123 kg N ha-1 (Lundstrum, personal communication, April 2018). In addition to the N fertilizer treatments, 85 kg P ha-1 and 162 kg K ha-1 were applied to all plots. Urea was used as the N fertilizer and was applied by banding it in each row at the time of potato planting, as typically done. The field sites were prepared for planting by mowing the grass and weeds that had grown during the winter then the soil was tilled. A Kubota L3301 HST 4WD tractor (Kubota Canada, Canada) and hiller attachment were used to prepare the field and make rows that were 30 cm wide and 60 cm apart from each other. Kennebec seed potatoes (Solanum tuberosum L. cv. Kennebec) were hand planted in all of the typical planting treatment plots on May 31, 2018 at a rate of 1800 kg ha-1 and depth of 10 cm. The late planting treatment plots were planted with the same spacing and depth on June 18, 2018 (i.e., 18-days after the typical planting date). The fields were irrigated to approximately 12% volumetric water content every two weeks from June 21 to August 14, 2018 using drip irrigation. The plants in the typical and late planting treatments were hilled on July 11 and July 27, 2018, respectively. The vines were mechanically cut with a weed-eater two weeks before harvest to allow the potato tuber skins to harden. The potatoes were harvested by hand on September 16, 2018 and October 3, 2018 for the typical and late planting treatments, respectively.  2.2.2 Sampling and Analysis Greenhouse gas emissions Static collars and the Gasmet DX-4040 (Gasmet Technologies Oy, Finland) were used to measure GHG emissions of CO2, N2O, and CH4. One collar was placed between two plants in each treatment plot. The collars were constructed from PVC pipe with a 10 cm diameter. The static collars were installed at least 24 hours before measurements were taken and routinely 23  checked to ensure that the soil surface inside was level. Any vegetation growing inside the chamber was carefully removed, minimizing disturbances to the soil. On the day of making measurements, the Gasmet was inspected for leaks prior to use. The zero level for all of the gases was verified by flushing the system with N2 gas, and when necessary, the zero level was also calibrated and the instrument offset was adjusted. The Gasmet also underwent weekly maintenance to ensure that it was functioning properly. From late May to October 2018, GHG measurements were taken in both fields every two weeks, for a total of 10 days. Measurements were also taken in the productive field on November 11, 2018 to quantify the emissions after a heavy rainfall event that is typical in the region during the winter. Due to poor road conditions, I was unable to take measurements from the unproductive field on this day. To take GHG flux measurements at each plot, the chamber lid was placed on the chamber for 6 minutes. After this time the chamber lid was removed and left upright for at least 2 minutes to allow for the GHG concentrations in the sampling system to reach ambient conditions before moving on to the next chamber. Ancillary data of air temperature, soil temperature, soil water content, and chamber height were recorded for each plot during the enclosure time for the flux measurement. The soil temperature was recorded at the start and end of the enclosure time. Air temperature inside the chamber was measured at the start, mid-point, and end of the enclosure time. Volumetric water content was measured during the enclosure time using a Fieldscout TDR 100 (Spectrum Technologies, Inc. Plainfield, IL) at 3 equidistant points approximately 12 cm from the collar. The chamber height was measured at four points within the chamber using a ruler.  24  The GHG fluxes were calculated with MATLAB version R2014a (Mathworks 2014) using the following formula: Equation 2.1 𝐹𝐹 = 𝜌𝜌 𝑆𝑆 𝑉𝑉 / 𝐴𝐴 where F is the flux (µmol m−2 sec−1), ρ is the molar density (mol m-3) of dry air as corrected for the average mixing ratio of water, S is the slope of the accumulation of the greenhouse gas (expressed as a mixing ratio) in the headspace of the collar as determined by a linear regression using 1 minute of measurements for CO2 and 5 minutes of measurements for N2O and CH4 (µmol mol−1 sec−1), V is the volume of the headspace (m3), and A is the soil surface area in the headspace (m2).   The total GHG flux over the growing season was calculated using piecewise linear interpolation between the days of GHG flux measurements (Gana et al. 2018). The combined effects of CO2 N2O, and CH4 emissions was calculated as CO2 equivalents (CO2e): Equation 2.2 CO2e = 𝑚𝑚CO2  𝐹𝐹CO2 + GWPN2O 𝑚𝑚N2O 𝐹𝐹N2O + GWPCH4  𝑚𝑚CH4  𝐹𝐹CH4 where CO2e is the CO2 equivalents (kg CO2e ha-1), 𝑚𝑚CO2 is the molecular mass of CO2 (44.01 g mol-1), 𝑚𝑚N2O is the molecular mass of N2O (44.01 g mol-1), 𝑚𝑚CH4 is the molecular mass of CH4 (16.04 g mol-1), GWP is the global warming potential, which is 298 for N2O and 34 for CH4 (relative to CO2 which has a value of 1), over a 100-year time period while accounting for climate-carbon feedback (Myhre et al. 2013), and F is the cumulative flux of a given GHG (mol ha-1). Potato Yield  The harvested potatoes were washed, counted, and weighed for each plot. The total yield for the plot was calculated as fresh weight in kg ha-1. 25  Cost-Benefit Analysis of Potato Production Treatments  A number of different measures were used to assess the costs and benefits of the different N fertilizer and planting treatments at both fields. The N2O emission factor (EF), which estimates the amount of N2O emissions per unit of fertilizer N applied, was calculated as: Equation 2.3 𝐸𝐸𝐹𝐹 = (𝐸𝐸𝑖𝑖 − 𝐸𝐸0)𝑁𝑁× 100 where EF is the emission factor (%), Ei is the total N2O-N emissions over the growing season (May to October 2018) for a given N fertilizer treatment (kg N ha-1), E0 is the total N2O-N emissions over the growing season for the control N fertilizer treatment (kg ha-1), and N is the fertilizer rate (kg N ha-1). The total GHG emissions relative to the crop yield was calculated as a measure of the emission intensity (EI, kg CO2e (kg potato)-1): Equation 2.4  𝐸𝐸𝐸𝐸 = 𝐶𝐶𝐶𝐶2𝑒𝑒𝑌𝑌 where CO2e is calculated using Equation 2.2 (kg CO2e ha-1) for emissions from May to October 2018, and Y is the potato yield (kg ha-1) for a given N fertilizer treatment. The effect of the N fertilizer treatment on yield was assessed by calculating the N agronomic efficiency (AEN): Equation 2.5 𝐴𝐴𝐸𝐸𝑁𝑁 = (𝑌𝑌𝑖𝑖 − 𝑌𝑌0)𝑁𝑁  where AEN is the agronomic efficiency (kg potato (kg N)-1), Yi is the potato yield (kg ha-1) for a given N fertilizer treatment, Y0 is the potato yield (kg ha-1) in the control treatment (no fertilizer N), and N is the fertilizer rate (kg N ha-1). 26  The cost efficiency (CE) of potato production under different N fertilizer rates was assessed using the following equation: Equation 2.6 𝐶𝐶𝐸𝐸 = (𝑌𝑌𝑖𝑖 − 𝑌𝑌0) ∗ 𝑃𝑃𝐹𝐹𝐶𝐶 where CE is the cost efficiency ($ $ -1), Yi is the potato yield (kg ha-1) for a given N fertilizer treatment, Y0 is the potato yield (kg ha-1) for the control treatment, P is the price of potatoes ($ kg-1), and FC is the cost of N fertilizer for a given N fertilizer treatment ($ kg-1 N). In Fall 2018, the average price of fresh potatoes in BC was $0.64 per kg (Statistics Canada 2020a). The urea used in this study was purchased from a local fertilizer company and the price was $0.79 per kg. All prices are in Canadian dollars. 2.2.3 Statistical Analysis The results from the productive and unproductive sites were analyzed separately due to differences in the soil chemical and physical properties as reported by Lussier (2018) and Fausak (2019). To evaluate differences in GHG emissions and yield among the treatments, a linear mixed-effects model was used with fertilizer (three levels – zero, moderate, and high) and planting date (two levels – typical and late) as fixed effects, and fertilizer within each block as a random effect to match the split-plot experimental design. A likelihood ratio test was performed to assess if there was a significant interaction between the treatment factors. If there was no significant interaction between the fertilizer and planting date treatments, a Partial F-test was used to test for main effects on the simplified model. If there was an interaction, a follow-up Tukey multiple comparison procedure was conducted to test for differences between the N fertilizer rate and planting date treatment combinations. If there was no interaction but there were 27  main effects for either N fertilizer rate or planting date then a Tukey multiple comparison procedure was conducted to test for differences between the N fertilizer rates or planting dates.  2.3 Results Greenhouse gas emissions   Over the growing season, there were no significant differences in CO2 or N2O emissions across the treatments at the productive field or the unproductive field (Figure 2.1; Figure 2.2). The total CO2 and N2O emissions were notably higher at the productive field than the unproductive field. The total CH4 emissions did not have a clear difference between the productive and unproductive fields (Figure 2.3). The mean total CH4 emissions were generally negative, indicating that the fields acted as a sink. The high fertilizer – typical planting date treatment at the productive field, and the control – typical planting date treatment at the unproductive field both had a positive mean emission of CH4 over the growing season. The error bars for each of the total GHG figures illustrate the considerable field scale variability of these three gases. Including the November sampling date at the productive field in the calculation of the total N2O revealed clearer treatment differences though they were not statistically significant (Figure 2.4). The high fertilizer rate had approximately 9-times higher mean total N2O emissions when the November sampling date was included compared to when it was not. There was also a substantial increase in N2O emissions for the moderate fertilizer - late planting date treatment. The inclusion of the November sampling date did not have a similar effect on the total CO2 and CH4 emissions as the emissions for these GHGs remained relatively consistent (data not shown). 28  Using the May to October measurements, the total CO2e emissions were primarily composed of CO2 emissions (Figure 2.5). In the productive field, there were contributions of N2O emissions to the total CO2e as well. In both fields, the CH4 emissions ranged from -44.8 to 15.7 kg CO2e ha-1. While there was generally a net uptake of CH4 at the fields, it did not substantially offset total emissions of CO2 and N2O. There were no significant differences between treatments for either of the fields. Similar to the trends of total CO2 and N2O, the productive field had higher total CO2e compared to the unproductive field. For the productive field, including the November sampling date resulted in greater total emissions from the high N fertilizer rate but there were no statistically significant differences between treatments (Figure 2.6).  Potato Yield In the productive field, yield increased with increasing N fertilizer rates, and was significantly different between the control and the high rate (Figure 2.7). At the productive field, yield was 12% greater at the high N fertilizer rate relative to the moderate N fertilizer rate, but they were not significantly different from one another. The planting date did not have a significant effect on yield in either of the fields. In the unproductive field, there were no statistically significant differences in yield across the treatments.  Cost-Benefit Analysis  The N2O EF did not show any clear trends in N2O emissions (Table 2.2). There was substantial variability in the EFs at the field scale and no significant differences between the treatments. Similarly, the EI results showed that the total GHG emissions relative to the crop yield was not significantly different among the fertilizer and planting treatments (Table 2.2).  29   The AEN and CE results at both fields showed that both yield and revenue, did not significantly differ between moderate and high N fertilizer treatments (Table 2.3). Although the differences were not statistically significant, both of these measures showed marginal increases with greater amounts of N fertilizer application for the typical planting time. The opposite trend was found for the late planting time where the measures marginally decreased with greater amounts of N fertilizer. The unproductive field had consistently lower AEN and CE values than the productive field due to the lower yields.   2.4 Discussion There is a strong body of literature on the general contributions of N fertilizers to GHG emissions from cropland. On a regional scale, there is a need to better understand how crop management affects the production and consumption of GHGs. Quantification of CO2, N2O, and CH4 emissions from potato fields in Delta, BC, obtained in this study, provides a foundational understanding of how potato production contributes to the region’s agricultural GHG emissions.  As expected, there were no changes in soil CO2 emissions with the N fertilizer and planting treatments. The range of CO2 emissions was similar to those reported by Fausak (2019) who planted green beans and potatoes at same location as the productive field in 2018 and 2019, respectively. He found no differences in soil CO2 emissions between fertilized and unfertilized N treatments for either of the crops (Fausak 2019). Another study on a corn-soybean rotation found that ammonium nitrate fertilizer application at rates ranging from 0 to 225 kg N ha-1 had no effect on soil CO2 emissions at one of their four field sites (Al-Kaisi et al. 2008). Between the moderate and high N fertilizer treatments, there were no significant differences in N2O emissions. It is likely that I missed the peak N2O emissions following N 30  fertilizer application, contributing to the lack of significant differences between treatments. This is a limitation of the manual chamber method compared to other GHG measurement systems such as automatic chambers or eddy covariance systems that are more capable of measuring diurnal and seasonal variability (Butterbach-Bahl et al. 2016). The default EF for mineral fertilizers is 0.01, but there is a wide margin on this estimate as dependent on region and crop management (Rochette et al. 2018; IPCC 2019b). Research by Gao et al. (2013) on potato production in Manitoba using urea fertilizer, found that the N2O EF over the growing season (May to October) ranged from 0.1 to 0.83% in 2009 and 0.71 to 1.02% in 2010. I observed similar variability and range in our EF in the productive field. The N2O emissions induced by N fertilizer in the unproductive field were more consistent across treatments, yet the EF values were still higher than the default 0.01 from the IPCC (2019b).  The EFs of 1.1 and 0.6 for particular treatments are similar to those reported by Gao et al. (2013). The N2O emissions from May to October were used to calculate EFs in this study, but the onset of heavy precipitation in the LFRV during late October and November stimulates the production of N2O from N fertilizer additions which would increase EFs. Continuing measurements through the off-season can capture these N2O emissions induced by rainfall in the fall and spring in addition to freeze-thaw events. The inclusion of the November sampling date highlighted a peak in N2O emissions, especially for the high N fertilizer rate, that was not apparent in early October. More frequent sampling dates around the time of fertilizer application or rainfall events could have allowed for more measurements of temporal N2O hotspots. Nitrous oxide emissions are well-known for their spatial and temporal variability (Hénault et al. 2012; McDaniel et al. 2017). Nitrogen from fertilizer is often moved by runoff or leaching before it is converted to N2O, which could result in indirect N2O losses (Butterbach-Bahl et al. 2013; Berhe 31  et al. 2014). In this study, I focused on the measurement of the direct N2O emissions from the applied N fertilizer. For a comprehensive estimate of all of the N2O emissions induced by the N fertilizer, the indirect N2O emissions should also be considered. The lower N2O emissions in the unproductive relative to the productive field, was most likely due to the soil salinity issues at the former. High electrical conductivity (EC) influences soil microbial activity, leading to increases or decreases in N2O and CO2 emissions depending on the soil moisture conditions (Amos et al. 2005; Adviento-Borbe et al. 2006). Taking the influence of soil water content into consideration increases the complexity of N2O production in saline soils. Total N2O emissions from soils with an EC of 2 dS m-1 were higher when WFPS was 90% compared to 60% (Adviento-Borbe et al. 2006). I was unable to measure the GHG emissions from the unproductive field in November 2018; hence, I do not have evidence to assess how the combined effects of soil salinity and water content in the unproductive field influenced GHG emissions. Further research is required to understand how salinity and soil degradation issues contribute to the production of GHGs in the LFRV.  The mean total CH4 emissions were generally near zero with evidence of both CH4 production and consumption. My findings indicate that neither N fertilizer rate nor planting date had an effect on total CH4 emissions in either of the fields, supporting my hypothesis. The combination of CH4 production and consumption could be due to spatial heterogeneity (e.g., soil moisture) across the field or disturbances in the potato hills (McDaniel et al. 2017). Past research in the region has shown similar combinations of net CH4 emissions and consumption in crop production (Thiel et al. 2017; Fausak 2019). The total CO2e was primarily comprised of CO2 for both the May to October and the May to November calculations. Thiel et al. (2017) found similar results where the mean annual CO2e 32  was dominantly CO2 emissions, followed by N2O. The EI of the total GHG in CO2e was fairly low across all of the treatments. Compared to other crops, potato crops have a lower EI. Trost et al. (2016) found that the EI for a potato crop was similar across N fertilizer treatments.  The potato crop yield increased with N fertilizer rate in the productive field, and was in agreement with the findings of several other studies (White and Sanderson 1983; Gao et al. 2013, 2017; Maltas et al. 2018). Unexpectedly, potato yield at the unproductive field did not differ among the three N fertilizer rates. I expect that the lack of crop response to N fertilizer at the unproductive field is related to the soil degradation and high soil salinity at this site. Research in New Brunswick ranked the Kennebec potato variety as having relatively poor salinity tolerance in comparison to the other varieties grown in Canada (Khrais et al. 1998). A conflicting finding from a simulated soil salinity study in Quebec found that soil salinity and saline sub-surface irrigation water did not significantly impact total Kennebec potato tuber weights (Patel et al. 2001). The unproductive field is representative of other crop fields in the LFRV with poor productivity that is associated with soil degradation issues such as poor soil structure, acidification, and salinity (Lussier et al. 2019). These fields generally also have low vegetative biomass as determined from grassland set-aside research in the region (Lussier et al. 2019). Due to the complexity of the conditions in these unproductive fields, it is difficult to assess the main cause of the poor crop response to N fertilizer, but this may be an opportunity for further research. In Canada, marketable potato tubers must have a diameter between 5.1 cm and 11.4 cm (Nyiraneza et al. 2017; Canadian Food Inspection Agency 2018). I did not grade our potatoes according to these restrictions, so I were unable to determine the proportion of the total yield that meets the Canadian marketable potato tubers standards. Across BC, the estimated potato yield in 33  2018 was approximately 35,663 kg ha-1, which was 12% higher than the 2013-2017 average yield of 31,910 kg ha-1 (Province of British Columbia 2019).  The AEN and CE parameters suggest that the yield and revenue from the potato crop relative to the N fertilizer rate did not differ significantly between the moderate and high N fertilizer rates. The lack of difference in cost efficiency from applying 90 kg N ha-1 versus 120 kg N ha-1 supports lower N fertilizer application and future research should assess the BC Ministry of Agriculture’s recommended rate of 70 kg N ha-1 (British Columbia Ministry of Agriculture 2012a).  As I hypothesized, the planting date did not significantly influence GHG emissions or crop yields. A study by White and Sanderson (1983) found that delayed planting did not have a significant impact on Kennebec potato yields over 5 years of production. However, in the same study they found significant decreases in yield with later planting date for the Russet Burbank variety (White and Sanderson 1983). A delayed planting time in the spring, when soils are not too wet and at poor workability, helps farmers to prevent soil degradation due the use of heavy farm machinery. The delay in planting date pushes the harvest date further into the fall, unless a potato variety that would reach maturity earlier was used. Kennebec potatoes are a mid- to late-season variety that needs 90 to 130 days to reach maturity. In my study, both planting dates had the same number of days from planting to harvest, so the late planting treatment also had a late harvest. The later harvest date increases the risk of crop loss due to poor soil workability conditions in the fall. Another consideration in delaying crop planting is the planting deadline established by crop production insurance, limiting how long producers can delay planting. In BC, the planting deadline for potato crops is June 20, regardless of year, requiring producers to plant by that date in order to qualify for the insurance (British Columbia Ministry of Agriculture 34  2014). The suitability of other potato varieties to the changing climate and growing conditions in this region is currently being studied through the Potato Variety Evaluation project (Dickison et al. 2019). The evaluation of different varieties is a fundamental component in adapting potato crop production to the changing climate conditions in the LFRV.  2.5 Conclusions During the 2018 growing season there were no differences in total CO2, N2O, and CH4 emissions with increasing N fertilizer rate or delayed planting time. For the productive field, the sampling date in November, at the onset of the rainy season, allowed me to capture a peak in N2O production. The difference between treatments was still not significant, due to field variability but there were greater mean total N2O emissions for the moderate and high fertilizer treatments.  Yield increased with N fertilizer rate for the productive field, but the AEN and CE parameters emphasized that the yield and profitability of the 90 kg N ha-1 and 120 kg N ha-1 fertilizer application rates were comparable. The lack of potato yield response to increases in N fertilizer rates for the unproductive field, was most likely caused by soil degradation on that field. At both fields, the delayed planting treatment had similar yield to the typical planting treatment. This finding supports delaying potato planting until spring soil workability conditions are favourable. Important considerations with delayed planting include the crop insurance planting deadline and that the harvest date is also delayed. In the fall there is an increased risk of poor soil workability due to heavy precipitation which can limit potato harvest. The measurements of GHG emission from the potato field in this study contribute to filling a gap in agricultural GHG emissions data for the LFRV. While the findings from this 35  study are limited because of only having one production season of measurements, the quantification of GHG emissions from potato fields is critical in understanding how BC’s most economically important field vegetable contributes to agricultural GHG emissions.  The findings from this study support lowering the amount of inorganic N fertilizer inputs at fields with soil degradation, like the unproductive field, which will subsequently lower residual N losses to the environment through N2O emissions or leaching. At productive fields, further research across a larger number of field sites and years is required to determine if 120 kg N ha-1 is considered excessive across the LFRV.                 36  Table 2.1 Baseline soil properties of the productive and unproductive fields, measured in May 2018. The standard error of the mean is shown in brackets (n = 18 for productive field; n = 9 for unproductive field). Property Units Depth (cm) Productive Field Unproductive Field Sand1 % 0-15 25 (0.7) 29 (1.2)   15-30 26 (0.3) 28 (0.0) Silt1 % 0-15 59 (0.7) 53 (1.2)   15-30 58 (0.0) 55 (1.5) Clay1 % 0-15 16 (0.0) 18 (0.9)   15-30 16 (0.3) 17 (1.5) pH (water)  0-15 5.9 (0.08) 6.3 (0.11)   15-30 6.0 (0.06) 6.8 (0.11) pH (CaCl2)  0-15 5.4 (0.07) 5.3 (0.07)   15-30 5.4 (0.07) 5.5 (0.08) Electrical Conductivity1 dS m-1 0-15 1.19 (0.30) 2.43 (0.70)   15-30 1.20 (0.30) 4.03 (1.20) Total C % 0-15 1.5 (0.04) 1.9 (0.06)   15-30 1.4 (0.05) 1.6 (0.04) Total N % 0-15 0.6 (0.00) 0.2 (0.00)   15-30 0.1 (0.00) 0.1 (0.00) 1 These parameters were determined in May 2017 by Fausak (2019) when the experimental fields were established (n = 12).  37   Figure 2.1 Total CO2 emissions (kg C ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error bars represent the standard error of the mean (n = 3). There was a significant interaction between the fertilizer and planting treatments in the productive field, but not in the unproductive field. There were no significant differences between treatments (α = 0.05).   Figure 2.2 Total N2O emissions (kg N ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error bars represent the standard error of the mean (n = 3). For both fields, the interaction between fertilizer and planting date was not significant and there were no significant differences between treatments (α = 0.05). 38   Figure 2.3 Total CH4 emissions (kg C ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error bars represent the standard error of the mean (n = 3). There was a significant interaction between the fertilizer and planting treatments in the productive field, but not in the unproductive field. For the productive field, different letters indicate a statistically significant difference between treatments (α = 0.05). In the unproductive field there were no significant differences between treatments (α = 0.05).   Figure 2.4 Total N2O emissions (kg N ha-1) from the treatments in the productive field using measurements in 2018 from May to October (October end date) (shown in Figure 2.2) and May to November (November end date). Error bars represent the standard error of the mean (n = 3). There were no significant differences between treatments (α = 0.05).  39   Figure 2.5 Total GHG emissions in CO2 equivalents (CO2e) (Mg CO2 ha-1) from the treatments in both the productive and unproductive fields, as calculated for late May to October 2018. Error bars represent the standard error of the mean (n = 3). There was a significant interaction between the fertilizer and planting treatments in the productive field, but not the unproductive field. In both fields, there were no significant differences between treatments (α = 0.05).   Figure 2.6 Total GHG emissions in CO2 equivalents (CO2e) (Mg CO2 ha-1) from the productive field calculated using the measurements from late May to November 2018. Error bars represent the standard error of the mean (n = 3). There were no statistically significant differences between the treatments (α = 0.05). 40   Figure 2.7 Potato yield (Mg ha-1) at the productive and unproductive fields. Error bars represent the standard error of the mean (n = 3). There was no significant interaction between fertilizer and planting treatments. In the productive field, there was a significant difference between fertilizer treatments as indicated by different letters (α = 0.05). In the unproductive field, planting date had a significant effect on yield but there were no significant differences between treatments (α = 0.05).                      41  Table 2.2 The N2O emission factor (EF) and the emission intensity (EI) of the total GHG emissions in CO2 equivalents for the productive and unproductive fields for GHG measurements taken from May to October 2018. The standard error of the mean is shown in brackets (n = 3). At both fields, there was no interaction between the N fertilizer and planting date treatments for either of the measures. There were no significant treatment differences in N2O EF or EI between the treatments (α = 0.05). Field Planting N Fertilizer  N2O EF EI  Date Rate (%) (kg CO2e / kg yield) Productive Typical Control NA 0.3 (0.07)   Moderate 0.0 (1.08) 0.1 (0.03)   High 1.1 (1.27) 0.2 (0.01)  Late Control NA 0.2 (0.06)   Moderate 0.6 (1.12) 0.2 (0.02)   High 0.1 (0.60) 0.1 (0.03) Unproductive Typical Control NA 0.1 (0.04)   Moderate 0.1 (0.05) 0.1 (0.00)   High 0.2 (0.16) 0.1 (0.03)  Late Control NA 0.2 (0.03)   Moderate 0.1 (0.23) 0.1 (0.02)   High 0.3 (0.15) 0.1 (0.00)         42  Table 2.3 The agronomic efficiency (AEN) and cost efficiency (CE) of different treatments in the productive and unproductive fields. The standard error of the mean is shown in brackets. At both fields, there was no interaction between the N fertilizer and planting date treatments and no significant treatment differences for either AEN or CE (α = 0.05). Comparisons cannot be made between the productive and unproductive fields. Field Planting N Fertilizer  AEN CE  Date Rate (kg yield / kg N fertilizer) (crop revenue / N fertilizer cost) Productive Typical Control NA NA   Moderate 148.1 (17.59) 55.2 (6.55)   High 164.3 (18.16) 61.2 (6.77)  Late Control NA NA   Moderate 127.1 (37.49) 47.4 (13.97)   High 113.3 (81.81) 42.2 (30.49) Unproductive Typical Control NA NA   Moderate 2.9 (60.70) 1.1 (22.62)   High 31.3 (36.03) 11.7 (13.43)  Late Control NA NA   Moderate 74.6 (59.34) 27.8 (22.11)   High 41.9 (23.54) 15.6 (8.77) 43  Chapter 3: Effects of N Fertilizer Rate and Planting Date on Soil Nitrogen Dynamics and Potato Yield  3.1 Introduction Potatoes are a globally important crop but intensive management practices associated with potato production, such as high fertilizer application rates and routine tillage, often have negative impacts on soil quality (Stark and Porter 2005; Pawelzik and Möller 2014). Tillage enhances decomposition of soil organic matter, compacts soils and destroys soil structure, especially when soil workability is poor (Angers et al. 1999). While it might take total soil carbon (C) years and even decades to respond to management practices, labile soil C is much more responsive as its changes over shorter timescales (Janzen et al. 1998). Among the many labile C fractions, permanganate oxidizable carbon (POXC) is regarded as being sensitive to management practices (Bongiorno et al. 2019) and is closely associated with aggregate stability (Wang et al. 2019; Lussier et al. 2020).  Nitrogen is the most limiting nutrient for potato growth and the excessive N fertilizer rates are often applied to avoid yield losses. Nitrogen uptake by potato plants increases with N fertilizer rate (Zebarth and Rosen 2007). Approximately 45 days after planting, N is translocated from aboveground biomass to the potato tubers (Zebarth and Rosen 2007). With increasing N fertilizer rate, tuber yield increases until it reaches a maximum that is dependent on environmental conditions, soil type, and potato cultivar. The development of N fertilizer use efficiency strategies that balance yield and environmental sustainability in potato production has been a topic of extensive research (Stark and Porter 2005; Zebarth and Rosen 2007; Gao et al. 2015). 44  In British Columbia (BC), the majority of potatoes are produced in the Lower Fraser River Valley (LFRV). The region’s recommended N fertilizer rate for potato production is 70 kg N ha-1 (British Columbia Ministry of Agriculture 2012a), but 90 kg N ha-1 is the average amount of fertilizer applied with reported rates as high as 112 kg N ha-1 (Brisbin and Runka 1995). The application of excess N fertilizer lowers the economic risk of production, but increases the risk of environmental pollution as residual nitrates (NO3-) can leach into groundwater or stimulate the production of nitrous oxide (N2O) (Zebarth and Rosen 2007).  Most of the LFRV is occupied by very productive, medium to fine textured soils that are suitable for a wide range of agricultural crops. However, some agricultural fields in this region have poor natural drainage that is further compromised by a heavy rainfall in the spring and fall. Delayed crop planting is a solution to avoid poor soil workability conditions in the spring but there is minimal data that provides insights into how this may affect crop quality, yields, and soil nutrients. Existing literature emphasizes how planting date affects crop quality or irrigation efficiency with limited research on how soil properties are affected (White and Sanderson 1983; Kawakami et al. 2005; Paredes et al. 2018).   The objective of this study was to determine the effects of three N fertilizer rates (0, 90, and 120 kg N ha-1) and potato planting dates (typical and late) on plant available N (PAN) (NH4+-N and NO3--N), POXC, total soil C and N, potato yield quality and quantity. I hypothesized that (i) PAN, total soil N, potato plant biomass, and potato plant total N will increase with N fertilizer rate; (ii) POXC, total soil C, and potato plant total C will not significantly change with N fertilizer rate treatments; and (iii) the planting date will not influence soil and plant properties. 45  3.2 Materials and Methods 3.2.1 Site Description and Experimental Design The experiment was conducted at two operational farm fields in the Municipality of Delta, BC from May to November 2018 (Appendix A). A study by (Lussier et al. 2019) categorized one of the field sites (49.065087, -123.137965) as productive, and the other (49.057207, -123.123570) as unproductive, based on soil properties such as electrical conductivity, sodium concentration, and the amount of aboveground biomass.  The LFRV receives approximately 1,180 mm in annual precipitation of which 75% occurs from October to April (Appendix B). The field sites are situated on the Fraser River Delta, and have nearly level slope (< 3%) with average elevation of 2 metres above sea-level (Luttmerding 1981). The soil in this region is formed on fine to medium-textured deltaic deposits from the Fraser River. Both of the field sites have a silt loam soil texture (Table 2.1) (Fausak 2019). The soil at the productive field is classified as either Blundell Rego Gleysol and Crescent Orthic Gleysol, while the soil at the unproductive field is classified as Ladner Humic Luvic Gleysol (Luttmerding 1981; Fausak 2019). All three of the soil series found at the experimental sites feature poor to very poor drainage (Luttmerding 1981). The field experiment was established in May 2018 at both fields as a randomized block design with three N fertilizer application rates and three replicates of each treatment. Each fertilizer treatment plot was divided into typical planting and late planting treatments (Appendix C) The following fertilizer rates were selected based on recommended and existing management practices for BC: control (0 kg N ha-1), moderate (90 kg N ha-1), and high (120 kg N ha-1). An N application rate of 90 kg ha-1 is estimated as the average amount of N fertilizer used in the LFRV (Brisbin and Runka 1995); however, more current fertilizer application rates are reported to be 46  100 to 123 kg N ha-1 (Lundstrum, personal communication, April 2018). In addition to the N fertilizer treatments, 85 kg P ha-1 and 162 kg K ha-1 were applied to all plots. Urea was used as the N fertilizer and was applied by banding it in each row at the time of potato planting, as typically done. The field sites were prepared for planting by mowing the grass and weeds that had grown during the winter then the soil was tilled. A Kubota L3301 HST 4WD tractor (Kubota Canada, Canada) and hiller attachment were used to prepare the field and make rows that were 30 cm wide and 60 cm apart from each other. Kennebec seed potatoes (Solanum tuberosum L. cv. Kennebec) were hand planted in all of the typical planting treatment plots on May 31, 2018 at a rate of 1800 kg ha-1 and depth of 10 cm. The late planting treatment plots were planted with the same spacing and depth on June 18, 2018 (i.e., 18-days after the typical planting date). The fields were irrigated to approximately 12% volumetric water content every two weeks from June 21 to August 14, 2018 using drip irrigation. The plants in the typical and late planting treatments were hilled on July 11 and July 27, 2018, respectively. The vines were mechanically cut with a weed-eater two weeks before harvest to allow the potato tuber skins to harden. The potatoes were harvested by hand on September 16, 2018 and October 3, 2018 for the typical and late planting treatments, respectively.  3.2.2 Sampling and Analysis Plant available N The analysis of PAN, included the determination of NH4+-N (Weatherburn 1967) and NO3--N (Doane and Horwáth 2003), and was done on soil samples that were collected from 0-15 cm and 15-30 cm depths. Samples were collected from May to October 2018, at the following seven times: 7/14 days before planting, 1/3, 17/18, 36/45, 63/72, and 90 days after planting, and 47  at harvest. Between the typical and late planting treatments, the sampling dates had discrepancies in the number of days after planting, but they were grouped as shown above for analysis purposes. Samples were transported in coolers and stored at 4°C until extraction with 25 mL of 2 M KCl that was done within 48 hours of sampling. During extraction, the samples were shaken on a reciprocal shaker for 20 minutes, then centrifuged at 2000 rpm for 5 minutes, filtered through a Fisherbrand Q2 filter paper, and analyzed colorimetrically using a 96-well microplate absorbance reader (Biorad iMark, Hercules, CA, USA). Gravimetric soil water content (SWC) was measured by oven-drying 20 g of soil sample at 105ºC for 24 hours.  Total soil C and N  For determination of the total soil C and N, samples were taken from 0-15 cm and 15-30 cm depths prior to planting, at the time of planting, and at harvest. These samples were air-dried, ground with a rolling pin, passed through a 2-mm sieve, and then ball-mill ground for analysis of total C and N using mid-infrared FTIR spectroscopy (Reeves III. et al. 2002). For each of the ball-mill ground samples, three replicates of 0.025 g were tested 60 times for spectral reflectance across a range of wavelengths from 400 to 4000 cm-1 with a 2 cm-1 resolution using the Tensor 37 HTS-XT spectrophotometer (Bruker Optics Gmbh, Ettlingen, Germany). We also analyzed 25% of the prepared samples for total C and N with a Vario EL Cube Elemental Analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany). The data from the elemental analyzer were used to calibrate and validate the FTIR data in a partial least square regression model (Paul et al. 2020).     48  Permanganate oxidizable carbon  For POXC analysis, soil samples were taken at the 0-7.5 cm depth prior to planting and at harvest in 2018. The analysis of POXC, was based on the Weil et al. (2003) method and the detailed procedure by Culman et al. (2012) (Appendix D). Absorbance at 550 nm was determined using a microplate spectrophotometer (TECAN Group Ltd., Zurich, Switzerland).  Aboveground and tuber biomass and total C and N analysis Potato aboveground biomass samples were collected at mid-season (53 days after planting) then again at the time of vine-killing. A 1 m × 1 m quadrat was randomly placed over the two inside rows of each plot and six plants from within the quadrats were collected. The samples were weighed, oven-dried at 60ºC, then re-weighed to calculate the dry matter weight (DMW). The dry vegetation was set aside for the determination of the total C and N. The harvested tubers of potatoes were washed and graded by size into the following five classes; B = > 7.62 cm, A1 = 5.72 - 7.62 cm, A2 = 3.81 - 5.72 cm, A3 = 2.54 - 3.81 cm, and A4 = < 2.54 cm following Fausak (2019). For each treatment plot and size class, the tubers were counted, weighed, and a subsample of the tubers, proportional across the size classes, were cut into 2.5 cm cubes. For each treatment combination, a 20 g sample of the cubed tubers were weighed, oven-dried at 60ºC, then re-weighed to calculate the DMW. The remaining cubed potato samples were stored at -18ºC until analysis of total C and N.  The dried aboveground biomass samples were ball-mill ground and analyzed for total C and N with a Vario EL Cube Elemental Analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany). Tuber samples were freeze dried with a Bulk Tray Vacuum Freeze Drier (Labconco, Kansas City, MO, USA) at -55ºC for four days then ball-mill ground. The samples were analyzed using the elemental analyzer, as described above, to determine total C 49  and N concentration (%). The total C and N content of the aboveground biomass was calculated using the mass of the dry biomass. 3.2.3 Statistical Analysis The differences in PAN, POXC, aboveground and tuber biomass, and total soil C and N, aboveground, and tuber biomass between the treatments were analyzed using ANOVA and Tukey post-hoc tests. A linear mixed-effects model was used with fertilizer (three levels – zero, moderate, and high) and planting date (two levels – typical and late) as fixed effects, and fertilizer within each block as a random effect to match the split-plot experimental design. A likelihood ratio test was performed to assess if there was a significant interaction between the treatment factors. If there was no significant interaction between the fertilizer and planting date treatments, a Partial F-test was used to test for main effects on the simplified model. If there was an interaction, a follow-up Tukey multiple comparison procedure was conducted to test for differences between the N fertilizer rate and planting date treatment combinations. If there was no interaction but there were main effects for either N fertilizer rate or planting date then a Tukey multiple comparison procedure was conducted to test for differences between the N fertilizer rates or planting dates. The normality and variance homogeneity assumptions of ANOVA were confirmed using Shapiro-Wilks test and Levene’s test, respectively, in addition to diagnostic plots. I used data transformations, when necessary, to ensure the ANOVA conditions were met. A statistical significance threshold of α = 0.05 was used for hypothesis testing. All of the statistical analyses were conducted using R software version 3.6.2 (R Core Team 2019).   50  3.3 Results Plant available N The N fertilizer rate and planting date had a significant effect on NH4+-N and NO3--N (Appendix E). Between the moderate and high N fertilizer rates, NH4+-N at the 0-15 cm depth was not significantly different at any point during the growing season for either field (Figure 3.1). For the productive field, NH4+-N at the 0-15 cm depth reached peak 36/45 days after planting on the moderate and high N fertilizer treatments; where NH4+-N was significantly greater than on the control. Following this peak, NH4+-N of the moderate and high N fertilizer treatments remained significantly greater than the control. At the 15-30 cm depth there were periodic peaks in NH4+-N and at 36/45 days after planting the high N fertilizer – typical planting treatment was significantly lower than the high N fertilizer – late planting treatment.  The unproductive field had a peak in NH4+-N at the 0-15 cm depth within days of planting for the late planting treatment, but the moderate and high N fertilizer treatments were not significantly different from the control until 36/45 days after planting. The typical planting treatment at the unproductive field reached peak NH4+-N 36/45 days after planting and it was significantly different than the control. At the unproductive field’s 15-30 cm depth there was a small peak in NH4+-N for the high N fertilizer – late planting treatment but it was not significantly different from the other treatments. Although the values at 15-30 cm are near zero 90 days after planting the high N fertilizer treatment was significantly greater than the control.  The NO3--N for the moderate and high N fertilizer treatments at 0-15 cm depth were not significantly different from each other but they were both significantly greater than the control at both fields for a number of sampling days (Figure 3.2). Peak NO3--N was reached at similar times as NH4+-N at both fields at 0-15 cm, but at 90 days after planting it began to increase again 51  at both fields. At 90 days after planting, there is an ongoing increase in N uptake by potato plants for tuber growth. At the 15-30 cm depth for the productive field, NO3--N for the moderate and high N fertilizer treatments was significantly greater than the control at 17/18 and 36/45 days after planting. At the time of harvest for both fields, the typical planting had a significantly lower NO3--N than the late planting. Total soil C and N  There were no significant differences in total soil C or N concentration among the treatments at both of the depths and fields, with the exception of total C at the 15-30 cm depth of the productive field where late planting had 0.23% more total soil C than the typical planting (Table 3.2). Total C and N were relatively unchanged though across both N fertilizer and planting treatments at the end of the growing season relative to before planting (Table 3.2). At 0-15 cm for both fields, there was a slight increase in total soil N with the moderate and high N fertilizer rates. Permanganate oxidizable carbon   At the end of the growing season, planting date had a significant effect on POXC, while N fertilizer rate did not (Table 3.2). The late planting had a higher amount of POXC compared to the typical planting. This difference was statistically significant at the unproductive field but not the productive field. Generally, the POXC at the unproductive field was higher than the POXC at the productive field. There was also a considerable increase in the amount of POXC, nearly 100 mg kg-1, from before planting to the end of the growing season in the late planting treatment at the unproductive field.   52  Aboveground and tuber biomass   The treatment effect on the amount of aboveground biomass differed between the mid-season and time of vine-killing. For both fields, the moderate and high N fertilizer rates with typical planting time had greater mean aboveground biomass compared to the late planting, though this difference was not statistically significant due to variability at the field scale (Figure 3.3). At the time of vine-killing the mean aboveground biomass was consistently higher for the late planting treatment than the typical planting treatment but there were no significant differences between treatments (Figure 3.3).   The tuber biomass increased with N fertilizer at the productive field and the high N fertilizer treatment was significantly higher than the control (Figure 3.3). For the unproductive field the tuber biomass did not significantly change with N fertilizer rate. There were no significant differences in tuber biomass with planting date at either of the fields. Aboveground biomass and tuber C and N   There were limited changes in total C concentration of the aboveground biomass at the mid-season, with no significant differences among the treatments at either of the fields (Table 3.3). The total C content of the aboveground biomass at mid-season was significantly greater in the moderate and high N fertilizer rate treatments compared to the control at the productive field (Table 3.4). Additionally, at the productive field the typical planting had greater total C content than the late planting. Aboveground biomass samples taken at vine-killing had similar C concentrations as samples taken at mid-season but greater total C content. For the productive field at vine-killing, the typical planting – control N fertilizer rate treatment had significantly higher total C concentration than late planting – control N fertilizer rate treatment, while the other treatments had no differences. There were also significant differences at the unproductive 53  field with higher total C concentration in the typical planting – high N fertilizer rate treatment compared to the both the late planting – moderate N fertilizer rate and late planting – high N fertilizer rate treatments. At the unproductive field, the typical planting treatment had slightly lower total C content than the late planting treatment. The total C concentration in the tubers was significantly higher in the late planting for the productive field, yet the values were similar between the planting treatments. The treatments at the unproductive field had no significant differences in C concentration in the tubers. Planting date did not have an effect on total C content but there was an increase in total C content with N fertilizer rate at the productive field.   There were significant differences in total N concentration among N fertilizer treatments at the mid-season for both fields (Table 3.3). The total N content of aboveground biomass also increased with N fertilizer at the mid-season but only at the productive field (Table 3.4). With greater N fertilizer rates, the N concentration and content of the aboveground biomass generally increased but the moderate and high N fertilizer rates were not significantly different from one another. By the time of vine-killing the N concentration of the aboveground biomass decreased since the mid-season sampling and there were no longer treatment differences. At the productive field there were still significant differences in total N content of the aboveground biomass at vine-killing with N fertilizer rate. The tubers from the productive field were significantly affected by planting date as the typical planting had lower N concentration than the late planting. The total N content of the tubers at the productive field was significantly greater where N fertilizer was applied, but the moderate and high N fertilizer rates were not significantly different from each other. There were no significant differences in tuber N concentration or content among treatments at the unproductive field.  54  3.4 Discussion I hypothesized that PAN would increase with N fertilizer rate and found that PAN for the moderate and high N fertilizer rates did increase relative to the control, but that it did not differ between those two N rates. Previous research reported similar results where N fertilizer rate had a significant effect on PAN but the differences were not always significant (Giletto et al. 2019). With the moderate and high N fertilizer rates, there were greater amounts of soil NO3--N at harvest compared to before the N fertilizer was applied at the start of the growing season. In the unproductive field, there was an upward trend in NO3--N at the 0-15 cm depth in the latter half of the growing season, while NO3--N plateaued at the productive field. The greater accumulation of NO3--N at the unproductive field was due to the poor crop response to the application of N fertilizer application. As the crops were not utilizing the N fertilizer there was greater potential for N losses. The residual NO3--N found after harvest could potentially be lost through leaching, especially with the heavy precipitation that occurs in the fall. I did not measure NO3--N below 30 cm but it could have provided insights into the movement NO3--N through the soil profile and an estimate of leaching losses.  There were no differences in total soil C or N concentrations with either treatment. Research by Tein et al. (2014) on conventional potato production with N fertilizer rates ranging from 0 kg N ha-1 to 150 kg N ha-1 had similar results. In their study some years had no differences in total soil C or N following potato harvest with N fertilizer rate. They also found that comparing soil C before potato planting and one year later, soil C was significantly lower in treatments with greater N fertilizer rates (Tein et al. 2014). Measurements of total soil C and N concentration were made before potato planting and at the time of harvest so one growing season was not long enough to observe differences. 55  For the productive field, there were no changes in POXC, as I hypothesized. Unexpectedly, the unproductive field had greater POXC in the late planting treatment compared to the typical planting treatment. I believe that the difference in POXC with planting date was associated with crop residues left on the soil following vine-killing in late September for the late planting treatment and the subsequent increase in precipitation. When vine-killing occurred for the typical planting treatment there was less precipitation that occurred in the 2 weeks until POXC samples, compared to the late planting treatment. Coppens et al. (2006) found that precipitation events stimulate the decomposition of surface applied crop residues, then as the residues dry the rate of decomposition decreases. We did not sample for POXC beyond the harvest date, but I expect that it would have decreased with time as decomposition processes continued. At the mid-season there was more aboveground biomass in the typical than late planting treatment, but this difference was not observed at the time of vine-killing. The PAN levels at the time from planting to mid-season did not significantly differ with planting date treatments indicating that N was not a limiting factor. It is possible that differences in air temperature and SWC during the early growth stages for each planting treatments were influential factors (Westermann 1993; Timlin et al. 2006). Environmental conditions such as air and soil temperature, SWC, and level of photosynthetically active radiation are highly influential on plant growth and development (Westermann 1993; Zebarth and Rosen 2007). When day and night temperatures increase above 27ºC and 15ºC, respectively, daily potato plant growth is limited (Haverkort and Verhagen 2008). The late planting treatment had a greater number of days beyond these thresholds from the time of planting to mid-season sampling. The N concentrations of the aboveground biomass decreased from mid-season to vine-killing, as expected due to the 56  increase in N portioning to the tubers and senescence of the plant leaves (Biemond and Vos 1992; da Silva et al. 2018).  The amount of dry tuber biomass in the control was comparable to the moderate N treatment at the productive field although PAN levels were low. Instances of N deficiency lead to greater amount of biomass in tubers and root systems in potatoes, which explains the amount of tuber biomass in the control (Bélanger et al. 2001). Total N concentration in tubers generally increases with N fertilizer rate (Tein et al. 2014; Caldiz et al. 2018), but I did not observe this trend. Environmental factors may have contributed to the lack of differences as the LFRV experiences low precipitation and high temperature during the growing season, while the fields were irrigated every 2 weeks. The optimum SWC for N mineralization is between 50 and 80% of field capacity so the low SWC between irrigation dates could have limited microbial N transformations to plant-available forms and reduced the accessibility of PAN (Whalen and Sampedro 2010; St. Luce et al. 2011). As I hypothesized, total C concentration in tubers did not change with N fertilizer rate. The majority of studies investigate more specific categories of C such as sugars and starches to determine tuber quality, but I did not analyze these properties in this study (Zebarth et al. 2004; Tein et al. 2014; Gao et al. 2015). The total N content of the tubers increased where N fertilizer was applied, compared to the control which shows that the tubers are accumulating N when it is available. However, there were no significant differences in total N content in aboveground biomass at vine-killing or tubers between the moderate and high N fertilizer rates indicating that excess N from the high N fertilizer treatment is not being taken up by the plants. Focusing on crop management, the high N fertilizer rate was not necessary compared to the moderate rate. The productive and unproductive fields had different responses to the N 57  fertilizer and planting treatments in terms of PAN and tuber biomass. For the productive field, the high N fertilizer rate resulted in greater PAN early on in the growing season during plant development, but it did not significantly increase tuber biomass. The planting date did not have a strong effect on these properties for the productive field. There was still residual N left at harvest with the moderate and high fertilizer treatments at the productive field and the high fertilizer rate did not always have greater residual N than the moderate rate. The presence of relatively high residual soil N indicates that there could still be improvements made in N fertilizer management. This study only focused on the effects of N rates, but improving the application timing or type of fertilizer used could be beneficial. The tuber biomass at the unproductive field was similar whether N fertilizer was applied or not and was not affected by planting date. The unproductive field had a greater amount of residual NO3--N with the late planting compared to the typical planting though, which increases the potential for N losses to groundwater or the atmosphere. This finding highlights the difference in treatment response between agricultural soils with different levels of soil quality. There is opportunity to improve the management of degraded agricultural soils in the LFRV to promote better N use-efficiency and prevent environmental pollution due to N losses.  3.5 Conclusions Over one growing season, N fertilizer rate and planting date resulted in significant differences for PAN, POXC, and plant C and N concentrations. For both the productive and unproductive fields, the moderate and high N fertilizer rates significantly increased PAN, especially at the 0-15 cm depth. With the application of N fertilizer, there was greater NO3--N at the end of the growing season than at the start. The residual NO3--N, especially in the 58  unproductive field, supports the need for further research on understanding how best management practices in N fertilizer-use efficiency may differ among soils in the LFRV that have varying degrees of degradation. The duration of the experiment was not long enough to observe changes in total soil C or N concentrations in response to the treatments. The POXC, representing the more labile C pool, in the unproductive field had an unexpected increase in response to planting date that can be attributed to decomposition of crop residues following an increase in precipitation.  For the plant properties, the effect of N fertilizer rate and planting date was dependent on the number of days after planting. Aboveground biomass was significantly different with planting date for the productive field at mid-season, but the treatment effect was not observed at maturity, when vine-killing occurred. For the productive field, dry biomass of tubers increased with N fertilizer rate while there were no treatment effects at the unproductive field. The lack of treatment response to N fertilizer rate at the unproductive field highlights the need for a different approach to improving N fertilizer-use efficiency at fields with drainage issues. There were no changes in total C in the plant biomass, but N fertilizer rate and planting date influenced total N in the aboveground biomass at the mid-point of the growing season. The lack of treatment differences for total N in the plant biomass at vine-killing and harvest could have been a result of low SWC as environmental factors often limit plant growth and nutrient content, even when there is an availability of nutrients.  The measurements of soil and plant properties from only May to October 2018 are limitations of this study as treatment effects on soil C and N concentrations likely extended past the growing season. Additionally, continuous crop management with these treatments over multiple years and evaluating potato production within a crop rotation could provide a better 59  understanding the long-term effects of N fertilizer rates and delayed planting dates. The findings from this study provide insights into how N fertilizer rates and planting date in potato production influence soil and crop quality within the LFRV.                    60  Table 3.1 Soil properties of the productive and unproductive fields, measured prior to planting in May 2018. The standard error of the mean is shown in brackets (n = 18 for productive field; n = 9 for unproductive field). Property Units Depth (cm) Productive Field Unproductive Field Sand1 % 0-15 25 (0.67) 29 (1.15)   15-30 26 (0.33) 28 (0.00) Silt1 % 0-15 59 (0.67) 53 (1.20)   15-30 58 (0.00) 55 (1.53) Clay1 % 0-15 16 (0.00) 18 (0.88)   15-30 16 (0.33) 17 (1.53) pH (water)  0-15 5.9 (0.08) 6.3 (0.11)   15-30 6.0 (0.06) 6.8 (0.11) pH (CaCl2)  0-15 5.4 (0.07) 5.3 (0.07)   15-30 5.4 (0.07) 5.5 (0.08) Electrical Conductivity1  dS m-1 0-15 1.19 (0.30) 2.43 (0.70)   15-30 1.20 (0.30) 4.03 (1.20) Aggregate Mean Weight Diameter mm 0-7.5 14.24 (0.03) 13.64 (0.05) POXC2 mg kg-1 0-7.5 465.3 (25.1) 505.2 (26.1) Total C % 0-15 1.5 (0.0) 1.9 (0.1)   15-30 1.4 (0.1) 1.6 (0.0) Total N % 0-15 0.2 (0.0) 0.2 (0.0)   15-30 0.1 (0.0) 0.1 (0.0) 1 These properties were determined by Fausak (2019) in May 2017, when the field experiment was established (n = 12).  2 POXC: permanganate oxidizable carbon   61   Figure 3.1 Soil NH4+-N (mg N kg-1) at 0-15 cm and 15-30 cm depths for the treatments at the productive and unproductive fields, from late May to October 2018. The vertical lines indicate the time of planting (day 0) and harvest (day 109). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05).    Figure 3.2 Soil NO3--N (mg N kg-1) at depths of 0-15 cm and 15-30 cm for the treatments at both the productive and unproductive fields, from late May to October 2018. The vertical lines indicate the time of planting (day 0) and harvest (day 109). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05).     62  Table 3.2 Total soil C and N concentration (%), as well as permanganate oxidizable carbon (POXC), measured at harvest at the productive and unproductive field. Error bars represent the standard error of the mean (n = 3). For total C at the 15-30 cm depth of the productive field, different letters indicate statistically significant differences with planting treatment (α = 0.05). There were no other significant differences with the N fertilizer or planting treatments for total soil C or N at either of the other depths or fields (α = 0.05). For POXC, at the unproductive field, different letters indicate significant differences between planting treatments while there were no significant differences with N fertilizer rate (α = 0.05). Letters cannot be compared between depths or fields. Significant treatment effect is indicated by an asterisk (α = 0.05). Field Planting Date N Fertilizer Rate Total C (%) Total N (%) POXC (mg kg-1) 0-15 cm 15-30 cm 0-15 cm 15-30 cm 0-7.5 cm Productive Typical Control 1.5 (0.06) 1.3 (0.10)   a 0.1 (0.01) 0.1 (0.01) 429 (54)   Moderate 1.6 (0.08) 1.3 (0.05)   a 0.2 (0.01) 0.1 (0.00) 399 (103)   High 1.6 (0.14) 1.0 (0.23)   a 0.2 (0.01) 0.1 (0.02) 516 (54)  Late Control 1.5 (0.04) 1.5 (0.12)   b 0.2 (0.01) 0.1 (0.01) 537 (63)   Moderate 1.6 (0.01) 1.5 (0.05)   b 0.2 (0.00) 0.1 (0.01) 539 (67)   High 1.5 (0.10) 1.4 (0.10)   b 0.2 (0.01) 0.1 (0.01) 505 (92) Unproductive Typical Control 1.8 (0.07) 1.6 (0.11) 0.2 (0.01) 0.1 (0.01) 525 (34)   a   Moderate 2.0 (0.12) 1.8 (0.12) 0.2 (0.01) 0.2 (0.01) 518 (15)   a   High 1.9 (0.14) 1.8 (0.17) 0.2 (0.01) 0.2 (0.01) 586 (29)   a  Late Control 1.8 (0.08) 1.5 (0.18) 0.2 (0.00) 0.1 (0.01) 633 (61)   b   Moderate 1.9 (0.08) 1.7 (0.10) 0.2 (0.01) 0.1 (0.01) 619 (19)   b   High 1.8 (0.15) 1.9 (0.13) 0.2 (0.01) 0.2 (0.01) 748 (64)   b  Field Source of Variation df F-value Productive N Fertilizer Rate (N) 2 0.17    0.04*   0.01* < 0.01* 0.30  Planting Date (PD) 1 0.89 < 0.01* 0.83  0.26   4.21*  N × PD 2 0.36  0.47 0.53  0.69 2.77 Unproductive N Fertilizer Rate (N) 2 0.33  0.10 0.23  0.10 2.83  Planting Date (PD) 1 0.37  0.63   0.01*  0.69   9.49*  N × PD 2 0.53  0.44 0.06  0.45 0.09 63    Figure 3.3 Dry weight of aboveground plant biomass at mid-season and vine-killing, and tuber biomass at harvest from the productive and unproductive fields. Error bars represent the standard error of the mean (n = 3). Different lowercase letters indicate a significant difference between N fertilizer × planting date (α = 0.05). Different capital letters indicate a significant difference between N fertilizer treatments when planting date did not have a significant effect (α = 0.05). There were no instances were planting date solely resulted in a significant difference between treatments. Letters cannot be compared between the productive and unproductive field.  64  Table 3.3 Total C and N concentration (%) in the aboveground biomass at mid-season and at vine-killing, and potato tubers at harvest from the productive and unproductive fields. The standard error of the mean is shown in brackets (n = 3). Different lowercase letters indicate a significant difference between N fertilizer × planting date treatments (α = 0.05). Different capital letters indicate a significant difference when the interaction between N fertilizer × planting date was not significant. Total C at tuber harvest differed with planting date while total N at vine-killing differed with N fertilizer treatments (α = 0.05). Letters cannot be compared between the productive and unproductive field. Significant treatment effects are indicated in bold (α = 0.05). Field Planting Date N Fertilizer  Rate Total C (%) Total N (%) Aboveground Biomass Mid-Season Aboveground Biomass Vine-Killing Tuber Harvest Aboveground Biomass  Mid-Season Aboveground Biomass  Vine-Killing Tuber Harvest Productive Typical Control 40.0 (0.4)  38.0 (0.5)    a 39.0 (0.4)   A 3.5 (0.2)   a 2.6 (0.1) 1.3 (0.1)   Moderate 38.9 (1.4) 37.3 (1.1)   ab 39.4 (0.5)   A 5.0 (0.1) ab 2.9 (0.3) 1.4 (0.0)   High 38.0 (0.6) 36.5 (0.6)   ab 39.3 (0.7)   A 4.7 (0.2)  b 2.5 (0.2) 1.3 (0.0)  Late Control 37.6 (2.7) 29.0 (1.8)    b 40.1 (0.1)   B 3.3 (0.8)  a 2.2 (0.1) 1.3 (0.1)   Moderate 39.4 (0.4) 33.2 (1.1)   ab 39.6 (0.3)   B 4.7 (0.1) ab 2.7 (0.1) 1.6 (0.0)   High 37.3 (1.7) 37.1 (3.9)   ab 40.4 (0.4)   B 4.2 (0.8)  b 3.6 (0.7) 1.6 (0.1) Unproductive Typical Control 38.0 (0.5) 34.4 (0.2)   bc 39.5 (0.3) 3.2 (0.2)  A 2.3 (0.1) 1.3 (0.1)   Moderate 39.4 (0.2) 35.3 (0.6)   bc 39.7 (0.3) 4.8 (0.4)  B 2.6 (0.1) 1.3 (0.1)   High 37.3 (1.1) 35.0 (0.5)    c 39.9 (0.1) 5.3 (0.3)  B 2.3 (0.1) 1.4 (0.3)  Late Control 38.1 (1.4) 33.6 (0.6) abc 39.8 (0.1) 3.5 (0.1)  A 2.1 (0.1) 1.2 (0.1)   Moderate 38.4 (1.2) 30.2 (0.3)    a 40.1 (0.3) 4.6 (0.3)  B 2.3 (0.2) 1.5 (0.1)   High 38.3 (0.4) 31.5 (1.0)  ab 40.0 (0.1) 5.0 (0.3)  B 2.4 (0.2) 1.5 (0.1)  Field Source of Variation df   F-value   Productive N Fertilizer Rate (N) 2 1.76 0.53 1.48 1.57 17.81 14.31  Planting Date (PD) 1 0.91 24.53 10.54 3.10 0.46 7.99  N × PD 2 1.71 12.81 4.52   13.69 0.61 5.75   Unproductive N Fertilizer Rate (N) 2 2.44 1.52 2.26 76.37 4.45 3.49  Planting Date (PD) 1 0.01 2.35 3.73 0.02 1.37 0.14  N × PD 2 1.72 32.28 0.75 3.30 4.78 3.42 65  Table 3.4 Total C and N content (kg ha-1) of aboveground biomass at mid-season and vine-killing, and potato tubers at harvest from the productive and unproductive fields. The standard error of the mean is in brackets (n = 3). Different lowercase letters indicate a significant difference between N fertilizer rate × planting date treatments (α = 0.05). Different capital letters indicate a significant difference between N fertilizer rate or planting date treatments when the N fertilizer rate × planting date interaction was not significant. Letters cannot be compared between the productive and unproductive fields. Significant treatment effects are indicated in bold (α = 0.05). Field Planting Date N Fertilizer  Rate Total C (kg ha-1) Total N (kg ha-1) Aboveground Biomass Mid-Season Aboveground Biomass Vine-Killing Tuber Harvest Aboveground Biomass  Mid-Season Aboveground Biomass  Vine-Killing Tuber Harvest Productive Typical Control 173.6 (62.58) A 605.5 (48.05) 1250.5 (246.81) A 14.7 (4.92) a 41.1 (4.69) A 40.5 (10.23) A   Moderate 606.2 (137.79) B 950.4 (66.06) 1978.6 (113.50) AB 77.3 (16.61) b 72.9 (7.25) B 71.0 (1.70) B   High 602.9 (167.15) B 1014.7 (90.65) 2802.3 (193.48) B 75.1 (20.90) b 70.3 (10.03) B 94.8 (5.27) B  Late Control 108.8 (14.74) C 525.4 (94.38) 1773.5 (363.32) A 9.3 (2.41) a 39.0 (7.22) A 56.3 (13.35) A   Moderate 213.5 (32.22) D 981.2 (146.11) 2530.0 (361.16) AB 25.6 (3.55) ab 79.5 (12.81) B 103.9 (13.56) B    High 180.9 (15.18) D 1157.5 (411.25) 2674.3 (534.63) B 19.5 (1.96) a 101.8 (31.25) B 100.8 (14.74) B Unproductive Typical Control 190.8 (58.38) 689.8 (54.01) A 1594.9 (394.36) 15.8 (4.37) A 46.1 (4.06) 53.9 (12.22)   Moderate 500.4 (191.36) 910.0 (25.48) A 1773.0 (45.45) 63.3 (25.47) A 67.4 (1.24) 60.0 (2.47)   High 454.2 (124.20) 1040.3 (54.35) A 1923.9 (404.15) 64.8 (19.24) A 67.4 (3.27) 64.2 (7.05)  Late Control 131.4 (15.91) 935.8 (246.28) B 1204.6 (220.11) 12.0 (1.59) B 59.5 (17.33) 36.3 (5.41)   Moderate 195.6 (34.73) 894.1 (153.79) B 1665.9 (312.49) 23.2 (3.37) B 71.3 (19.81) 60.8 (11.59)   High 194.6 (4.24) 1360.0 (489.48) B 1589.6 (196.71) 25.3 (1.70) B 101.7 (37.00) 58.6 (7.09)  Field Source of Variation df   F-value   Productive N Fertilizer Rate (N) 2 31.67 11.73 18.73 76.13 22.64 30.27  Planting Date (PD) 1 27.38 0.01 1.83 3.55 0.00 5.58  N × PD 2 5.90 0.27 2.02   7.97 0.54 2.49 Unproductive N Fertilizer Rate (N) 2 4.28 4.89 1.96 13.63 4.46 6.03  Planting Date (PD) 1 1.31 7.57 5.68 9.37 0.67 4.12  N × PD 2 0.85 1.14 1.01 2.93 0.46 5.91 66  Chapter 4: Combined Effect of Temperature and Soil Water Content on GHG Emissions and Nitrogen Transformations in the Short-Term Following Urea Application  4.1 Introduction Soils, as a regulator of greenhouse gas (GHG) emissions, can act as either sinks or sources for GHGs (Oertel et al. 2016). Numerous field and incubation studies have been conducted to evaluate GHG (CO2, N2O, and CH4) emissions from arable land (Bodelier and Laanbroek 2004; Butterbach-Bahl et al. 2013; Oertel et al. 2016). Net GHG emissions are dependent on soil biological activity, which is highly responsive to environmental variables such as soil temperature and soil water content (SWC) as well as nutrient availability (Oertel et al. 2016). Those have all been identified as confounding variables in GHG production and uptake (Schaufler et al. 2010).  Soil respiration by root and soil organisms collectively accounts for the production of CO2 (Oertel et al. 2016). Soil respiration is limited when volumetric water content (VWC) is less than 20% or when soil is saturated with water (Smith et al. 2003). In between these dry and wet extremes, temperature has the dominant influence on soil respiration. Another control on CO2 emissions are the availability of C and N for microbial decomposition. Methane (CH4) emissions are strongly affected by SWC as CH4 is generally produced under saturated (i.e., anaerobic) conditions. Temperature has a small positive effect on CH4 uptake that is interrelated with SWC as higher temperatures contribute to drier soil conditions (Smith et al. 2003). The addition of N fertilizer can also limit the uptake of CH4 as NH4+ competes with CH4 for the oxidation enzyme, methane monooxygenase (Liu and Greaver 2009). 67  There are some contrasting findings though that support an increase in CH4 production following increased N availability (Bodelier and Laanbroek 2004). Globally, the use of nitrogen (N) fertilizers in crop production is a primary source of N2O emissions (Butterbach-Bahl et al. 2013). Nitrous oxide is the most potent GHG from crop production with a global warming potential (GWP) that is 298-times greater than CO2. The amount of N2O produced following N fertilization is mainly dependent on the type of fertilizer used and soil redox conditions (Smith et al. 2003). One of the most commonly used inorganic N fertilizers is urea, which when applied to soils, hydrolyzes producing NH4+ ions that are taken up by plants or undergo nitrification to form NO3-. While NO3- is another form of plant available N (PAN), it is also susceptible to leaching or denitrification (under anaerobic conditions / high SWC) to form NO, N2O, or N2. The nitrification of NH4+ to NO3- then subsequent denitrification is the primary pathway for N2O production in soils (Butterbach-Bahl et al. 2013). The nitrification and denitrification processes are mediated by the SWC and subsequently the oxygen availability for microbial activity. In soils with low mineral N and high SWC, N2O uptake can occur when N2O is converted to N2 by denitrifying bacteria (Chapuis-Lardy et al. 2007). Strategies for agricultural GHG mitigation highlight the need to improve N fertilizer use efficiency in order to reduce N2O emissions.  In this study, I conducted an incubation experiment investigating the effects of N fertilizer rate (0, 90, and 120 kg N ha-1), SWC (20% and 40% VWC), and temperature (4°C and 20°C) on the production of CO2, N2O and CH4. The primary objective was to assess the effects of SWC and temperature on GHG emissions following N fertilizer application. Additionally, I assessed how PAN, and total soil C and N respond to different combinations of experimental treatments. I hypothesized that: (i) treatments with low SWC and low temperature will limit the 68  GHG emissions; (ii) higher N fertilizer rates will result in greater N2O emissions and total soil N; (iii) the NO3--N will be highest and NH4+-N will be lowest in the high SWC - high temperature treatment; and (iv) total soil C will not be affected by either SWC or temperature.  4.2 Materials and Methods  4.2.1 Experimental Design  In early May 2018, a composite bulk soil of a silt loam texture, with 27% sand, 56% silt, and 16% clay, was collected from the 0-15 cm depth (Appendix F) at an agricultural field in Delta, BC (49° 05′ N, 123° 03′ W) (Fausak 2019). The soil was sieved through a 4-mm sieve and stored at 4°C until the incubation experiment. The incubation experiment was set up as a randomized complete block design with three N fertilizer rates (0, 90, and 120 kg N ha-1), two SWCs (20% and 40% VWC), two air temperatures (4°C and 20°C), and three blocks (each being a repeat of the experiment). The N fertilizer rate of 90 kg N ha-1 (referred to as moderate from this point onward) was chosen based on the recommendation of the British Columbia Ministry of Agriculture for potato production (2012), while 120 kg N ha-1 rate (referred to as high from this point onward) is commonly used by potato producers in the Lower Fraser River Valley (LFRV). The VWC of 20% is representative of regional field conditions as measured in early May 2018, while 40% represents near saturated soil conditions that commonly occur during the rainy season (i.e. October-April) in the LFRV. The 20% and 40% VWCs are equivalent to 31.9% and 64.9% water-filled pore space (WFPS), respectively. The incubation temperatures were chosen to represent the LFRV’s mean winter temperature of 4.1°C and summer temperature of 19.1°C (Environment and Climate Change Canada n.d.).  69   In preparing soil samples for the incubation experiment, 197 g of soil (dry equivalents) was placed in to a 500 mL glass jar and packed to 0.99 g cm-3 bulk density, corresponding to the field bulk density in May 2018. The opening of the jar was covered with damp paper towel and secured with an elastic band to allow air flow and limit evaporation. Each treatment combination had two jars, one for measuring GHG emissions and the other for taking soil samples for PAN analysis, for a total of 24 incubation jars per block. The water content and temperature of the samples were established 24 hours before applying fertilizer to allow the soil to equilibrate and avoid the measurement of a CO2 flush from wetting the soil. The jars were stored in the refrigerator (4°C) or at room temperature (20°C). The soil was incubated for 18 days after the fertilizer treatments were applied. The SWC of the incubation jars was measured and adjusted twice a week, prior to measuring GHG emissions or taking soil samples for analysis. The experiment was repeated three times with start dates of February 6, February 28, and March 25, 2019. 4.2.2 Sampling and Analysis Greenhouse gas emissions  A cavity ring down spectrometer (Model G2308, Picarro Inc., Santa Clara, CA, USA, was used to measure CO2, N2O, and CH4 fluxes. The accumulation of GHGs in the jar headspace was measured over a 1-min enclosure time with a 400 mL min-1 air flow rate using vacuum pump. The R2 values of the linear regressions for the three GHGs were verified to ensure that the 1-min enclosure time was long enough. Between measurements, the tubing and analyzer were flushed with fresh air for at least 2 minutes at a 10 L min-1 flow rate. Flux measurements were taken approximately every 3 days for the duration of the 18-day incubation period. The GHG 70  fluxes were calculated with MATLAB version R2014a (Mathworks 2014) using the following formula: Equation 4.1 𝐹𝐹 = (𝜌𝜌  𝑆𝑆  𝑉𝑉) / 𝑤𝑤 where F is the flux (nmol kg−1 sec−1), ρ is the molar density (mol m-3) of dry air as corrected for the average mixing ratio of water, S is the slope of the accumulation of the GHG mixing ratios in the headspace of the collar as determined by a linear regression (nmol mol−1 sec−1), V is the volume of the headspace (m3), and w is the dry weight of the soil (kg). Total emissions were calculated using piece-wise linear interpolation between sampling dates. The GWPs of 298 for N2O and 34 for CH4 on a 100-year horizon were used to convert emissions to CO2 equivalents (Myhre et al. 2013).  Plant available N  The analysis of PAN, included the determination of NH4+-N (Weatherburn 1967) and NO3--N (Doane and Horwáth 2003), and was done on 5 g of soil that was sampled on the same days as the GHG measurements. The samples were stored at 4ºC until extraction with 25 mL of 2 M KCl that was done within one day of sampling. During extraction, the samples were shaken on a reciprocal shaker for 20 minutes, then centrifuged at 2000 rpm for 5 minutes, filtered through a Fisherbrand Q2 filter paper, and analyzed colorimetrically using a 96-well microplate absorbance reader (Biorad iMark, Hercules, CA, USA).  Total soil C and N  Prior to establishing the incubation experiment, a sample of the soil was taken to determine total C and N. Soil samples for total soil C and N were also taken on the last day of the incubation period by destructively sampling the soil from each of the incubation jars. The soil 71  samples were air-dried, ground with a rolling pin, passed through a 2mm sieve, then ball-mill ground for analysis of total C and N using mid-infrared FTIR spectroscopy (Reeves III. et al. 2002). For each of the ball-mill ground samples, three replicates of 0.025g were tested 60 times for spectral reflectance across a range of wavelengths from 400 to 4000 cm-1 with a 2 cm-1 resolution using the Tensor 37 HTS-XT spectrophotometer (Bruker Optics Gmbh, Ettlingen, Germany). We also analyzed 25% of the prepared samples for total C and N with a Vario EL Cube Elemental Analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany). The data from the elemental analyzer were used to calibrate and validate the FTIR data in a Partial least square regression model (Paul et al. 2020).  4.2.3 Statistical Analysis  The differences in GHG emissions, PAN, and total soil C and N between the treatments were analyzed using ANOVA and Tukey post-hoc tests. I used a linear mixed-effects (LME) model which included fertilizer, SWC, and temperature as well as the two-way and three-way interactions between the factors as fixed effects, and block as a random effect. Type III ANOVA and interaction plots were used to evaluate the significance of interaction terms in the model. Tukey multiple comparison procedure was conducted to test for differences the N fertilizer rate, SWC, and temperature treatment combinations.  The normality and variance homogeneity assumptions of ANOVA were confirmed using Shapiro-Wilks test and Levene’s test, respectively, in addition to diagnostic plots. I used data transformations, when necessary, to ensure the ANOVA conditions were met. A statistical significance threshold of α = 0.05 was used for hypothesis testing. All of the statistical analyses were conducted using R software version 3.6.2 (R Core Team 2019), unless otherwise noted.  72  4.3 Results Greenhouse gas emissions  There were no significant differences in daily CO2 flux across the treatment combinations (Figure 4.1); and a slight decrease in CO2 flux was observed in all treatments as the incubation experiment progressed. Similar to daily CO2 fluxes, total CO2 emissions were also not significantly affected by N fertilizer rate, temperature, and SWC (Figure 4.2; 5.3Appendix G    Daily N2O fluxes were significantly different on the 3rd, 11th, 14th, and 18th days of the experiment for treatment combinations with high temperature and high SWC (Figure 4.3). No significant differences in daily N2O flux were observed for the other temperature and SWC treatments. These interactions between N fertilizer rate, temperature, and SWC led to significant differences in total N2O emissions across the treatments (   73  Figure 4.4). Under conditions of low SWC and low temperature, near-zero N2O fluxes were measured throughout the 18-day incubation period. The treatment combination of high SWC and high temperature stimulated either N2O production or uptake, depending on the amount of N fertilizer applied. Total N2O emissions were the greatest for the high (120 kg N ha-1) fertilizer rate and these emissions were significantly greater than the control and moderate N fertilizer rates.   Across all treatments, there were no significant differences in the daily CH4 fluxes as analyzed by sampling date (Figure 4.5). More consistent positive fluxes were observed at the low temperature in comparison to the negative fluxes (CH4 uptake) at the high temperature. Consequently, near-zero and positive total CH4 emissions were measured in the low temperature treatment, regardless of SWC (Figure 4.6). The moderate and high N fertilizer rates under the high temperature – high SWC treatment led to negative total CH4 emissions that were significantly lower than those under the low temperature – high SWC. There was no clear trend in the effect of N fertilizer rate on CH4 emissions.  Total GHG emissions in units of CO2 equivalents were primarily comprised of CO2 emissions (Figure 4.7). There were minimal contributions of N2O and CH4 emissions relative to CO2 in terms of CO2 equivalents. There were no significant differences in CO2 equivalents among treatments.  Plant available N  Both NH4+-N and NO3--N were significantly affected by N fertilizer rate, SWC, and temperature over the duration of the incubation experiment (Figure 4.8). For each of the sampling dates, significant differences were observed in NH4+-N across all treatment combinations. The NH4+-N consistently remained near zero for the control fertilizer rate. For the low temperature - 74  low SWC with moderate and high N fertilizer rates, NH4+-N content continuously increased with time. For low temperature - high SWC with moderate and high N fertilizer, the NH4+-N increased from the time of fertilization (day 0) until day 7 before decreasing for the remainder of the incubation. The high temperature - high SWC with moderate and high N fertilizer had peak NH4+-N at day 3 then it decreased rapidly. After 18-days, the high temperature - high SWC had the lowest NH4+-N concentration in comparison to the other treatments.  At a low temperature, NO3--N was near zero regardless of SWC and no significant differences were observed between the N fertilizer treatments on a given day (Figure 4.9). There were significant differences in NO3--N among the high temperature treatment combinations after day 3 of the incubation. A more significant increase in NO3--N for the moderate and high N fertilizer rates was observed in the high SWC compared to the low SWC. On day 18 of the incubation, the high N, high temperature, and high SWC treatment had over 2-times the amount of NO3--N than the high N - high temperature - low SWC treatment, which reflects the relative change in their NH4+-N contents at this same point in time. Total soil C and N  Limited changes in total soil C concentration were observed in response to the treatments. A significant difference was observed between the N fertilizer control and high N rate treatment under conditions of low temperature and high SWC where total soil C increased by 9.3% (Table 4.1Error! Reference source not found.). Comparing the control and high N fertilizer rates across the other treatments combinations shows that total soil C decreased slightly, but the changes were not significantly different.  The addition of N fertilizer at both moderate and high N rates resulted in a significant increase in total soil N concentration compared to the N fertilizer control, regardless of 75  temperature and SWC (Table 4.1Error! Reference source not found.). The moderate and high N fertilizer rate treatments were not significantly different from one another across all temperature and SWC treatment combinations.  4.4 Discussion Emissions of the three GHGs responded differently to the experimental treatments. The CO2 emissions made up the majority of total emissions in terms CO2 equivalents across all treatments. There was no significant effect of temperature or SWC on CO2 production. I expected to see an increase in CO2 production with temperature as established in the literature (Karhu et al. 2014; Oertel et al. 2016). Additionally, the SWCs in this experiment were likely not high enough to limit oxygen availability for decomposition processes and consequently CO2 production. An incubation study by Ruser et al. (2006) had similar findings where SWC did not have a significant effect on CO2 emissions, with the exception of WFPS at 98%. The soil respiration is limited when VWC is less than 20% (Smith et al. 2003), which is drier than experimental treatments in my study. Since the soil was homogenized during sample preparation (i.e., sieving) for incubation, that could have contributed to the lack of treatments effect. Sample preparation led to destruction of large aggregates. In addition, as noted by Schaufler et al. (2010), there was likely better oxygen availability in incubation jars compared to field conditions. The aerobic conditions would have enhanced microbial respiration, while destruction of aggregates may have exposed previously protected soil organic matter to microbial decomposition. The induced aeration and soil organic matter decomposition that occurred during the soil preparation likely overshadowed the treatment effects. The lack of significant difference in total CO2 emissions could also be related to the lack of treatment effect on total soil C. 76  The greatest total N2O emissions were observed in the treatments with N fertilizer additions and high temperature - high SWC conditions, which is agreement with other soil incubation studies (Dobbie and Smith 2001; Schaufler et al. 2010; Lai et al. 2019). The increase in daily N2O flux for this treatment combination reflects the decrease in NH4+-N and increase in NO3--N, indicating that N2O emissions were likely attributed to nitrification. Peak N2O emissions from arable soil have generally been measured in incubation experiments at temperatures above 18ºC and WFPS greater than 60% (Dobbie and Smith 2001; Schaufler et al. 2010). The near-zero N2O emissions in response to the low temperature and both SWCs were also in agreement with similar incubation experiments (Dobbie and Smith 2001; Schaufler et al. 2010). It was interesting to find N2O uptake in the treatment with 0 kg ha-1 N fertilizer - high temperature - high SWC, which agrees with research summarized by Chapuis-Lardy et al. (2007). It is likely that the negative N2O fluxes were stimulated by limited mineral N and low oxygen availability due to high SWC. An incubation experiment carried out by Schindlbacher et al. (2004) on forest soils, found that soil temperature and water account for up to 86% of variability in N2O emissions, emphasizing their importance on the production of N2O.   There was a definitive effect of temperature on whether the soil acted as a CH4 source or sink. At low temperatures, total CH4 emissions were observed, while at high temperatures there was a net uptake of CH4. The 18-day incubation period was likely not long enough to detect the effects of SWC on CH4 emissions as CH4 production occurs after any available NO3- is reduced under low redox conditions (Smith et al. 2003). Increasing N fertilizer rates led to more CH4 uptake under the high temperature - high SWC treatment, though the difference was not statistically significant. The addition of inorganic N fertilizers typically reduces CH4 uptake as 77  supported by previous research (Bodelier and Laanbroek 2004), yet conflicting findings offer evidence of N fertilizers having a positive effect on CH4 uptake by the soil.    The NH4+-N and NO3--N measurements provided insight into the N dynamics that occurred following N fertilizer application. At day 0, I observed significant differences in NH4+-N between the N fertilizer treatments, but not NO3--N, indicating that the urea had being hydrolyzed but the accumulation of NO3--N via nitrification was not yet observable. Over the remainder of the incubation period, there has been a strong interaction between temperature and SWC that influenced the transformation of NH4+-N to NO3--N. At the high temperature treatment, I observed an accumulation of NO3--N across both SWCs that corresponded to a decrease in NH4+-N. At low temperatures, there was minimal NO3--N accumulation. At the end of the incubation, the total soil N when N fertilizer was applied was significantly higher relative to the control. There were no significant differences between the moderate and high fertilizer treatments, which may have been due to the complexity of the N transformations. A soil incubation experiment by Kowalenko and Cameron (1976) also observed minimal NO3--N accumulation at a temperature of 4ºC, regardless of SWC. At 25ºC, they observed peak levels of NO3--N across their SWC treatments (Kowalenko and Cameron 1976). Decreases in NH4+ that are not observed through the transformation to NO3- and N2O are likely due to NH3 volatilization or NO production, which I was unable to measure in this experiment. While an incubation study allows for the controlled investigation of treatment effects on GHG emissions as well as C and N cycling, such studies have limitations. At the field scale, the C and N cycle processes are much more dynamic with large spatial variability and there are additional considerations such as nutrient leaching. The short duration of the incubation experiment is another important consideration when assessing these findings as the strength of 78  the treatment effects may change with time. Lastly, the use of intact soil cores would improve the incubation experiment as it would more accurately represent field soil conditions as the soil structure would not be destroyed.   4.5 Conclusions  The 18-day incubation of soil under different N fertilizer rates, temperatures, and SWC had significant effects on N2O emissions, PAN, and total soil N. The total N2O emissions, while small when measured over only 18 days, showed a strong treatment response. The N2O emissions from the high fertilizer - high temperature - high SWC treatment were significantly greater than all of the other treatments. The interaction of temperature and SWC on nitrification was observed as there was limited conversion of NH4+-N to NO3--N under low temperature conditions. There were no treatment effects on CO2 emissions, CH4 emissions, and total soil C concentration. The results from this study provide insight into how environmental variables affect agricultural soils in the LFRV. The proper rate and timing of N fertilizer with consideration of temperature and SWC conditions are crucial in mitigating N2O emissions from agricultural fields. Further research focusing on determining the threshold of the temperature and SWC that lead to GHG emissions are needed to develop more efficient GHG mitigation practices. Additionally, the study of a wider range of soil types from different locations within a region would provide a more comprehensive assessment of the factors that most influentially govern GHG emissions.  79   Figure 4.1 Daily CO2 flux measured over the duration of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, no significant differences between treatments were observed (α = 0.05).   Figure 4.2 Total CO2 emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). There were no significant differences across treatments (α = 0.05). 80   Figure 4.3 Daily N2O flux measured over the duration of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, an asterisk denotes that there is a significant difference between N fertilizer rate × soil water content (SWC) × temperature treatments (α = 0.05).   Figure 4.4 Total N2O emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). Different letters indicate significant differences between treatments (α = 0.05). 81   Figure 4.5 Daily CH4 flux measured over the duration of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, no significant differences between treatments were observed (α = 0.05).   Figure 4.6 Total CH4 emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). Different letters indicate significant differences between treatments (α = 0.05). 82   Figure 4.7 Total CO2 equivalent emissions from the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). There were no significant differences across treatments (α = 0.05).   Figure 4.8 Measurements of NH4+-N over the duration of the incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05). 83   Figure 4.9 Measurements of NO3- - N over the duration of the incubation experiment. Error bars represent the standard error of the mean (n = 3). For each day, significant differences between treatments is denoted by an asterisk (α = 0.05).                        84  Table 4.1 Total soil C and N concentrations measured at the end of the 18-day incubation experiment. Error bars represent the standard error of the mean (n = 3). Different letters indicate a significant difference between treatments (α = 0.05). Temperature Soil Water Content N Fertilizer Rate Total Soil C  Total Soil N    g kg-1 4ºC 20% Control 17.26 ± 0.38 ab 1.61 ± 0.02 a   Moderate1 18.07 ± 0.26 abc 1.81 ± 0.02 c   High 17.61 ± 0.33 abc 1.80 ± 0.01 bc  40% Control 16.98 ± 0.37 a 1.58 ± 0.02 a   Moderate 18.41 ± 0.12 abc 1.84 ± 0.06 c   High 19.27 ± 0.51 c 1.95 ± 0.04 c 20ºC 20% Control 17.51 ± 0.64 abc 1.62 ± 0.04 ab   Moderate 18.74 ± 0.27 abc 1.86 ± 0.04 c   High 19.08 ± 0.46 bc 1.87 ± 0.06 c  40% Control 17.15 ± 0.33 ab 1.60 ± 0.03 a   Moderate 18.37 ± 0.38 abc 1.83 ± 0.03 c   High 18.52 ± 0.28 abc 1.91 ± 0.03 c 1Moderate refers to 90 kg N ha-1, while high refers to 120 kg N ha-1   85  Chapter 5: Conclusions, Management Implications, and Future Research Recommendations  5.1 General Conclusions Potato production in the Lower Fraser River Valley (LFRV) often utilizes high N fertilizer rates and frequent tillage operations to maximize crop yields. Soil workability is also a limiting factor to production in this region as poor soil drainage and abundant precipitation in the spring and fall increase the risk of compaction from heavy machinery used for planting and harvest. To improve our understanding of agricultural GHG emissions in the LFRV, a field experiment and a soil incubation experiment were conducted to determine the effects of N fertilizer rates and delayed planting on GHG emissions and potato production. Over the 2018 growing season, at productive and unproductive experimental field sites in Delta, BC there were no differences in total CO2, N2O, and CH4 emissions with increasing N fertilizer rate up to 120 kg ha-1 or delayed planting time. The productive field had a peak in N2O emissions in November for the moderate and high N fertilizer rates due to the rainy season and presence of residual soil NH4+ and NO3- that were measured at harvest. Residual N increases the risk of environmental pollution through losses to groundwater by leaching or to the atmosphere as N2O. Nitrogen fertilizer rate had a significant effect on PAN as the moderate and high N fertilizer treatments had higher PAN following fertilizer application, especially at the 0-15 cm depth. Planting date had a significant effect on PAN as well, but this treatment effect was most often observed at the 15-30 cm depth, later on in the growing season. There were no changes in total soil C or N concentration with either N fertilizer rate or planting date, which is to be expected since the experiment was only implemented for one growing season. There was a 86  response in POXC to the planting date treatment at the unproductive field as plant residues left on the soil surface during vine-killing and an increase in precipitation likely stimulated decomposition activity.  As expected, yield increased with N fertilizer rate at the productive field. The yield at the unproductive field did not respond to the amount of N fertilizer applied, which revealed the impact that soil degradation can have on crop production in the LFRV. There were no differences in yield with planting date. The cost-benefit analysis illustrated that the moderate and high N fertilizer rates had similar agronomic efficiency and cost effectiveness for both fields. At the mid-point of the growing season there was greater mean aboveground plant biomass for the moderate and high N fertilizer treatments with typical planting, compared to the late planting but the difference was not statistically significant. These differences in biomass were not observed at the time of vine-killing though. The effect of N fertilizer rate and planting date on total C and N concentration of the plant biomass was also inconsistent over the course of the growing season. The total N concentration and content of plant biomass was greater in the moderate and high N fertilizer treatments compared to the control at mid-season. At the productive field, the total N content of plant biomass in the moderate and high N fertilizer treatments still were similar at vine-killing and harvest. To supplement the field experiment, an 18-day soil incubation experiment was conducted to investigate how N fertilizer rate, temperature, and SWC interact and influence GHG emissions in the region in the short-term following N fertilizer application. The incubation experiment found that N2O emissions, PAN, and total soil N were significantly affected by N fertilizer rate, temperature, and SWC. There was a strong response in total N2O emissions to the N fertilizer rate, temperature, and SWC treatments as the combination of N availability, high temperature, 87  and high SWC promoted the production of N2O. The nitrification process was inhibited under low temperature conditions as limited NH4+ was converted to NO3-. The N fertilizer rate, temperature, and SWC treatments did not affect CO2 emissions, CH4 emissions, or total soil C concentration.   5.2 Study Limitations  The field experiment was limited by only one growing season of data collection but was supplemented by the incubation experiment to further investigate the effects of SWC and temperature on GHG emissions and N transformations associated with inorganic N fertilization. The 20ºC temperature treatment in the incubation experiment is not comparable to the mean temperature in November of 8ºC when N2O emissions were observed in the field study. While N2O production was near zero at 4ºC in the incubation, there is a point between 4ºC and 20ºC where N2O is produced that I was not able to detect.  The cost effectiveness parameter used in the field experiment does not include a number of other crop production costs such as labour, pesticides, and machinery operation. A cost-benefit analysis that includes these costs would provide a more accurate assessment of how N fertilizer rate and planting date influence the profitability and environmental sustainability of potato production in the LFRV.  5.3 Management Implications and Recommendations for Further Research The quantification of GHG emissions in this study improves our understanding of how potato production in the LFRV contributes to agricultural GHG emissions. During the growing season, there were no observable impacts of N fertilizer and planting date treatments on GHG 88  emissions, but an important finding is that the treatment effect of N fertilizer rate on N2O became apparent in November, with the onset of precipitation. Further research on how residual soil N and increased precipitation outside of the growing season influence GHG emissions is needed.   To improve N use efficiency beyond application rate, the use of alternative N fertilizer sources such as polymer coated urea, nitrification inhibitors, or urease inhibitors would have delayed the peak PAN and lowered N2O emissions induced by N fertilizer application. The timing of N fertilizer application is another component of improved N fertilizer use efficiency. Splitting the application over the growing season to optimize N availability when the plants require it could reduce excess N in the soil. Further research on the use of different types of N fertilizer and application timing for potato production in the LFRV would be beneficial. To more realistically understand the effects of the N fertilizer and planting date treatments, a longer duration experiment would need to be carried out.  The findings from the incubation experiment emphasize the importance of environmental conditions when assessing the timing of N fertilizer application in order to mitigate N2O emissions and improve N fertilizer-use efficiency. The GHG emissions and PAN results show that temperature and SWC have a combined effect on the transformation processes of N. An opportunity for further research would be to assess the response of GHG emissions and soil N dynamics to a more extensive range of temperatures and SWCs, freeze-thaw cycles as experienced in the winter, and varying degrees of soil salinity to more accurately represent fields in the LFRV.  The GHG data collected in this study is an important contribution the regional quantification of agricultural GHG emissions. While I did not see any treatment effects on GHG emissions during the growing season, this research highlights the importance of measuring 89  emissions produced outside of the growing season. Additionally, the findings of this study emphasize the opportunity for improved N fertilizer management in potato production within the LFRV. There were no significant differences in yield, plant total N content, or cost effectiveness between the 90 and 120 kg N ha-1 treatments at the productive field, which suggests that the 120 kg N ha-1 fertilizer rate may be excessive and contribute to N losses to the environment. Continued research is required to determine if 120 kg N ha-1 for potato production is excessive compared to 90 kg N ha-1 at other productive fields in the region and if this is consistent over different years. This study found that the greatest opportunity for improved N fertilizer management is at unproductive fields, where the application of N fertilizer did not improve yields relative to the control. At the unproductive field, high soil salinity and poor drainage were limiting factors for crop growth as the application of N fertilizer did not enhance yields. Based on this study, I recommend that N fertilizer rates be restricted at fields with soil degradation issues in order to lower potential N2O emissions and residual soil N. Further research on these degraded soils in the LFRV is needed to provide better understanding of how the existing soil conditions in combination with management practices influence crop yield and GHG emissions. Additionally, there were greater amounts of residual soil N after harvest where fertilizer was applied, and this was more pronounced at the unproductive field. Residual soil N can further contribute to the production of N2O outside of the growing season or be lost to groundwater through leaching. Crop management strategies that optimize yields while minimizing agricultural GHG emissions are necessary to mitigate climate change and promote agricultural sustainability. In the LFRV, limiting N fertilization at unproductive fields and further investigation of lowering N fertilizer rates at productive fields are tangible strategies to achieve this balance. As precipitation 90  patterns are expected to become even more variable, impacting the timing of potato planting and harvest and emphasizing the need to adapt current management practices. The research conducted for this study supports a delayed planting time, to avoid poor soil workability and risk of compaction in the spring. The delayed planting time will most likely not impede crop yields as long as the crop is harvested before fall precipitation, which once again reduces soil workability. Modifying crop management strategies to address current and anticipated potato production issues will support regional soil conservation and promote the continued agricultural capability of the LFRV. 91  Bibliography  Adviento-Borbe, M.A.A., Doran, J.W., Drijber, R.A., and Dobermann, A. 2006. 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Potato Res. 84: 3–18. doi:10.1007/BF02986294. 101  Appendices  Appendix A  Locations of the Productive and Unproductive Fields in Delta, BC    102  Appendix B  The 2018 Monthly Averaged Maximum, Mean, and Minimum Temperatures (ºC) as well as Mean Precipitation (mm) that were Measured at the Vancouver International Airport  Source: Environment and Climate Change Canada (n.d.)           103  Appendix C  Experimental Field Maps with the Nitrogen Fertilizer Rate and Planting Date Treatments for the Productive and Unproductive Fields  C.1 Productive Field   C.2 Unproductive Field  104  Appendix D  Permanganate Oxidizable Carbon Analysis Procedure  A stock solution of 0.2 M KMnO4 was prepared in a solution of CaCl2 at a pH 7.2. The soil was air-dried, ground with a wooden rolling pin, and passed through a 2mm sieve. Samples of 2.5 g were measured into 50mL falcon tubes then 2 mL of the stock KMnO4 and 18 mL of deionized water were added to each tube. The tubes were shaken on an oscillating shaker for 2 minutes at 240 oscillations per minute. To allow for the soil to settle, the tubes were placed in a dark room for 10 minutes. The supernatant was diluted by adding 0.5 mL of it to new falcon tubes with 49.5 mL of deionized water. The diluted sample solution was inverted 3-times to mix then three replicates of 250 μL were pipetted into 96-well microplate. A standard curve was prepared with a blank of deionized water, and four standard KMnO4 solutions (0.005 M, 0.01 M, 0.015 M, and 0.02 M) and added to each microplate. A microplate spectrophotometer (TECAN Group Ltd, Zurich, Switzerland) was used to measure absorbance at 550 nm. The absorbance values for the deionized water blanks were subtracted from all other absorbance values. The following equation was used to calculate POXC:  𝑃𝑃𝐶𝐶𝑃𝑃𝐶𝐶 = [0.02 𝑚𝑚𝑚𝑚𝑚𝑚/𝐿𝐿 − (𝐵𝐵0 + 𝐵𝐵1 × 𝐴𝐴𝐴𝐴𝐴𝐴)] × (9000 𝑚𝑚𝑚𝑚 𝐶𝐶/𝑚𝑚𝑚𝑚𝑚𝑚) × 0.02 𝐿𝐿 / 𝑊𝑊𝑊𝑊  where POXC is permanganate oxidizable carbon (mg kg-1 soil), 0.02 mol/L is the initial solution concentration, B0 is the intercept of the standard curve, B1 is the slope of the standard curve, Abs is the absorbance of the sample, 9000 mg C/mol is the amount of C oxidized by 1 mole of MnO4, 0.02 L is the volume of stock KMnO4 reacted with each sample, and Wt is the weight of air-dried soil sample in kg.105  Appendix E  Plant Available Nitrogen ANOVA Output for the 2018 Field Experiment  F-values are shown in the table and significant treatment effect (α = 0.05) is indicated in bold. Plant Available N Days after Planting Field 0-15 cm Depth 15-30 cm Depth N Fertilizer (F) Planting Date (PD) F x PD N Fertilizer (F) Planting Date (PD) F x PD NH4+-N Pre-planting Productive 16.38 2.59 9.43 2.03 7.43 3.49   Unproductive 10.37 0.57 0.88 0.48 7.58 4.69  0 / 3 Productive 9.21 0.86 8.52 1.17 0.98 0.32   Unproductive 6.91 0.22 3.54 0.67 4.38 0.46  17 / 18 Productive 22.78 8.09 1.13 8.92 1.25 2.29   Unproductive 7.13 0.96 5.91 2.80 0.50 4.83  36 / 45 Productive 36.94 1.15 6.55 3.72 0.27 34.91   Unproductive 43.18 0.07 4.04 6.81 3.33 1.55  63 /72 Productive 55.50 0.21 1.15 10.46 1.68 0.44   Unproductive 47.44 2.04 2.89 14.96 0.09 8.36  90 Productive 340.45 < 0.01 1.17 7.57 0.24 12.46   Unproductive 18.68 0.31 3.88 24.94 4.17 0.35  108 / 110 Productive 60.24 1.56 2.26 14.20 7.32 4.84   Unproductive 65.23 6.26 0.38 16.55 7.71 0.35 NO3--N Pre-planting Productive 2.53 1.26 5.77 4.38 0.68 3.33   Unproductive 19.06 11.88 8.77 5.20 3.70 0.35  0 / 3 Productive 15.21 10.40 6.33 13.48 3.68 2.82   Unproductive 9.31 0.81 3.35 1.56 0.01 2.65  17 / 18 Productive 50.09 7.83 0.38 19.46 6.18 1.03   Unproductive 7.96 9.81 1.30 6.91 16.84 0.40  36 / 45 Productive 131.84 49.00 26.78 75.23 1.28 3.37   Unproductive 46.18 0.07 4.04 8.89 4.13 6.51  63 /72 Productive 75.66 45.17 17.42 14.32 3.47 0.16   Unproductive 84.26 27.93 0.76 10.15 4.34 10.80  90 Productive 29.66 37.29 1.91 8.49 0.09 9.34   Unproductive 3.73 4.74 8.67 19.22 14.78 1.28  108 / 110 Productive 74.84 11.24 9.46 10.82 29.34 1.26   Unproductive 84.92 25.71 2.75 12.38 16.85 3.33 106  Appendix F  Properties of the Soil Used in the Incubation Experiment  The standard error of the mean is shown in brackets (n = 3, except total soil C and total soil N where n = 1). Property Units Value Sand1 % 25 (0.67) Silt1 % 59 (0.67) Clay1 % 16 (0.00) pH (water)  5.9 (0.02) pH (CaCl2)  5.4 (0.17) Electrical Conductivity1 dS m-1 1.19 (0.30) Total soil C g kg-1 16.9 Total soil N g kg-1 1.6 1These properties were determined by Fausak (2019) (n = 12).                107  Appendix G  ANOVA Output for Total GHG Emissions in the Incubation Experiment  F-values are shown in the table and significant treatment effect (α = 0.05) is indicated in bold. Source of Variation df F-value CO2 N2O CH4 CO2e N Fertilizer Rate (N) 2 2.16  0.49 4.83 3.41 Soil Water Content (SWC) 1 4.87 0.02 0.37 6.57 Temperature (T) 1 3.69 0.29 12.11 4.42 N × SWC 2 3.54 0.15 1.86 3.93 N × T 2 1.05 1.38 2.30 1.66 SWC × T 1 6.61 7.05 2.38 8.15 N × SWC × T 2 3.07 42.08 7.99 3.86   

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