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The effects of 2- and 3-year grassland set-asides on plant available nitrogen and greenhouse gas emissions… Fausak, Lewis Karl 2019

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THE EFFECTS OF 2- AND 3-YEAR GRASSLAND SET-ASIDES ON PLANT AVAILABLE NITROGEN AND GREENHOUSE GAS EMISSIONS IN DELTA, BRITISH COLUMBIA by  Lewis Karl Fausak  B.Sc., The University of Alberta, 2013  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)  August 2019  © Lewis Karl Fausak, 2019 ii   The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:  The Effects of 2- and 3-Year Grassland Set-Asides on Plant Available Nitrogen and Greenhouse Gas Emissions Delta, British Columbia  submitted by Lewis Karl Fausak in partial fulfillment of the requirements for the degree of Master of Science in Soil Science  Examining Committee: Dr. Sean Smukler, Land and Food Systems Co-supervisor Dr. Maja Krzic, Faculty Land and Food Systems Co-supervisor  Dr. Art Bomke, Faculty of Land and Foods Systems Supervisory Committee Member    Dr. Cindy E. Prescott, Faculty of Forestry Additional Examiner  Additional Supervisory Committee Members: Dr. Will Valley, Faculty of Land and Food Systems Supervisory Committee Member Mr. Drew Bondar, Delta Farmland and Wildlife Trust Supervisory Committee Member  iii  Abstract  Since 1993, the Grassland Set-Aside (GLSA) Stewardship Program has incentivized farmers in the western Fraser River delta, British Columbia, Canada to plant a grass-legume mixture on active cropland and leave it fallow for 1-4 years to improve soil quality and provide wildlife habitat. Benefits to wildlife are well documented, but not well understood for soil quality. Study objectives were to quantify the effects of 2- and 3-year GLSAs on plant available nitrogen (N), crop production, soil quality, and greenhouse gas emissions. A field experiment was established in 2017 on a productive and unproductive field with fertilizer treatments (0 and 80-kg N ha-1) compared across GLSA treatments (i) AC – GLSA biomass removed and (ii) 2G – 2-year-old GLSA biomass was incorporated, and seeded with beans. In 2018, fertilizer treatments (0 and 100-kg N ha-1) were compared across the same GLSA treatments and (iii) 3G –3-year-old GLSA biomass was incorporated, and seeded with potatoes. Active carbon (POXC) and aggregate stability (MWD) were measured 3 times per growing season, plant available nitrogen (PAN) was sampled every 2 weeks from May-September, and carbon dioxide, methane, and nitrous oxide were measured weekly from May-September and every 3 weeks from October-April.  MWD increased in 2G and 3G in the year of incorporation relative to AC and POXC increased for 3G relative to AC and 2G. Average seasonal PAN did not differ across treatments but was higher earlier in the season for 2G. Bean yields were greater in 2G compared to AC in the productive field, but otherwise crop yields did not respond to GLSA. N content of bean crops did not differ between treatments, was higher for 3G compared to AC in the unproductive field. 2G increased carbon dioxide emissions in 2018, but 3G only increased emissions in the 2018 production season. Nitrous oxide emissions treatments were higher in 2G treatments across all iv  seasons, but lower in 3G treatments in the 2018 production season. Results suggest 2- and 3-year GLSAs do not increase average PAN to subsequent crops, but increase PAN earlier in the season, and increase crop yield and quality depending on subsequent crops.   v  Lay Summary  The Grassland Set-Aside (GLSA) Stewardship Program incentivizes farmers in the western Fraser River delta (WFRD) to take their active cropland out of production for 1-4 years and seed it to a grass-legume mixture. The objectives of my study were to evaluate the effects of these short-term GLSAs on soil quality, nitrogen availability, crop yields and quality, and greenhouse gas emissions in a productive and unproductive field. In 2017, aggregate stability improved due to 2-year GLSAs in the productive field. In 2018, aggregate stability and active carbon increased in both fields due to 3-year GLSAs. No soil nitrogen benefits were found, crop yields increased for the productive field beans in 2-year GLSA, potato N concentration increased in the productive field with GLSAs, and GLSAs increase greenhouse gas emissions. Findings of my study provide useful information for farm management in the WFRD.  vi  Preface  This thesis represents unpublished work which I conducted with the help of undergraduate students and advisors. I was the lead investigator in the studies included in chapters 2, 3, and 4. I was responsible for research objectives and questions, data collection, data analysis, and thesis composition. Early sample collection was led by Dr. Sean Kearney. Laboratory and field assistance for collecting and analyzing samples was provided by Chantel Chizen, Andrea Stevenson, and Daniel Wong. One undergraduate research project completed under the supervision of myself and Dr. Maja Krzic contributed to the research included in Chapter 4, which provided the 2017 aggregate stability data. The UBC Sustainable Agricultural Landscapes (SAL) laboratory coordinators Katie Neufeld and Paula Porto and the UBC Biometeorology and Soil Physics Group provided assistance and support with field and laboratory protocols included in this study.  Dr. Maja Krzic and Dr. Sean Smukler were the supervisory authors on this project and were involved in the design of the project, analysis of the data, and editing of this thesis. The project was completed in collaboration with Dr. Art Bomke, Dr. Will Valley, and Drew Bondar, who helped with experimental design and project development.  I am responsible for all data analysis of Chapter 3, including mean weight diameter, permanganate oxidizable carbon, plant available nitrogen, crop yield, pest damage, crop quality, and greenhouse gas emissions. All data analysis and interpretation is my original work. vii  Table of Contents  Abstract ......................................................................................................................................... iii  Lay Summary .................................................................................................................................v  Preface ........................................................................................................................................... vi  Table of Contents ........................................................................................................................ vii  List of Tables ..................................................................................................................................x  List of Figures ............................................................................................................................... xi  List of Equations ..........................................................................................................................xv  List of Abbreviations ................................................................................................................. xvi  Acknowledgements ................................................................................................................... xvii  Chapter 1: General Introduction .................................................................................................1  1.1 Soil Management Issues in the Lower Fraser River Delta ............................................. 1  1.2 Grassland Set-asides ....................................................................................................... 2  1.2.1 History of Set-aside Programs Around the World .................................................. 2 1.2.2 The Grassland Set-aside Stewardship Program ...................................................... 4 1.2.3 Effects of Grassland Set-asides on Soil Properties ................................................. 6 1.2.3.1 Soil Aggregate Stability ...................................................................................... 6  1.2.3.2 Soil Organic Carbon ........................................................................................... 7  1.2.3.3 Plant Available Nitrogen..................................................................................... 8  1.2.3.4 Greenhouse Gas Emissions ................................................................................. 9  1.3 Study Objectives and Hypotheses ................................................................................. 10  Chapter 2: Materials and Methods ............................................................................................13  2.1 Field Descriptions ......................................................................................................... 13  viii  2.1.1 2017 Field Season ................................................................................................. 14 2.1.2 2018 Field Season ................................................................................................. 15 2.2 Sampling and Analyses ................................................................................................. 20  2.2.1 Soil and GLSA Properties ..................................................................................... 20 2.2.2 Characterization of Field and GLSA Properties ................................................... 21 2.2.3 Crop Yield, Quality, Carbon, and Nitrogen Concentration .................................. 25 2.2.4 Plant Available Nitrogen....................................................................................... 26 2.2.5 Greenhouse Gas Emissions ................................................................................... 27 2.2.6 Wet Soil Aggregate Stability ................................................................................ 29 2.2.7 Permanganate Oxidizable Carbon......................................................................... 30 2.3 Statistical Analysis ........................................................................................................ 31  Chapter 3: Results and Discussion .............................................................................................33  3.1 Soil Aggregate Stability ................................................................................................ 33  3.2 Permanganate Oxidizable Carbon................................................................................. 36  3.3 Plant Available Nitrogen and Residual Soil Nitrogen .................................................. 38  3.4 Crop Yield and Quality ................................................................................................. 46  3.5 Greenhouse Gas Emissions ........................................................................................... 51  3.5.1 Carbon Dioxide ..................................................................................................... 51 3.5.2 Methane................................................................................................................. 53 3.5.3 Nitrous Oxide ........................................................................................................ 54 3.6 Implications for Local Farmers ..................................................................................... 58  Chapter 4: General Conclusions.................................................................................................60  4.1 Conclusions ................................................................................................................... 60  ix  4.2 Strengths and Challenges of Research .......................................................................... 62  4.3 Directions for Future Research ..................................................................................... 63  Bibliography .................................................................................................................................64  Appendices ....................................................................................................................................78  Appendix A ............................................................................................................................... 78  Appendix B ................................................................................................................................79  Appendix C. ...............................................................................................................................80  Appendix D ...............................................................................................................................85  Appendix E ................................................................................................................................87  Appendix F. ...............................................................................................................................88  x  List of Tables  Table 2.1 Average soil chemical and physical properties across all plots (n=12) before establishment of my field experiment in May of 2017 for the productive and unproductive fields. Standard errors are shown in brackets. ......................................................................................... 23 Table 2.2 Average properties of aboveground biomass from 2- and 3-year-old grassland set-asides (GLSA) determined in May of 2017 and 2018 from the productive and unproductive fields (n=6). Standard errors are shown in brackets. .............................................................................. 24 Table 3.1 Analysis of variance results for mean weight diameter (MWD) at post-tillage, mid-season, and post-harvest in 2017 and 2018 for the productive and unproductive fields. Bolded values indicate statistical significance (p < 0.05). ........................................................................ 36 Table 3.2 Analysis of variance for permanganate oxidizable carbon (POXC) at post-tillage, mid-season, and post-harvest in 2017 and 2018 for the productive and unproductive fields. Bolded values indicate statistical significance (p < 0.05). ........................................................................ 38 Table 3.3 Analysis of variance of bean (2017) and potato (2018) yield in the productive and unproductive field. Bolded values indicate statistical significance (p < 0.10). ............................ 50 Table 3.4 Comparison of average (n=3) potato pest damage, potato N concentration, and bean N concentration (n=3) in the productive and unproductive. Bolded values and different letters show significant differences between treatments by Tukey’s Honest Significant Difference test (HSD, p<0.05). ......................................................................................................................................... 51 Table 3.5 Analysis of variance of potato pest damage, potato nitrogen (N) concentration (%), and bean N concentration (%) in the productive and unproductive field. Bolded values indicate statistical significance (p < 0.10). ................................................................................................. 51  xi  List of Figures  Figure 2.1 Historical (1981-2010) and reported mean total monthly precipitation (A) and temperature (B) data from the Vancouver International airport weather station (Min. of Environment, 2019). ..................................................................................................................... 17 Figure 2.2 Map of the western Fraser River Delta (FRD) and location of two fields located in Delta, British Columbia (BC). ...................................................................................................... 17 Figure 2.3 Plot layout of the productive field in 2017. ................................................................. 18 Figure 2.4 Plot layout of the unproductive field in 2017. ............................................................. 18 Figure 2.5 Plot layout of the productive field in 2018. ................................................................. 19 Figure 2.6 Plot layout of the unproductive field in 2018. ............................................................. 19 Figure 2.7 Principal component analysis (PCA) biplot with variables (C: soil carbon, EC: electrical conductivity, pH.H20: pH in water, pH.CaCl2: pH in CaCl2 solution, ABG: aboveground biomass, Ca: calcium, K: potassium, Db: bulk density, Fe: iron, N: nitrogen, PO4: available phosphorus, Mg: magnesium, and CEC: cation exchange capacity) grouped by the productive field and unproductive fields including all samples collected at 0-15 and 15-30-cm depths. Higher C and N were likely caused by poor drainage and water saturation in the unproductive field…………………………..………………………………………………….. . 25 Figure 2.8 Band and Inter-row placement of greenhouse gas collars in 2017 (A) and 2018 (B). 31 Figure 3.1 Comparison of average (n=3) mean weight diameter (MWD) of water-stable soil aggregates between treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at post-tillage, mid-season, and post-harvest in 2017 and 2018. Panels A-F are for the productive xii  field, and G-L are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05). ........................................................ 35 Figure 3.2 Comparison of average (n=3) permanganate oxidizable carbon (POXC) between treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at post-tillage, mid-season, and post-harvest in 2017 and 2018. Panels A-F are for the productive field, and G-L are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05). .................................................................................. 38 Figure 3.3 Comparison of average ammonium (NH4+-N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15 and 15-30-cm depths in 2017 and 2018. Panels A-D are for the productive field, and E-H are for the unproductive field. Red asterisks indicate significant differences for that date (p<0.05). ................................................................. 43 Figure 3.4 Comparison of average (n=3) ammonium (NH4+-N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15-cm in 2017 and 2018 for the production and post-season. Panels A-D are for the productive field, and E-H are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05). .................................................................................. 44 Figure 3.5 Comparison of average nitrate (NO3-N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15 and 15-30-cm depths in 2017 and 2018. Panels A-D xiii  are for the productive field, and E-H are for the unproductive field. Red asterisks indicate significant differences for that date (p<0.05). ............................................................................... 45 Figure 3.6 Comparison of average (n=3) nitrate (NO3--N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15-cm in 2017 and 2018 for the production and post-season. Panels A-D are for the productive field, and E-H are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05). .............................................................................................................................. 46 Figure 3.7 Comparison of average (n=3) test crop yield for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) in 2017 and 2018. For beans in the (A) productive field and (B) unproductive field and potatoes (C) in the productive field and (D) unproductive yield. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05). .............................................................................................................................. 50 Figure 3.8 Comparison of average (n=3) daily gas fluxes of carbon dioxide (CO2) (A), methane (CH4) (B), and nitrous oxide (N2O) (C) from 2017 to 2019 in the productive field for treatments of annually cropped (AC), annually cropped with N fertilizer (AC + N), 2-year grassland set-aside with N fertilizer (2G + N), and 3-year grassland set-aside with N fertilizer (3G + N). Red asterisks indicate significant differences for that date by Tukey’s Honest Significant Difference test (HSD, p<0.05). ....................................................................................................................... 56  Figure 3.9 Comparison of average (n=3) cumulative seasonal GHG emissions over the 2017-2019 production and non-production season for treatments of annually cropped (AC), annually cropped with N fertilizer (AC + N), 2-year grassland set-aside with N fertilizer (2G + N), and 3-xiv  year grassland set-aside with N fertilizer (3G + N). Panels A-D are for carbon dioxide (CO2), panels E-H are for methane (CH4), and panels I-L are for nitrous oxide (N2O). Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05). . 57                   xv  List of Equations  Equation 2.1: ................................................................................................................................. 28  Equation 2.2: ................................................................................................................................. 29  Equation 2.3: ................................................................................................................................. 30    xvi  List of Abbreviations BC – British Columbia CAP - Common agricultural policy CO2 - Carbon dioxide gas CH4 – Methane gas DF&WT - Delta Farmland and Wildlife Trust EA - Elemental analyzer EC - Electrical conductivity GHG – Greenhouse gasses (carbon dioxide, methane, and nitrous oxide) GLSA - Grassland set-asides MWD - Mean weight diameter N2O - Nitrous oxide gas NH4+-N – Ammonium nitrogen NO3--N – Nitrate nitrogen PAN - Plant available nitrogen PCA - Principal components analysis PCP - Permanent Cover Program POXC - Permanganate oxidizable carbon SOC – Soil organic carbon WFRD - western Fraser River delta     xvii  Acknowledgements  I would like to give my thanks to both of my supervisors, Dr. Sean Smukler and Dr. Maja Krzic for offering me the opportunity to work with them and for their tireless efforts and support throughout the duration of my thesis work. Working together with these two researchers who are both incredibly passionate and knowledgeable has inspired me in a professional and personal capacity, and I feel exceptionally lucky to have worked with them. I would also like to thank my committee members Dr. Art Bomke, Dr. Will Valley, and Drew Bondar for their continued guidance and support since the beginning of the project.  This research was supported by the Delta Farmland and Wildlife Trust, the Mitacs Accelerate Program, the Investment Agriculture Fund, and the Government of British Columbia. I would like to thank them for providing funding, without which this work would not have been completed.  I would like to thank staff at UBC in the Land and Foods Systems faculty and support from the work learn program that funded several undergraduate students to help support this research. I want to give thanks to the Sustainable Agricultural Landscapes lab coordinators Katarina Neufeld and Paula Porto who assisted with sampling and experimental setup. I also want to thank my lab mates for their support, camaraderie, and continued friendship throughout this process.  Lastly, I would like to give special thank my parents and family who have given me unwavering support and helped keep me grounded over the past two years and throughout the course of my education.  1  Chapter 1: General Introduction  Grassland set-asides (GLSAs), also known as grassland arable-rotations or grass-leys, are a conservation practice in which farmers take arable land out of crop production and seed it with grasses and legumes for at least one season. There are many reasons for GLSA implementation, including reducing market surpluses, providing wildlife habitat, transitioning of fields to organic production, and improving soil quality through increased soil organic matter and reduced soil erosion. In the western Fraser River delta (WFRD), a complex set of socioeconomic factors have contributed to agricultural intensification since the 1960s (Fraser, 2004). These intensive practices have led to varying levels of soil degradation in the region. In response to farmers’ concerns about increasing soil degradation, the Delta Farmland and Wildlife Trust (DF&WT), a local non-profit organization, established the GLSA Stewardship Program in 1993. Under this program, farmers are given a cost-share incentive to remove their agricultural land from production and seed it to grass and legume species for a period of 1 to 4 years (DF&WT, 2014). The effects of these short-term GLSAs on wildlife habitat have been well documented in the WFRD (Merkens, 2005); however, their impact on subsequent crops and soil properties are not well understood.  1.1 Soil Management Issues in the Lower Fraser River Delta  Due to expropriation of agricultural land by government agencies in the late 1960s, agricultural land in the Fraser River delta of British Columbia (BC) is operated under insecure land tenure (Fraser, 2004). As a result, many farmers are less inclined to implement expensive soil conservation practices (e.g., laser leveling, installation of subsurface drainage tiles, and long-term crop rotations) that can improve soil productivity and prevent soil degradation in the long-term (Fraser, 2004). Instead, they tend to focus on annual cash-crop production with short-term 2  crop rotations (Fraser, 2004). Farmers in the WFRV also employ intensive farming practices which include the use of various tillage practices and chemical fertilizer applications. This, in combination with high precipitation in the fall and winter, poor natural soil drainage due to high clay and silt content, salinity issues, and the aforementioned management practices make these soils susceptible to compaction and loss of soil organic matter (SOM) (Bertrand et al., 1991; Bomke and Temple, 1990; Principe, 2001).  Many farmers in the WFRD apply high rates of nitrogen (N) fertilizers in excess of plant needs and use intensive tillage practices to maximize crop yields. Mechanical disturbance of soil by tillage can stimulate microbial activity through increasing availability of oxygen. The microbes then consume C and N for metabolic energy and increase microbial respiration of soil organic carbon (SOC) and mineralization of N, leading to loss of C and N in the soil. Tillage can also lead to loss of soil structure and macropores, in turn increasing denitrification N losses especially during wet season (Coote et al., 1981; Baumhardt, 2015; Ratsep et al., 1994; Sullivan and Poon, 2016).  1.2 Grassland Set-asides  1.2.1 History of Set-aside Programs Around the World  Typically, a GLSA is a voluntary program and farmers are provided with an economic incentive to enroll their land for a set number of years (Rolfe, 1993; Baer, 2000). The European Economic Community used GLSAs from 1988 to 2008 as part of the Common Agricultural Policy (CAP), with the goal of limiting surplus crop production by reducing the production area of farms (European Commission, 2004). The CAP program included either rotational set-asides, requiring a portion of total arable land under set-aside from January to August of the same year or a fixed set-aside requiring an area of land in set-aside for at least 5 years, with farmers 3  receiving a payment for each year land was kept in set-aside (European Commission, n.d). In 1996, total enrollment in set-asides in 15 countries of the European Union was 7.26 million ha (5% of EU’s agricultural land) (Brouwer and Lowe, 2000). Because the price of crops increased due to the EU gaining access to different markets, shifting from a net-importer to a net-exporter status, the set-aside programs were removed from the CAP as the economic benefits to farmers and the environment were no longer justified. (European Commission, 2009).  The United States Department of Agriculture created the Conservation Reserve Program (CRP) in 1985 with the main goals of improving water quality, reducing soil erosion, and providing wildlife habitat (Ferris and Siikamaki, 2009). Farmers with land that meets certain eligibility criteria remove their fields from production and establish a long-term (10-15 years) plant cover of grassland, windbreaks, buffer strips, or riparian buffers in exchange for financial compensation (Stubbs, 2014). As of 2018, enrollment in the CRP program was at 9.1 million ha, and peak enrollment in the program was 14.9 million ha in 2007 (USDA FSA, 2018). In Canada, the Federal Government in coordination with the Prairie Farm Rehabilitation Administration, used GLSAs in the Permanent Cover Program (PCP) from 1988 to 1992 in Saskatchewan, Manitoba, and part of Alberta, and later included the Peace River area of Alberta and BC in 1991 (Vaisey et al., 1996). The program was established to reduce soil degradation on land susceptible to erosion. Farmers were given a single payment, which varied between which province and amount of area put into PCP, for establishing perennial forage or tree cover on marginal land for either a 10- or 21-year period, farmers were also allowed to manage their land by removing hay or grazing livestock. Over the course of the program, 522,000 ha of marginal land was converted to perennial cover (Vaisey et al., 1996). Reported benefits of the PCP were reduced soil degradation in terms of erosion, improved water quality by reducing sediment load 4  and chemical residues, enhanced wildlife habitat, increased C sequestration, reduced payments by federal governments from agricultural compensation programs (Guaranteed Revenue Insurance Program, Net Income Stabilization Account, and Crop Insurance) and by local governments for removing sediments from road ditches and drains (Vaisey et al., 1996). Ontario implemented a similar PCP from 1992 to 1994, through the Federal Government and the Ontario Soil and Crop Improvement Association. The goals of the program were to protect fragile land by reducing erosion and chemical runoff by planting permanent cover of GLSAs or trees in buffer strips for 5-, 10-, or 15-year time periods on sensitive cropland, especially along waterways or on floodplains (NSCP, n.d.). This program included 2,029 ha of buffer strips (NSCP, n.d.). A federal initiative called Greencover Canada was implemented across Canada from 2003 to 2008, with the goals of improving management practices, protecting water quality, reducing greenhouse gas emissions, and enhancing wildlife biodiversity by converting environmentally sensitive land to perennial cover. A financial incentive was provided for farmers to plant a minimum of 40 acres of perennial cover to grass or tree species to be maintained for at least 10 years (AAFC, 2003). This program converted approximately 145,000 ha of marginal land to perennial cover (McNeil, 2013). A similar program called the Conservation Cover Program was established in Saskatchewan from 2001 to 2003, with the goals to conserve soil, protect rural water quality, enhance fish and wildlife habitat, reduce GHGs, sequester C, and increase pasture for livestock (Gosselin and Tremblay, 2001). Farmers could include up to 50 acres into perennial cover. Over two years, approximately 324,000 ha of farmland was converted to perennial cover (Folk, 2003). 1.2.2 The Grassland Set-aside Stewardship Program  5  The GLSA Stewardship Program established by the DF&WT in 1994 is the only remaining GLSA program in Canada. It is a relatively unique program, as other GLSA programs usually require longer enrollment periods, and it is funded by a non-profit organization that implements several different programs on an ongoing basis all aimed at conserving agricultural and wildlife resources. In the DF&WT’s GLSA Stewardship Programs, farmers are given an annual cost-share payment to convert their productive crop land to a mixture of grass and legume species for a period of 1 to 4 years – receiving $300, $250, $250, and $300/acre/year for the 1st, 2nd, 3rd, and 4th year, respectively. The main objectives of this program are: providing wildlife habitat, improving soil structure, and increasing SOM (DF&WT, 2014). Farmers enroll what they consider to be productive and unproductive (degraded) fields into the GLSA program for a number of reasons, including: improvement of degraded soils, inclusion in annual crop rotations, or to enhance the transition from conventional to organic crop production (Lussier et al., 2019; Yates, 2017). The GLSA seed mix contains a mix of grasses and clover established based on requirements of low cost and maintenance, ability to enhance soil organic matter and structure, and ability to provide cover for foraging, roosting, and nesting wildlife (DF&WT, 2014). Harvest of GLSA for hay or silage is permitted once per year at certain time periods with a reduced yearly cost-share payment, and DF&WT may permit mowing to increase wildlife habitat value (DF&WT, 2014). Although there is some evidence that short-term GLSAs can improve soil quality (Yates, 2017; Lussier, 2018) the effects of GLSAs on subsequent crop production is not well understood nor are the other environmental outcomes such their contribution to climate change mitigation. While high levels of SOM is typically associated with increased PAN and N mineralization in soil (Nevens and Reheul, 2002; Zhang et al., 2015), some farmers in the region 6  worry that GLSAs may actually be detrimental to subsequent crop production in the year following GLSA incorporation, as incorporating organic materials with a high C to N ratio can temporarily immobilize PAN (Trinsoutrot et al., 2001; Vinten et al., 2002), or host pests that can increase crop damage and impact crop quality (Keiser et al., 2012; Parker and Howard, 2001). It is also possible that any benefits to soil quality or C sequestered in the soil during the GLSA are lost through intensive tillage for the preparation of the subsequent cash crop. 1.2.3 Effects of Grassland Set-asides on Soil Properties  1.2.3.1 Soil Aggregate Stability  Soil aggregate stability is a measure of soil structure, and the ability of aggregates to withstand external pressures of erosion (Papadopoulos, 2011). An increased aggregate stability has been found to have a correlation with higher levels of SOM (Tisdall and Oades, 1982; Paustian et al, 2000; Six et al., 2000; Lussier, 2018). It is a useful indicator of soil quality, as it is strongly impacted by soil management practices (Six et al., 1999) and combines elements of physical, biological, and chemical soil properties (Doran and Parkin, 1996). Karlen et al. (1996) found increased aggregate stability after 2.5-years of CRP compared to paired cropland in Iowa. Several local studies have found that GLSA’s have potential to increase water-stable soil aggregates; however, some of the studies have been conflicting. Yates et al. (2017) found that there were no differences between 2-, 3-, and 4-year GLSA, but found improvements after 6-year GLSAs. Hermawan and Bomke (1996) and Armstrong (2013) found increased aggregate stability after 2-year GLSA. Lussier et al. (2019) found increases in aggregate stability after 1- and 2-year GLSAs. However, Principe (2001) did not find differences in aggregate stability after 1-year GLSA. These differences were found to be due to varying baseline characteristics of fields and management practices of farmers prior to entering GLSA (Lussier et al., 2019; 7  Principe, 2001; Yates, 2014) as well as varying associated management practices that accompanied GLSA (Lussier et al., 2019). 1.2.3.2 Soil Organic Carbon  SOC is an important indicator for soil quality, as it contributes to soil structure, water retention and storage, nutrient storage, and nutrient availability (Brady and Weil, 2010). GLSAs can contribute to SOC through root exudates, turnover of plant biomass, and through plant biomass incorporation at the end of the GLSA rotation (Gebhart et al., 1994). Tillage breaks up the soil aggregates and introduces oxygen to soil microbes. When microbes are able to access oxygen, they can rapidly utilize SOC in the soil through aerobic respiration, resulting in increased CO2 emissions and potentially decreased SOC stocks. By halting tillage and seeding perennial vegetation, SOC may increase as the disturbance of soil structure is decreased, microbial access to oxygen is reduced, and organic matter from perennial aboveground and belowground biomass is incorporated into the soil. Karlen et al. (1999) reported differences in SOC after 2.5 years of CRP GLSA in Iowa, USA compared to annually cropped fields, while Gebhart et al. (1994) found differences after 5 years of CRP in GLSA in Texas, Kansas, and Nebraska, USA compared to nearby annually cropped fields. However, rates of SOC accumulation are variable, and have been found to be dependent on climatic conditions of moisture and temperature, and on the length of GLSA implementation (Hutchison et al., 2007), making localized studies important for implications of the effects on GLSA. For example, studies in the WFRD by Yates et al. (2017) found that SOC levels did not differ significantly between 2- to 6-year GLSAs and paired annually cropped fields, and Lussier (2018) found that there were no significant differences in SOC between 2-year GLSAs and annually cropped fields. It was noted in both studies that differences in soil properties prior to GLSA establishment 8  including Na content, electrical conductivity, and aggregate stability as well as past management practices (e.g., laser leveling, subsoiling, tillage, crop rotations) determined establishment of GLSAs and their effects on SOC.  Active C, a labile pool of SOC that typically represents 3-4% of total soil C, has been identified as a good indicator of soil quality (Cambardella and Elliot, 1994). This pool of SOC is easily decomposed and is associated with microbial activity, nutrient cycling, and aggregate formation (Weil et al., 2003). One method of examining different fractions of the active C pool as described by Weil et al. (2003) uses a dilute (0.02 M) slightly alkaline solution of potassium permanganate (KMnO4) to determine permanganate oxidizable C (POXC). The active C pool determined by this method was found to be sensitive to management effects and closely related to several measures of microbial activity and various other fractions of SOC, as well as water-stable aggregates (Weil, 2003, Lewis et al., 2011; Lopez-Garrido et al., 2011; Culman et al., 2016; Romero et al., 2018; Plaza-Bonilla et al., 2014; DuPont, 2010). In the study conducted by Lussier (2018) mentioned above, although not significant, POXC responded to changes between 2-year GLSAs and paired annually cropped fields. Again, these fields varied widely in terms of baseline conditions and management. This is similar to results from Idowu and Kircher (2016), that found no differences in POXC for land in CRP for 15 years compared to adjacent annually cropped fields. 1.2.3.3 Plant Available Nitrogen  Although N is highly abundant in the atmosphere, hydrosphere, and lithosphere, only 1% of soil N is potentially available for plant uptake (Galloway, 2003). GLSAs can contribute to PAN through decomposition of its biomass (Nevens and Reheul, 2002; Zhao et al., 2016); however, its contribution is determined by the C:N ratio of the biomass. A higher concentration 9  of C relative to N (C:N > 25:1) can temporarily reduce PAN through immobilization of nitrogen, while a lower ratio (C:N < 25:1) promotes mineralization of N and increases PAN (Trinsoutrot et al., 2001; Vinten et al., 2002). Baumhardt et al. (2015) found that soil degradation including the loss of SOM, microbial disruption due to tillage, and weakened soil structure from intensive agriculture practices using inorganic fertilizer led to an inability of the soil to provide sufficient PAN for crop growth, requiring higher reliance on fertilizer and manure inputs. Although there have been several studies evaluating GLSA effects on soil quality in the WFRD, there have been few studies that examine PAN in subsequent crop production. Walji (2017) studied PAN from GLSAs in the WFRD and had confounding results. It was found that 3-year GLSAs contributed an 18-kg N ha-1 in compared to annually cropped fields in one year of the study. However, in the second year of the study, 3-year GLSAs reduced N by 20-kg N ha-1 relative to annually cropped fields. Variability of GLSAs contribution to PAN was influenced by: the type of fertilizer applied, variability in GLSA biomass C:N ratios, differences in soil quality (SOC, total N, pH, and Na), and variability in time between GLSA incorporation and cash crop seeding. 1.2.3.4 Greenhouse Gas Emissions  Intensive agricultural practices can contribute to climate change through land use change, use of tillage practices, inorganic and organic fertilizer inputs (Robertson, 2000). However, through sustainable agricultural practices such as reduced tillage, conversion of marginal land to perennial vegetation, use of cover crops, improved fertilizer management and amendment use, and agroforestry, there is potential to mitigate GHGs (Paustian et al., 2016). While GLSAs could be a means of sequestering a sizable amount of C, the overall impact on climate must account for other GHGs produced throughout the rotation. GHGs of CO2, CH4, and N2O (GHG) from agriculture together are about 8.5% of total GHGs in Canada (60.0-Mt. CO2-eq per annum) of 10  which 22% are due to inorganic nitrogen fertilizers, 18% from soil management, 13% from manure management, and 5% from liming, urea application, or C containing fertilizers, (Environment and Climate Change Canada, 2018) all management practices that can occur in a GLSA rotation. It is important to note that CH4, and N2O have potential to contribute more to the greenhouse effect due to higher radiative forces of 298 and 25 times over a 100-year time period compared to CO2 respectively. In order to effectively assess the impact of GLSAs on the climate it is important to quantify these other GHG emissions after incorporation. However, there are relatively few studies examining the effect of short-term GLSAs on GHGs. One study found no differences in CO2 emissions in 1- to 4-year-old GLSAs with different manure application rates in reference to a pea crop following maize production, but did find higher CO2 emissions after a 17-year-old GLSA (Acharya et al., 2012). Another study found increased CO2 and N2O missions following conversion of fields in CRP for 22 years to conventional tillage treatments, but no differences in CH4 emissions (Ruan and Robertson, 2013). Similarly, increased 5-year cumulative N2O emissions were found in fields converted to continuous corn after 22 years in CRP (Abraha et al., 2018). In contrast, Dobbie and Smith (1996) found decreased CH4 oxidation after 3 years in GLSA compared to continuously cropped fields. However, there have been no studies quantifying the effects of GLSAs on GHGs in the WFRD. 1.3 Study Objectives and Hypotheses  The objectives and related hypotheses of the study are: Study objective 1: Quantify the effects of incorporating a 2- and 3-year-old GLSA with and without N fertilizer application on soil aggregate stability and POXC in productive and unproductive fields.  11  Associated hypotheses: I hypothesize that with GLSAs, there will be higher soil aggregate stability and POXC than in fields with continuous cropping. I however expect that these benefits would be reduced with the addition of N fertilizer as it is likely to increase microbial consumption of labile C from increased N supply. I hypothesize that these benefits will be observable in the second season following GLSA incorporation but will not be as high as in the initial season after incorporation because of further disruption from tillage and decomposition of SOC.  Study objective 2: Quantify the effects of incorporating a 2- and 3-year-old GLSA with and without N fertilizer application on PAN, crop yield, and crop quality. Associated hypotheses: I expect higher PAN in the cropping season following a GLSA because of the incorporation of additional N stored in the perennial GLSA plant biomass. I expect the additional N stored in GLSA will not be sufficient for crop production and additional fertilizer will be required to meet crop demands. I also hypothesize that the benefits of GLSA on PAN will be observable in the second season following GLSA but will not be as great as the initial season because labile carbon will be decomposed in the first growing season. Furthermore, I hypothesize that in the production season following GLSAs, there will be an earlier increase in PAN than in fields with annual cropping, because of the incorporation of labile C and additional N stored in the GLSA biomass. I also hypothesize that these benefits will be observable in the second season following GLSA but will not be as great as the initial season due to decomposition of SOC. Finally, I hypothesize that relatively high C:N ratio of GLSA biomass will delay immobilization of more recalcitrant C sources until later in the season, which will 12  increase the soil NO3- left after crop harvest known as residual soil N. I hypothesize that if the GLSAs increase PAN that the crop yields will also be improved relative to the annual cropping and that these benefits will continue into the second season. However, I also hypothesize that crop quality of potatoes decrease due to pest damage with longer durations of GLSA, as they may harbor insects.  Study objective 3: Quantify the effects of incorporating a 2- and 3-year-old GLSA with N fertilizer on GHGs. Associated hypotheses: I hypothesize that because the GLSA provides large quantities of labile C, microorganisms are going to respire more, resulting in higher CO2 emissions in the seasons following GLSA than in annual cropping. With additional N fertilizer I would expect increased microbial activity should result in increased N2O emissions in the GLSA compared to annual cropping particularly in the fertilized treatments. If soil structure is improved as expected, I hypothesize that CH4 emissions are going to decrease because of greater aeration in the cropping season following GLSA compare to annual cropping. I expect to see this pattern continue the following the year.   This study will contribute to a better understanding of how the incorporation of 2- and 3-year-old GLSAs impact subsequent soil properties and vegetable crop production and how these outcomes vary, based on the background conditions of the fields. This information may provide farmers with evidence of the impacts of GLSA on their crops, improve fertilizer recommendations regarding better N application rates and reduced losses to the environment, and inform policy decisions concerning soil quality and GHGs. 13  Chapter 2: Materials and Methods  2.1 Field Descriptions  The WFRD is one of the most productive agricultural regions in the province, accounting for 25.5% of gross farm receipts ($954.6 million) and occupying 1.5% of total farmland area (38, 380 ha) of BC (Ministry of Agriculture, 2017). The region has temperate maritime climate, characterized by mild wet winters and warm dry summers with an average air temperature of 11.7C and average rainfall of 1189-mm, with 80% of precipitation occurring between October and April (Figure 2.1) (Ministry of Environment, 2019). Soils are typically of the Gleysolic order and are formed on medium to moderately textured fine Fraser River deltaic deposits approximately 100-cm thick. Topography is slightly undulating with slopes <3%, and elevation is 1-3 m above sea level (Luttmerding, 1981).  The study was carried out from May 2017 to March 2019 on two operational farms in the municipality of Delta, BC (Figure 2.2). In 2015, both fields were seeded with a mixture of grass and legume species with 25% orchard grass (Dactylis glomerata L.), 28% tall fescue (Festuca arundinacea L.), 30% short fescues (Festuca rubra var. commutata L.and Festuca rubra var. rubra L.), 15% timothy (Phleum pratense L.), and 2% red clover (Trifolium pratense L.) by seed weight.  Lussier et al. (2019) completed an analysis of eight fields in the GLSA stewardship program in the Delta region and found that they could be classified into productive and unproductive groups. Unproductive fields had lower mean weight diameter (MWD) of water-stable soil aggregates, low total soil C, higher exchangeable sodium content, and lower GLSA aboveground biomass. Two fields included in my study were selected from those eight fields, where the productive field (49.0354N, 123.0815W) had lower exchangeable sodium, lower 14  bulk density, and higher GLSA biomass, while the unproductive field (49.0326N, 123.0725W) had higher exchangeable sodium, higher bulk density, and lower GLSA biomass. The productive field was located on a silt loam Crescent Orthic Gleysol, while the unproductive field was on a silt loam Ladner Humic Luvic Gleysol (Luttmerding, 1981). The productive field had manure inputs within the three years prior to my study, while the unproductive field did not. Both fields were characterized by moderately poor to poor natural drainage and had only surface drainage system installed without any laser leveling or sub-surface drainage (Lussier, 2018). 2.1.1 2017 Field Season   In 2017, on both productive and unproductive fields, a field experiment (60 m × 15 m) was established as a completely randomized split-plot block design with three blocks (replications), where GLSA treatments were applied at the plot level, and fertilizer treatments at the subplot level (Figure 2.3 and Figure 2.4). In the first week of May, the 2-year-old GLSA plots were mowed using a Stihl FS 240 trimmer to allow aboveground biomass to dry out before incorporation. In the second week of May, the following treatments were established:  1. AC: 2-year GLSA aboveground biomass was removed with a rake, belowground biomass was tilled in, and no N fertilizer was applied. 2. AC + N: Same as above, except N fertilizer rate of 80-kg N ha-1 was applied. 3. 2G: 2-year GLSA aboveground and belowground biomass were both tilled in, and no N fertilizer was applied. 4. 2G + N: Same as above, except N fertilizer rate of 80-kg N ha-1 was applied. On May 21st, soil on both fields were tilled (which resulted in incorporation of the GLSA biomass) using three passes of a Kubota L3301 HST 4WD tractor with a Maschio type W PTO 15  rotary tiller. On July 7, Cadillac variety garden beans (Phaseolus vulgaris L. cv. Cadillac) were seeded at a density of 50,000 seeds ha-1 using a Jang JD-1 Manual Seeder at a depth of 5-cm. N fertilizer treatments were applied at the split plot level at treatment rates, in a split application with a band at seeding of 50-kg N ha-1 and an additional band at flowering on August 31st using ammonium sulfate (21-0-0). Phosphorus (P) was applied at a rate of 30-kg P ha-1 and potassium (K) at 90-kg K ha-1 across all treatments with a hoe using triple super phosphate (0-45-0 + 13.5 Ca) and potassium sulfate (0-0-50).  Beans were irrigated at the block level using sprinkler irrigation once every two weeks, starting on July 21st until soil reached approximately 12% volumetric water content at the 0-10-cm depth, which was measured using a Fieldscout TDR 100 (Spectrum Technologies, Inc. Plainfield, IL) soil moisture meter. Beans were harvested on September 21st in both fields. GLSA was allowed to continue to grow in continuous GLSA plots for future treatments, and some plots were not included in the study. 2.1.2 2018 Field Season In 2018, the experiment was continued in the same field locations used in 2017. However, in addition to the treatments established in 2017, which had gone through a year of annual crop production, one more treatment (3-year GLSA) was established (Figure 2.5 and Figure 2.6), creating the following set of treatments: 1. AC: 2-year GLSA aboveground biomass was removed with a rake, belowground biomass was tilled in, and no N fertilizer was applied followed by one season of bean production. 2. AC + N: Same as above, except N fertilizer rate of 100-kg N ha-1 was applied. 16  3. 2G: 2-year GLSA aboveground and belowground biomass were both tilled in, and no N fertilizer was applied followed by one season of bean production. 4. 2G + N: Same as above, except N fertilizer rate of 100-kg N ha-1 was applied. 5. 3G: 3-year GLSA aboveground and belowground biomass were both tilled in, and no N fertilizer was applied. 6. 3G + N: Same as above, except N fertilizer rate of 100-kg N ha-1 was applied. The 3G treatments were established by mowing half of the continuous GLSA plots in the last week of May and tilling the GLSA using 3 passes of the same machinery used in 2017. All other treatments were tilled at the same time using 2 passes of the same machinery. A Kubota L3301 HST 4WD tractor with an attached row hiller was used to establish plant bed. Kennebec potatoes (Solanum tuberosum L. cv. Kennebec), a white mid-season potato variety with high yields of large tubers (Mosley et al., 1995), were seeded by hand on June 5th at a rate of 1800-kg ha-1 at a target depth of 10-cm. N fertilizer treatments were applied at the same time in a band at the split-plot level at the treatment rates. P and K were applied at rates of 85-kg ha-1 and 162-kg ha-1, respectively across all treatments (Figure 2.5 and Figure 2.6). Potatoes were irrigated using drip irrigation once every two weeks starting June 21st until soil reached approximately 12% volumetric water content, as in 2017. Potato plants were hilled using a hoe on July 11th and were top killed on August 30th by mowing the plants at the soil surface using a Stihl FS 240 trimmer to allow potato skins to harden underground for 2 weeks. Potatoes were harvested on September 21st, and on October 9th a cover crop of 70% fall rye (Secale cereale L.), 20% winter peas (Pisum sativum ssp. arvense L.), and 10% winter vetch (Vicia villosa L.) by seed weight was applied at a rate of 50-kg ha-1.  17   Figure 2.1 Historical (1981-2010) and reported mean total monthly precipitation (A) and temperature (B) data from the Vancouver International airport weather station (Min. of Environment, 2019).    Figure 2.2 Map of the western Fraser River Delta (FRD) and location of two fields located in Delta, British Columbia (BC).         18   Figure 2.3 Plot layout of the productive field in 2017.   Figure 2.4 Plot layout of the unproductive field in 2017. 19   Figure 2.5 Plot layout of the productive field in 2018.   Figure 2.6 Plot layout of the unproductive field in 2018.  20  2.2 Sampling and Analyses   2.2.1 Soil and GLSA Properties  At the beginning of the study in May of 2017, immediately after GLSA treatment establishment, soil samples were taken at 0-15 and 15-30-cm depths. Four subsamples were taken from each plot using an Oakfield soil probe and composited by depth for each plot. These samples were air dried for 2 weeks, ground, and passed through a 2-mm sieve. Subsamples of 50 g were taken from each sample and sent to the BC Ministry of Environment for analysis of effective cation exchange capacity (CEC) including cations Al, Ca, Fe, K, and Na and available P. CEC was determined by inductively coupled plasma optical emission spectrometry (ICP-OES) using 0.2 M barium chloride solution extractions (Hendershot and Duquette, 1986). Available soil P was determined using the Bray P-1 method (Bray and Kurtz, 1945) and was measured on an ultraviolet visible spectrophotometer. These samples were also used to determine soil pH, electrical conductivity (EC), total soil C, total soil N, and texture. Soil pH and EC were measured using an Oakton PC700 series benchtop meter and air-dried soil in water at a 1:2 ratio and pH was also measured in a 0.01M CaCl2 solution at a 1:2 ratio (Hendershot et al., 1993). Soil texture was determined using the hydrometer method (Lavkulich, 1981). Bulk density samples were taken once per block at the same time at depths of 0-15 and 15-30-cm and analyzed using the core method according to Hao et al. (1993).  In May of 2017 and 2018, soil samples were taken at 0-15 and 15-30-cm depths immediately after GLSA treatment establishment to analyze total soil C and N concentration using the diffuse Fourier transform mid-infrared spectroscopy (FT-MIR) method (Reeves et al., 2001) on a Tensor 38 HTS-XT spectrometer (Bruker Optics, Ettlingen, Germany). This data was parameterized and validated using an Elemental Vario El Cube elemental analyzer (EA) 21  (Elementar Analysenysteme GmbH, Hanau, Germany) (Kirsten and Hesselius, 1983) on 25% of the samples (Thiel et al., 2017). In 2017 and 2018 prior to GLSA incorporation, two replicate samples of GLSA aboveground biomass yield were collected per plot from 1 m × 1 m quadrats. These samples were analyzed for total C and N using EA as above. 2.2.2 Characterization of Field and GLSA Properties  Soil chemical and physical properties in 2017 and 2G and 3G GLSA aboveground biomass were characterized across 12 plots at each of the productive and unproductive fields to provide insight into how they respond to GLSA effects.  The two fields varied substantially, compared to the productive field, the unproductive field had 1229% more sodium, 104% higher EC, 30% more total soil C, 13% more total soil N, and 6% lower bulk density (Table 2.1 and Table 2.2). While the productive field had field had 160% and 180% more dry GLSA biomass in 2017 and 2018 respectively and higher soil and aboveground biomass C:N ratios compared to the unproductive field. Poor drainage and increased periods of saturation in the unproductive field likely led to slow SOC decomposition rates resulting in higher soil C and N relative to the productive field.   A principal component analysis (PCA) of all baseline field properties was completed to explain the variability of these properties in the productive and unproductive field. The PCA found that 67.5% of the variability in the data showed a response to the grouping of field type and displayed the main trends in soil variation in each field (Figure 2.7). The productive field was associated with higher GLSA aboveground biomass, higher bulk density and higher sand, while the unproductive field was associated with higher Na, EC, clay, silt, total soil C and N, and available phosphorus. It was expected that the unproductive field would have lower GLSA 22  aboveground biomass and higher sodium levels; however, it was not expected that the productive field would have lower total soil C and N, a higher bulk density, and lower available P.                      23  Table 2.1 Average soil chemical and physical properties across all plots (n=12) before establishment of my field experiment in May of 2017 for the productive and unproductive fields. Standard errors are shown in brackets.  Soil Properties Depth (cm) Unit Productive Field Unproductive Field Bulk Density 0-15 g cm-3 1.37 (0.0) 1.29 (0.0) 15-30 1.37 (0.0) 1.27 (0.0) Total Soil C 0-15 % 1.69 (0.1) 2.20 (0.0) 15-30 1.46 (0.1) 2.07 (0.1) Total Soil N  0-15 0.16 (0.0) 0.18 (0.0) 15-30 0.13 (0.0) 0.18 (0.0) Sand 0-15 58.7 (0.7) 53.3 (0.9) 15-30 58.0 (0.0) 55.0 (1.5) Silt 0-15 16.0 (0.0) 17.7 (1.2) 15-30 15.7 (0.3) 17.0 (1.5) Clay 0-15 25.3 (0.7) 29.0 (1.2) 15-30 26.3 (0.3) 28.0 (0.0) Aluminum  0-15 cmol+ kg-1 0.08 (0.0) 0.07 (0.0) 15-30 0.08 (0.0) 0.05 (0.0) Calcium  0-15 9.06 (0.1) 9.15 (0.0) 15-30 9.54 (0.1) 8.47 (0.1) Fe 0-15 0.00 (0.0) 0.00 (0.0) 15-30 0.00 (0.0) 0.00 (0.0) K 0-15 0.80 (0.0) 0.60 (0.0) 15-30 0.63 (0.0) 0.47 (0.0) Mg 0-15 1.80 (0.0) 3.61 (0.0) 15-30 2.18 (0.1) 3.63 (0.0) Mn 0-15 0.02 (0.0) 0.02 (0.0) 15-30 0.02 (0.0) 0.02 (0.0) Na  0-15 0.07 (0.0) 0.93 (0.1) 15-30 0.10 (0.0) 1.72 (0.1) Cation exchange capacity (CEC)  0-15 11.83 (0.1) 14.38 (0.1) 15-30 12.54 (0.1) 14.36 (0.1) Available PO4  0-15 mg kg-1 130.4 (3.2) 200.0 (2) 15-30 103.1 (3.5) 178.8 (5.1) pH in CaCl2 0-15 5.2 (1.5) 5.2 (1.5) 15-30 5.4 (1.6) 5.4 (1.6) pH in H2O 0-15 5.5 (0.0) 5.9 (0.1) 15-30 5.7 (0.0) 6.1 (0.1) Electrical conductivity (EC) 0-15 dS m-1 1.19 (0.3) 2.43 (0.7) 15-30 1.2 (0.3) 4.03 (1.2)   24  Table 2.2 Average properties of aboveground biomass from 2- and 3-year-old grassland set-asides (GLSA) determined in May of 2017 and 2018 from the productive and unproductive fields (n=6). Standard errors are shown in brackets.  Properties Unit Productive Field Unproductive Field 2017 – 2-year-old GLSA    Aboveground Biomass  Mg ha-1 5.08 (0.21) 1.95 (0.07) C:N Ratio  41.8 (6.89) 27.6 (3.05) Biomass C  Mg-C ha-1 2.13 (0.08) 0.79 (0.02) Biomass N kg-N ha-1 50.84 (2.07) 28.80 (0.99) Total C % 41.8 (1.43) 40.7 (0.22) Total N 1.0 (0.15) 1.5 (0.15) 2018 – 3-year-old GLSA       Aboveground Biomass Mg ha-1  5.13 (0.75) 1.83 (0.31) Biomass C:N Ratio   33.0 (2.50) 26.8 (1.19) Biomass C  Mg-C ha-1 2.07 (0.30) 0.74 (0.10) Biomass N kg-N ha-1 62.57 (9.16) 23.66 (4.01) Total C % 40.3 (0.51) 40.8 (0.07) Total N 1.2 (0.10) 1.5 (0.07)     25   Figure 2.7 Principal component analysis (PCA) biplot with variables (C: soil carbon, EC: electrical conductivity, pH.H20: pH in water, pH.CaCl2: pH in CaCl2 solution, ABG: aboveground biomass, Ca: calcium, K: potassium, Db: bulk density, Fe: iron, N: nitrogen, PO4:  available phosphorus, Mg: magnesium, and CEC: cation exchange capacity) grouped by the productive field and unproductive fields including all samples collected at 0-15 and 15-30-cm depths. Higher C and N were likely caused by poor drainage and water saturation in the unproductive field.  2.2.3 Crop Yield, Quality, Carbon, and Nitrogen Concentration  On September 21st, 2017, bean pods and bean plant biomass yield were sampled by taking two random samples per plot of an area 1 m × 1.6 m. These samples were then weighed, oven-dried at 60C for 48 hours to obtain moisture content, then ground and used to determine total C and N using the EA as in section 2.2.1. On September 21st, 2018, potato tuber yield was sampled by taking one random sample per plot of an area 1 m × 2 m. Potatoes were washed and 26  graded by size class (B=<7.62, A1=5.72-7.62, A2=3.81-5.72, A3=2.54-3.81, A4=<2.54-cm), and 10 potatoes were subsampled based on the percentage of potatoes in each class (Appendix A). These 10 potatoes were assessed for wireworm (Agriotes obscurus L.) damage on a scale of zero to five based on surface area damage (0=<5%, 1=5-10%, 2=10-15%, 3=15-20%, 4=20-25%, 5=>25). A 10 g sample from the center of each potato was cut into 1-cm × 1-cm cubes and stored in the freezer, prior to drying using a Labconco Model Freeze drier (Labconco, Kansas City, MO, USA) at -55°C for four days. These samples were then ground and used to determine total C and N using the EA as in section 2.2.1. Another 10 g sample from the center of each potato was dried at 60C for 48 hours to obtain moisture content.  2.2.4 Plant Available Nitrogen  To determine PAN, consisting of ammonium (NH4+-N) and nitrate (NO3--N), soil samples were collected every 10-14 days during the production season. Prior to treatment establishment, four subsamples were collected from 0-15 and 15-30-cm depths in each plot and composited by depth. After treatment establishment, four subsamples were collected from each subplot at 0-15 and 15-30-cm depths for both row and inter-row locations and composited by depth. Row areas refer to locations directly adjacent to plants and fertilizer application, while the inter-row areas refer to areas between plant rows. In 2017, sampling started on May 17th and was completed September 21st. In 2018, sampling started on May 21st and was completed on September 21st. Samples were packed into coolers and transported to the lab and stored at 4C. Within 48 hours of sampling, PAN was extracted using a 2 M KCl extraction solution and analyzed colorimetrically (Doane and Horwath, 2003) using a spectrophotometer (Bio-Rad iMark, Hercules, CA, USA) at 650 nm for NH4+-N and 540 nm for NO3--N.  To determine soil water content, a 20-g sample of field-moist soil sample was oven-dried 27  at 105C for 24 hours until it reached a stable weight (Blake and Hartge, 1986). Soil N concentration were converted to content (kg ha-1) using bulk density values for each plot obtained during baseline soil sampling. 2.2.5 Greenhouse Gas Emissions  To measure GHGs of CO2, CH4, and N2O, closed static chambers (non-flow through or non-steady state were used following the procedure described by Schiller and Hastie (1994) in combination with a DX4040 portable Fourier Transform Infrared (FTIR) Spectrometer gas analyzer (Gasmet) (Gasmet Technologies Group, Helsinki, Finland). Opaque cylindrical PVC collars with 20-cm inner diameter, 0.6-cm thick walls, 15-cm height, and a beveled edge, were inserted in the soil up to a depth of 5-cm to prevent lateral movement of gases. A rubber mallet was used to install the collars to prevent soil disturbance, and soil was leveled to maintain a constant headspace of 10-cm in the column. All measurements were taken a minimum of 24 hours after collars were inserted into the soil to allow soils to equilibrate.  In 2017, prior to seeding and fertilizer application, one collar was installed in the productive field on each plot that had GLSA treatments of AC and 2G. After seeding and fertilizer treatment establishment, collars were placed on the productive field on AC, AC + N, and 2G + N treatments (Figure 2.3). In 2018, additional collars were placed on 3G + N treatments (Figure 2.5). Collars were placed at row and inter-row locations of each treatment to estimate the total GHGs from the whole field (Figure 2.8). All sample measurements were taken between 9:00 am and 6:00 pm in the summer and 10:00 am to 4:00 pm in the winter to ensure relatively constant atmospheric conditions and soil temperatures. A fabricated metal lid with Teflon ® tubing (polytetrafluoroethylene) of 4-mm inner diameter and 6-mm outer diameter connected the lid to a Cole-Parmer Drierite 26800 28  Drying column containing blue-pink indicator silica gel which removed excess moisture from air samples. Air then passed through a Swagelok stainless steel all-welded in-line filter with 7 micron pore size and a Cole-Parmer RK-06103 filter holder with a hydrophobic Teflon membrane (1-2 m pore size, 50-mm diameter, and 0.254-mm thick) before being connected to the input of the DX4040 portable Fourier Transform Infrared (FTIR) Spectrometer gas analyzer (Gasmet Technologies Group, Helsinki, Finland). The gas output line used the same tubing to recirculate the air inside the chamber. The portable FTIR was calibrated with N2 prior to gas flux measurements to ensure accurate gas flux readings. Afterward, the portable FTIR was turned on for 5 minutes before measurements to ensure proper internal temperatures and stabilized background atmospheric gas concentrations, the lid was placed securely on the gas collar for 10 minutes with approximately 2 minutes between readings. The 2 minute break period allowed the machine to reach atmospheric gas concentrations before additional measurements. Ancillary measurements of collar air temperature and soil temperature at 10-cm depth (C) were taken using a thermometer, and soil volumetric water content (%) for 0-7.6-cm using the Fieldscout TDR 100 soil moisture meter were taken at the time of GHG sampling (Appendix B). During the production season from June to September of 2017 and 2018, GHGs were measured once per week, and during the non-production season from October to March of 2017 to 2018 and 2018 to 2019, GHGs were measured once every two to three weeks. Gas fluxes were calculated using MATLAB version 2014a (MathWorks, 2014), using the following equation: Equation 2.1:  𝐹 = 𝜌𝑆𝑉|𝐴 Where F is the flux (mol m-2s-1),  is the molar density of dry air (mol m-3), 𝑉 is the volume of the chamber headspace (m3), and 𝐴 is the area of the headspace (m2). Fluxes were calculated the same way for each gas. Between measurements the fluxes of GHG was linearly interpolated. 29  2.2.6 Wet Soil Aggregate Stability  Composite soil aggregate stability samples were taken at 0-7.5-cm depth and were comprised of three individual samples per subplot. Sampling was done three times during the production season – before seeding (June 9th, 2017 and June 4th, 2018), mid-season (August 9th, 2017 and July 17th, 2018), and before harvest (September 21st in 2017 and 2018). Samples were collected by a hand trowel and stored in a hard-walled container in a fridge at 4C until analysis. Soil aggregate stability was determined using the wet-sieve method (Nimmo and Perkins, 2002). Soil was sieved to obtain aggregates from 2-6-mm in diameter, 15-g of these aggregates were weighed into a sieve nest with opening sizes of 2, 1, and 0.25-mm, then aggregates were gradually moistened using a mister for 30 minutes until fully saturated. Sieve nests were attached to an agitator with a vertical stroke of 2.5-cm, an oscillating action through an angle of 30, moving at a rate of 30 strokes per minute, and submerged in water for 10 minutes before being removed. Sieves were then oven-dried at 105°C for 24 hours, and the soil remaining on each sieve was weighed. The mass from each size class was expressed as percentage of the total non-aggregate particle-free sample mass. Van Bavel’s calculation (Equation 2.2) described by Kemper and Rosenau (1986) was used to calculate the MWD:  Equation 2.2:  𝑀𝑊𝐷 = ∑ 𝑊𝑖4𝑖=1𝐷𝑖 Where MWD is mean weight diameter (mm), Wi is the weight of soil in that size fraction, i= 1 to n, where n is the number of size fractions (4) included in the sieve nest including the soil lost through the bottom of the sieve (<0.25-mm), and Di is the diameter of the aggregates left behind on the sieve. 30  2.2.7 Permanganate Oxidizable Carbon  To determine POXC, four subsamples were collected at 0-7.5-cm depth at the inter-row location and composited for each subplot. Samples were taken at the same times as aggregate stability samples. Soils were analyzed according to Weil et al. (2003) and procedures outlined by Culman et al. (2012). A 0.2 M KMnO4 stock solution prepared in a CaCl2 solution with a pH of 7.2, 5 g of air-dried soil was mixed with 2 mL of the KMnO4 solution and 18 mL of water in a falcon tube, put on a shaker with 240 oscillations per minute for 2 minutes, and placed in a dark space to incubate for 10 minutes. After this, 0.5 mL of supernatant was added to 49.5 mL of H2O in a new falcon tube, then stored in the dark until being read. This solution was then analyzed using a 96-well plate with blanks of deionized water and standards of KMnO4 solution on a TECAN Spark® spectrophotometer at 550nm (TECAN Group Ltd., Männedorf, Switzerland). POXC was determined using the following equation:  Equation 2.3:  𝑃𝑂𝑋𝐶 = [0.02 𝑚𝑜𝑙/𝐿 − (𝑎 + 𝑏 𝑥 𝐴𝑏𝑠)]𝑥(9000 𝑚𝑔 𝐶/𝑚𝑜𝑙)𝑥(0.02𝐿 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛/𝑤𝑡) Where 𝑃𝑂𝑋𝐶 is the amount of POXC in mg kg soil-1, 0.02 mol L-1 is the initial solution concentration of KMnO4, 𝑎 is the intercept of the standard curve, b is the slope of standard curve, Abs is the absorbance of unknown soil, 9000-mg C mol-1 is the milligrams of C oxidized by one mole of MnO4 changing from Mn7+ to Mn2+, 0.02-L is the volume of stock solution reacted, and 𝑤𝑡 is the weight of air-dried soil sample in kg. 31    Figure 2.8 Band and Inter-row placement of greenhouse gas collars in 2017 (A) and 2018 (B).  2.3 Statistical Analysis  All data were analyzed using R version 3.4.2 (R Development Core Team, Vienna, Austria). Prior to analysis of data, assumptions of normality and homogeneity of variance were tested using the Shapiro-Wilk test and Bartlett test respectively. If data did not meet these assumptions, they were log10 transformed or raised to the power of two.  A principal component analysis (PCA) was performed on the same properties using the FactoMineR package (Lê, Josse, and Husson, 2008) to help characterize soils in each field. The different fields (productive and unproductive) and soil depths (0-15-cm and 15-30-cm) were analyzed separately for all measurements. To determine differences between yield and soil properties, a linear mixed effects (LME) model and the nlme package version 3.1-131 (Pinheiro et al., 2017) was used with GLSA, fertilizer, and interaction of GLSA and fertilizer as fixed effects, and block as a random effect. The test method used to determine differences across treatments was analysis of variance (ANOVA). To determine differences between MWD, POXC, PAN, crop yield, and GHGs, a LME model and the nlme package version 3.1-131 (Pinheiro et al., 2017) was used with GLSA, fertilizer, and interactions of GLSA and fertilizer, GLSA and time, and fertilizer and time as fixed effects and block as a random effect. A Tukey post-hoc HSD test was performed using the emmeans function (Lenth, 2018) when the ANOVA 32  results showed significant differences between treatments ( = 0.05). To determine differences in pest damage a generalized linear model (GLM) and the polr function in the MASS package (Venables and Ripley, 2002) was used with GLSA, fertilizer, and interaction of GLSA and fertilizer as fixed effects. An F-test generated by ANOVA was used to determine significance of the interaction terms, and a chi-squared test to determine significance of individual factors ( = 0.05).  33  Chapter 3: Results and Discussion   3.1 Soil Aggregate Stability Substantial differences in MWD among treatments occurred in both field types during some periods of the year (Figure 3.1; Table 3.1). MWD varied across the season, with lower values at post-tillage (0.57  0.55-mm) and higher values at post-harvest (2.87  0.04-mm). These results were similar to a local study by Lussier et al. (2019), which found a range of MWD from 0.94 to 2.57-mm. In 2017, the productive field average MWD was higher in the AC and AC + N treatments than all other treatments at mid-season (Figure 3.1 B; Table 3.1), but then in post-harvest, AC treatments were lower (Figure 3.1 C; Table 3.1). No differences in MWD were observed in the unproductive field. In 2018, in the productive field, at post-tillage 3G treatments were significantly higher than 2G (Figure 3.1 D; Table 3.1), and in the unproductive field at post-tillage 3G and 3G + N had higher MWD than AC + N treatments (Figure 3.1 J; Table 3.1). Significant differences from post-harvest and post-tillage support my hypothesis that GLSA improves aggregate stability, but not consistently over time or across the two field types. Interestingly, N fertilized treatments showed minimal impact on MWD.  These results were in contrast to some other studies in the area that showed no aggregate response to short-term GLSAs even prior to incorporation. Principe (2001) found no differences in MWD after 1-year of GLSA compared to adjacent annually cropped fields, while Yates et al. (2017) found no differences in MWD between cropped fields and GLSA after 2-, 3-, 4-, and 6-year. Differences in soil baseline conditions among fields may have affected the response to MWD (Yates et al., 2017). Lussier et al. (2019) found the unproductive field had lower MWD than an adjacent annual crop rotation field prior to incorporation after 1- and 2-years in GLSA likely due to higher levels of Na causing dispersion of soil particles and prevention of aggregate 34  formation. This was similar to my results, where there were fewer differences in MWD between the treatments in the unproductive field, likely due to relatively high Na that prevents aggregate formation from dispersion of clay particles (Agassi, 1981). The difference that I found may have been more pronounced if AC treatment in my study had been in fact continuously cropped rather than being in GLSA for two years and containing GLSA belowground biomass. The lack of disturbance in these treatments, enmeshment of roots, and biomass turnover of roots, fungi, and organic matter from GLSA would likely have contributed to greater aggregate formation than true continuous annual cropping (Tisdall and Oades, 1982). This may explain why there were no MWD differences at post-tillage, but as the season progressed and the GLSA aboveground biomass incorporated into the soil in the 2G treatments decomposed, it contributed to increased MWD over time. My results were consistent with Hermawan and Bomke (1996) and Lussier et al. (2019), which found increases in MWD after 2-years in GLSA. Another study by Riley et al. (2008) in Norway also found increased MWD after 2-year GLSA followed by wheat and barley, and 3-year GLSA followed by barley using a simulated rainfall method to estimate aggregate stability. In 2018, 3G treatments had higher MWD than 2G and AC treatments in both fields, indicating that effects of GLSA on MWD may only persist through one production season, and that improvements to MWD in the unproductive field may occur after three years of GLSA. In the WFRD, increased MWD is most important in the post-harvest season, as it allows soil to withstand the heavy rainfall typically experienced in the winter. This was observed in my study in 2017, 2G and bean crops increased MWD at post-harvest in the productive field, but in 2018 potato crops and 3G or 2G treatments did not show improved MWD at post-harvest. This difference may be due to seed bed preparation in the WFRD for potatoes, as potato crops 35  typically get more intensive tillage compared to other crops, which has been found to decrease aggregate stability (Six, 1999).   Figure 3.1 Comparison of average (n=3) mean weight diameter (MWD) of water-stable soil aggregates between treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at post-tillage, mid-season, and post-harvest in 2017 and 2018. Panels A-F are for the productive field, and G-L are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05).               36    Table 3.1 Analysis of variance results for mean weight diameter (MWD) at post-tillage, mid-season, and post-harvest in 2017 and 2018 for the productive and unproductive fields. Bolded values indicate statistical significance (p < 0.05).  Year Timing Field GLSA Fertilizer GLSA*Fertilizer F p-value F p-value F p-value 2017 Post-Tillage Productive  0.21 0.65 0.34 0.57 0.17 0.69 Unproductive 0.55 0.46 0.59 0.45 0.33 0.57 Mid-Season Productive 11.95 <0.01 1.61 0.23 0.65 0.44 Unproductive 0.16 0.69 1.47 0.24 0.02 0.90 Post-Harvest Productive 27.13 <0.01 2.46 0.15 4.99 0.05 Unproductive 0.84 0.47 0.28 0.60 2.43 0.13 2018 Post-Tillage Productive 5.27 0.03 0.18 0.68 0.99 0.40 Unproductive 9.20 0.01 2.73 0.13 2.12 0.17 Mid-Season Productive 1.12 0.35 0.25 0.63 2.23 0.15 Unproductive 0.54 0.60 1.71 0.22 0.10 0.91 Post-Harvest Productive 2.71 0.11 4.76 0.05 2.26 0.16 Unproductive 0.18 0.84 0.64 0.44 0.43 0.66  3.2 Permanganate Oxidizable Carbon I observed significant differences in POXC among treatments in the productive field in 2017 and in both fields in 2018 (Figure 3.2; Table 3.2). Values ranged from 605  93 to 816  50=mg POXC-kg-1 soil, and were similar to those found in a local study by Lussier (2018) that ranged from 460 to 980-mg kg-1. In the productive field, in 2017, at post-tillage 2G had higher POXC than AC treatments (Figure 3.2 A; Table 3.2), and at post-harvest both fertilizer and GLSA × fertilizer were significant and the 2G + N had lower POXC than all other treatments (Figure 3.2 C; Table 3.2). In the 2018 mid-season, GLSA was again significant, and POXC was higher in the 3G + N compared to the AC and AC + N treatments, but the 3G was only higher than the AC treatment (Figure 3.2 E). In 2017 in the unproductive field, there were no differences between the 3G and 2G treatments regardless of fertilizer rate applied. However, in 2018, at post-tillage, POXC was higher in the 3G than 2G treatments for both fertilizer treatments but not distinguishable from the AC (Figure 3.2 J; Table 3.2), and at post-harvest, there was higher POXC in the N fertilized treatments (Figure 3.2 J; Table 3.2). 37  I observed minimal differences between treatments in 2017. This was consistent with what Lussier (2018) found, comparing 2-year-old GLSAs to adjacent annually cropped fields in the fall before incorporation. Again, differences may have been greater in 2017 had the AC treatment not included GLSA belowground biomass. The increased POXC in 2G and 3G treatments supports my hypothesis that a longer period of GLSAs increases this labile fraction of carbon. Similarly, Mirsky et al. (2008) found increases in POXC in a silt loam soil in Pennsylvania, USA between crop rotation treatments of 4 years of corn followed by 4 years of alfalfa hay, and another treatment of corn-oat-winter wheat followed by 2 years of red clover in comparison to continuous corn treatments. A study by Lewis et al. (2011) in Pennsylvania, USA, also found increases in POXC after 3-years of GLSA in combination with reduced tillage compared to 3-year GLSA in conventional tillage. However, other studies found that changes in POXC under GLSA management can take between 5-10 years (Burke et al., 1995; O’Brien and Jastrow, 2013). POXC is known to have a close relationship with both soil aggregate stability, as it is a precursor to microbial biomass and stable SOM, and PAN, by acting as primers for decomposition (Berthrong et al., 2013; de Graaff et al., 2010; Kaye and Hart, 1997). Therefore, the difference in POXC at different times in the season may be indicative of increased N mineralization or immobilization into microbial biomass.     38   Figure 3.2 Comparison of average (n=3) permanganate oxidizable carbon (POXC) between treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at post-tillage, mid-season, and post-harvest in 2017 and 2018. Panels A-F are for the productive field, and G-L are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05).  Table 3.2 Analysis of variance for permanganate oxidizable carbon (POXC) at post-tillage, mid-season, and post-harvest in 2017 and 2018 for the productive and unproductive fields. Bolded values indicate statistical significance (p < 0.05).  Year Timing Field GLSA Fertilizer GLSA*Fertilizer F p-value F p-value F p-value 2017 Post-Tillage Productive 0.53 0.02 0.02 0.90 0.02 0.90 Unproductive 1.24 0.31 0.00 1.00 0.00 1.00 Mid-Season Productive 0.07 0.80 0.22 0.66 1.62 0.25 Unproductive 1.58 0.26 0.34 0.58 0.98 0.36 Post-Harvest Productive 4.33 0.08 13.63 0.01 8.65 0.03 Unproductive 0.79 0.41 0.70 0.43 0.16 0.71 2018 Post-Tillage Productive 1.90 0.20 0.88 0.36 0.23 0.80 Unproductive 8.29 <0.01 1.03 0.34 0.08 0.93 Mid-Season Productive 13.99 <0.01 0.54 0.48 0.08 0.92 Unproductive 1.49 0.27 1.27 0.29 0.05 0.95 Post-Harvest Productive 1.46 0.28 5.09 0.05 2.33 0.15 Unproductive 1.33 0.31 8.03 0.02 0.06 0.94  3.3 Plant Available Nitrogen and Residual Soil Nitrogen  In addition to N fertilizer application, the aboveground biomass of GLSA contributed N to the 2G and 3G fields in varying amounts depending on the field and year (Table 2.2). In 2017 in the productive and unproductive field, aboveground biomass from the 2G treatments 39  contributed 51.6 and 29.0-kg N ha-1, respectively. In 2018 in the productive and unproductive field, aboveground biomass from the 3G treatments contributed 63.3 and 27.6-kg N ha-1, respectively. Concentration of NH4+-N varied between treatments at multiple times across both years and fields following incorporation of GLSA (Figure 3.3; Appendix C; Appendix D). In the productive field in 2017 at 0-15-cm, in late July there was higher NH4+-N in AC treatments (Figure 3.3 A), and all other differences were due to N fertilization. NH4+-N peaked in early August before sharply declining in late August likely due to crop uptake. In 2018 in the productive field, there were no differences in NH4+-N across the season at either depth (Figure 3.3 C and Figure 3.3 D). In 2017 in the unproductive field at both depths, NH4+-N differed significantly for several dates (Figure 3.3 E and Figure 3.3 F). At the 0-15-cm depth, NH4+-N in the 2G + N treatment was significantly higher in July, and AC + N was significantly higher in early August. In 2018 in the unproductive field at the 0-15-scm depth, NH4+-N decreased significantly in September compared to AC + N and 2G + N treatments. Seasonal average values ranged from 0.82  0.31 to 16.68  2.98-kg NH4+-N ha-1, and was significantly different in the post-season of 2017 in the productive field and in the 2018 production season in both fields at 0-15-cm (Figure 3.4). However, these differences were due to N fertilization, rather than the GLSA treatment.   Concentration of NO3--N varied among treatments at multiple times in both fields following incorporation of GLSA (Figure 3.5; Appendix C; Appendix D). In 2017 in the productive field at 0-15-cm depth, average NO3--N increased in 2G + N treatments earlier in the season, but was higher throughout the rest of the season in AC treatments (Figure 3.5 A), varying similarly at the 15-30-cm depth (Figure 3.5 B). In 2018 in the productive field at the 0-15-cm depth, differences in average NO3--N were due to N treatments, except before harvest, when 2G 40  + N and 3G + N were higher than AC + N and 2G treatments (Figure 3.5 C). In 2017 in the unproductive field at 0-15-cm depth, 2G treatments had higher NO3--N earlier in the season, with AC + N higher later in the season (Figure 3.5 E), varying similarly at the 15-30-cm depth (Figure 3.5 F). In 2018 in the unproductive field at 0-15-cm depth significant differences were due only to N treatments (Figure 3.5 G). In 2017 in the productive field in the post-season at 0-15-cm depth, average NO3--N was higher in the AC + N treatment than the 2G + N followed by the 2G and finally the AC treatment (Figure 3.6 B). Seasonal average values ranged from 4.88  1.39 to 27.53  10.68-kg NO3--N ha-1. In 2018, in both fields, differences in production season average NO3--N were due to N treatments only (Figure 3.6 C and Figure 3.6 G). Contrary to my hypothesis, there were no differences in average PAN over the production season between 2G and 3G treatments compared to AC treatments. This is in contrast to the confounding results of a local study by Walji (2017) using an on-farm experiment comparing 3-year GLSAs to a control with aboveground biomass removed. In that study, in 2015 a 3-year GLSA contributed 18-kg N ha-1 to subsequent crops, but in the 2016, paired annually cropped fields contributed 20-kg N ha-1 more compared to a 3-year GLSA. Other studies have found that GLSAs may contribute N to subsequent crops, especially when GLSA seed mixture included large proportion of legumes. Although the DF&WT GLSA seed mix included red clover as the only legume species, its proportion was small (2% of seed mix by weight). This could have contributed to the lack of increase in PAN as reported in other studies. For example, a study by Chalmers et al. (2001) on six contrasting soil types across the UK, found that 3- and 5-year GLSAs with 21% white clover and 79% perennial rye grass by seed weight contributed 14-33-kg N ha-1 to subsequent crops, and after one year of crop production continued to contribute an additional 12-18-kg N ha-1. Nevens and Reheul (2002) found that a 3-year GLSA of perennial 41  rye grass on a sandy loam soil in Belgium contributed 125-150-kg N ha-1 in the first year and 52-81-kg N ha-1 in the second year following GLSA incorporation in the soil. Johnston et al. (1994) compared N and yield in 1-6-year-old clover and ryegrass GLSAs, then fit exponential and linear curves to wheat and potato yields to find optimum fertilizer and GLSA combinations. They found that 1-6-year-old GLSAs decreased N requirements to maintain optimum crop yields in wheat to 174-48-kg N ha-1 and in potato to 150-137-kg N ha-1. Contrary to my hypothesis, the post-harvest NO3--N levels were not significantly higher in GLSA treatments. In the productive field, AC + N had higher post season nitrate compared to the 2G + N. This means that GLSA could have the potential to reduce post-harvest NO3--N, which is more likely to be lost to the environment in the winter due to the high levels of precipitation in the WFRD. Walji (2017) had reported 13-kg more post-season NO3--N ha-1 in 2015, and no significant differences in 2016 comparing 3-year GLSAs to paired continuous annually cropped fields. My results were similar to the findings in 2016, except for the productive field in 2017, which found lower NO3--N in 2G + N compared to AC + N. This could be due to the higher C:N ratio of biomass causing net immobilization at post-harvest (Allison, 1973; Addiscott et al., 1991). Several studies found higher residual soil NO3-N after GLSA implementation. For example, Bernsten et al. (2006) found incorporation of a 3-year GLSA post-harvest soil NO3--N increased by 63-216-kg NO3--N ha-1 in the first year after incorporation and increased by 61-235-kg NO3--N ha-1 in the second year after incorporation on loamy sand soils in Denmark. A 3-year-old grass clover GLSA that had been cut and grazed, resulted in 22 and 50-kg NO3--N ha-1 greater leaching losses, respectively in comparison to continuously cropped corn in a study by Cougnon et al. (2019) carried out in Belgium on a sandy loam soil with 836-mm of rainfall. 42  Several confounding variables exist in my study, which may have influenced the limited PAN benefits. The soil properties, such as relatively high Na, total soil C, and total soil N in the unproductive field and especially high EC (Table 2.1) along with higher GLSA aboveground biomass and higher C:N ratio of GLSA aboveground biomass (Table 2.2) likely affected PAN. High levels of Na like those in the unproductive field have been found to have a negative effect on nitrogen-fixing bacteria and plant growth, which could reduce PAN levels in soil (Delgado et al., 1994). Although more N was supplied by aboveground biomass in the productive field, the biomass C:N ratio of 41.8 (in 2017) and 33.0 (in 2018) most likely led to immobilization of PAN, while the biomass C:N ratio of 27.6 (in 2017) and 26.8 (in 2018) in the unproductive field may have resulted in greater mineralization of PAN (Allison, 1973; Addiscott et al., 1991). Additionally, differences in physiology of bean and potatoes, previous cropping history (Appendix E), as well as other management practices that preceded my study (e.g., addition of manure) could have also affected PAN in these fields. While my results showed the overall amount of PAN was unchanged by GLSA, my hypothesis that GLSA will provide an increase in early season PAN was supported. In the unproductive field in 2017, daily NH4+-N and NO3--N values seemed to peak earlier in the season for 2G treatments compared to AC treatments. This was similar to findings of Davies (2001) in Scotland where grazed grass-clover GLSAs with a lower C:N ratios had higher NO3--N 2-3 weeks after incorporation.    43    Figure 3.3 Comparison of average ammonium (NH4+-N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15 and 15-30-cm depths in 2017 and 2018. Panels A-D are for the productive field, and E-H are for the unproductive field. Red asterisks indicate significant differences for that date (p<0.05).  44   Figure 3.4 Comparison of average (n=3) ammonium (NH4+-N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15-cm in 2017 and 2018 for the production and post-season. Panels A-D are for the productive field, and E-H are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05). 45    Figure 3.5 Comparison of average nitrate (NO3-N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15 and 15-30-cm depths in 2017 and 2018. Panels A-D are for the productive field, and E-H are for the unproductive field. Red asterisks indicate significant differences for that date (p<0.05).  46   Figure 3.6 Comparison of average (n=3) nitrate (NO3--N) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) at 0-15-cm in 2017 and 2018 for the production and post-season. Panels A-D are for the productive field, and E-H are for the unproductive field. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05).   3.4 Crop Yield and Quality I found significant differences among treatments in crop yield for both beans (2017) and potatoes (2018) but the pattern was not consistent for the two crops or between the productive and unproductive fields (Figure 3.7). In 2017 in the productive field, bean yield was strongly affected by GLSA (p<0.01) but not by fertilizer N treatment (p=0.14) (Table 3.3). Average bean 47  yield ranged from 867  125 to 2,025  337-kg ha-1, and was higher in 2G compared to AC (Figure 3.7 A; Table 3.3). Bean yields were lower than average snap bean yield in BC of 6,090-kg ha-1 (Statistics Canada, 2012), which may have been due to the relatively low seeding rate. Although rhizobia bacteria are able to contribute biologically fixed N to beans, the amounts (35-kg N ha-1 in early maturing beans to 109-kg N ha-1 in late maturing beans) are lower compared to peas and soybean. Herridge and Danso (1995) found that without additional fertilizer, biological N fixation was not sufficient to obtain optimum yields. However, in my study there were no significant differences in bean yields due to fertilizer treatments. In 2018 in the productive field, potato yield was strongly affected by fertilizer (p<0.01) and weakly affected by GLSA (p=0.07) (Table 3.3). Potato yield ranged from 14,585 660 to 35,344  5,392-kg ha-1, with the AC + N treatment significantly higher than the 3G and no other differences among treatments.  In 2017 in the unproductive field, no significant differences in bean yields were found, which had marketable yield far lower than the productive field (Figure 3.7 B; Table 3.3). In 2018 in the unproductive field, potato yield was, like the productive field, affected by fertilizer (p=0.01) and weakly affected by GLSA (p=0.07) (Table 3.2). Potato yield was similar to the productive field and ranged from 19,922  5,121 to 36,945  5764-kg ha-1. However, as opposed to the productive field, in the unproductive field the 3G + N was significantly higher than AC and 2G treatments (Figure 3.7 D; Table 3.3). Potato yields for some treatments were lower than those found in a local study by Walji (2017) following the incorporation of 3-year GLSAs, which ranged from 33,156 to 38,201-kg ha-1, and less than average yield found in BC which ranged from 30,710 to 35,310 from 2013-2017 (Statistics Canada, 2018).  As was expected, 2G treatments bean yield was higher compared to the AC treatments in the productive field, which may be due to the large amount of C and N supplied to the 2G 48  treatments through GLSA aboveground biomass (Table 3.3). This, however, did not hold true in the unproductive field for beans, which may be due to intolerance of bean plants to Na, which can have a negative effect on bean plant physiology (Seemann and Critchley, 1985). There were no differences in potato yields in either field (Table 3.3) due to the GLSA. This was in agreement with the findings of Walji (2017), who also found no significant difference in yields after 3-year GLSAs compared to continuous annual cropping for potato and broccoli crops. Several other studies have found that GLSAs can improve crop yields. For example, Brozyna et al. (2013) found 14% higher dry matter yields for crops with manure application after one year of grass-clover GLSA on organically managed fields in a rotation with various cash crops and cover crops on loamy sand soil in Denmark. Another study by Johnston et al. (1994) on a sandy loam soil in the UK with 1-6-year-old clover and ryegrass GLSAs, found wheat yield increased in 1-3-year-old GLSAs but plateaued after 3-year GLSAs, and small but significant increases were found in the year following incorporation for potatoes in all 1-6-year-old GLSAs. While Nevens and Reheul (2002) found that a 3-year GLSA followed by 3-year silage-corn rotation increased corn yields by 84% compared to a paired control field without fertilizer on sandy loam soil in Belgium.  The timing of PAN to crops has been found increase yields in potatoes when synced to N uptake needs at the beginning of the season and mid-season (Sanderson and White, 1987; Stark et al., 2004). However, in my study PAN typically varied only slightly at the beginning of the season and was not significantly different throughout the growing season. This may explain why I did not see differences between GLSA treatments and crop yields. Similarly, the C:N ratio as described in section 3.3, was relatively high, which could have increased N immobilization during key periods of crop development, leading to decreased crop yields.  49  I found no clear pattern in crop quality parameters, namely N concentration and pest damage, among treatments (Table 3.4). Bean N concentration was strongly affected by GLSA in the productive field ( Table 3.5), with higher N concentration in the AC treatments compared to 2G. Bean N concentration was strongly affected by N fertilizer in the unproductive field ( Table 3.5), with higher N concentration in the fertilized treatments. Potato N concentration did not significantly differ in the productive field, but in the unproductive field potato N concentration was significantly higher in the 3G compared to AC treatments (Table 3.4), with GLSA and GLSA × fertilizer significant in the model ( Table 3.5). These results support my hypothesis that 2G and 3G can improve crop quality compared to AC treatments. This is similar to what Watson et al. (2011) found, with higher N concentration in oats after 4 years of a grass and clover GLSA compared to 3 years on a sandy loam soil in the UK. Eriksen et al. (2006) also found increased N concentration of wheat from 1-, 2-, and 8-year-old clover and ryegrass GLSAs compared to continuously cropped cereal crops on a sandy loam soil in Denmark. While pest damage was low (<10%) in both fields, results were somewhat confounding. There were no significant differences in potato pest damage among treatment means (Table 3.4) yet, potato pest damage was strongly affected by GLSA in the productive field and by fertilizer in the unproductive field. Wireworm pest damage was also lower in the productive field compared to the unproductive field. As GLSAs have been found to harbor pests during the establishment and growth phases (Keiser et al., 2012; Parker and Howard, 2001) it was not surprising to find some indication of pests in experimental plots surrounded by GLSA on all sides.   50   Figure 3.7 Comparison of average (n=3) test crop yield for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) in 2017 and 2018. For beans in the (A) productive field and (B) unproductive field and potatoes (C) in the productive field and (D) unproductive yield. Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05).   Table 3.3 Analysis of variance of bean (2017) and potato (2018) yield in the productive and unproductive field. Bolded values indicate statistical significance (p < 0.10).  Year Field df GLSA Fertilizer GLSA*Fertilizer F p-value F p-value F p-value 2017 Productive 10 2.56 <0.01 1.36 0.14 1.36 0.26 Unproductive 10 0.35 0.55 2.66 0.12 0.04 0.84 2018 Productive 10 3.46 0.07 12.86 <0.01 0.29 0.76 Unproductive 10 3.40 0.07 8.9 0.01 0.24 0.79      51  Table 3.4 Comparison of average (n=3) potato pest damage, potato N concentration, and bean N concentration (n=3) in the productive and unproductive. Bolded values and different letters show significant differences between treatments by Tukey’s Honest Significant Difference test (HSD, p<0.05).   Field Treatment Bean N Concentration (%) Potato Pest Damage  Potato N concentration (%) Productive  AC 3.12 (0.19) a 0 (0) a 1.2 (0.04) a AC + N 3.4 (0.15) a 0 (0) a 1.32 (0.09) a 2G  2.68 (0.19) a 0 (0) a 1.21 (0.11) a 2G + N 2.62 (0.2) a 0.67 (0.33) a 1.43 (0.10) a 3G  n/a  1.33 (0.67) a 1.34 (0.03) a 3G + N n/a   1 (0.58) a 1.25 (0.09) a Unproductive AC 3.43 (0.27) a 0.67 (0.33) a 1.05 (0.08) b AC + N 3.78 (0.09) a 0.33 (0.33) a 1.2 (0.07) ab 2G  3.21 (0.02) a 1 (0.58) a 1.18 (0.10) ab 2G + N 3.48 (0.06) a 0 (0) a 1.22 (0.04) ab 3G  n/a  1.67 (0.67) a 1.37 (0.10) a 3G + N n/a   0.67 (0.33) a 1.2 (0.06) ab  Table 3.5 Analysis of variance of potato pest damage, potato nitrogen (N) concentration (%), and bean N concentration (%) in the productive and unproductive field. Bolded values indicate statistical significance (p < 0.10).  Crop Quality Field GLSA Fertilizer GLSA*Fertilizer F p-value F p-value F p-value Bean N Concentration (%) Productive 11.47 0.01 0.36 0.57 0.88 0.38 Unproductive 3.14 0.13 4.53 0.07 0.05 0.83 Potato Pest Damage Productive 4.88 0.03 0.13 0.73 0.88 0.45 Unproductive 1.95 0.19 5.98 0.03 0.49 0.63 Potato N Concentration (%) Productive 0.29 0.75 1.51 0.25 1.71 0.23 Unproductive 3.29 0.08 0.02 0.90 3.11 0.09   3.5 Greenhouse Gas Emissions  3.5.1 Carbon Dioxide  CO2 emissions varied significantly for several dates (Figure 3.8 A), and during the 2018 production and non-production season, with most emissions occurring during the production season (Figure 3.9 A-D). Average daily fluxes ranged from -1.14 to 50.40-kg CO2-C ha-1 (Figure 3.8 A) (Appendix F). Seasonally cumulative CO2 emissions ranged from 2,410  77 to 3,664  173-kg CO2-C ha-1 in the production season and 1,451  95 to 2,374  204-kg CO2-C ha-1 in the non-production season (Figure 3.8 D). There were no significant differences among treatments in 52  2017 (Figure 3.9 A and B; Appendix F), but in the 2018 production season the 2G + N and 3G + N treatments produced significantly more CO2 emissions (Figure 3.9 C) and in the 2018 non-production season with the 2G + N treatment producing significantly more CO2 emissions compared to 3G + N (Figure 3.9 D). These values were similar to annual emissions of CO2 reported for continuously cropped fields with potato/legume rotation recorded by Thiel et al. (2017) (5,700 to 6,940-kg CO2-C ha-1) in the WFRD. Only the results from the 2018 production season support my hypothesis that GLSAs would increase CO2 respiration after incorporation. While the results of 2017 suggest that there are no differences in CO2 respiration following GLSA compared to AC, it may be that in 2017 the belowground biomass incorporated in the AC treatment masked the effect of decomposition of additional C from GLSA aboveground biomass. It could have been that it was not until this belowground biomass was consumed by microbial populations that the differences became evident. The 2018 results were consistent with other studies that observed greater CO2 emissions following longer-term GLSAs. In an incubation study on a loamy sand soil in Denmark by Acharya et al. (2012) found no differences in CO2 emissions in 1- to 4-year-old GLSAs with different manure fertilizer application rates compared to continuous annual cropping but did find higher CO2 emissions in soils from a 17-year-old GLSA. Similarly, Ruan and Robertson (2013) found significant increases in CO2 emissions following conversion of fields in CRP for 22 years to conventional tillage on a loamy sand soil in Michigan, USA which was attributed to increased C from GLSA biomass. Our results suggest that much of the C gained during short-term GLSAs may be lost in the production season following incorporation.    53  3.5.2 Methane  I found that CH4 emissions did not exhibit many differences for daily emissions (Figure 3.8 B), and there were no differences between seasonal emissions (Figure 3.9 E-H). Daily average CH4 fluxes ranged from -17 to 25-g CH4-C ha-1 (Figure 3.8 B) and significant differences were found only for July 27, 2018 (Appendix F) with no discernable pattern between treatments. Seasonal cumulative fluxes were small and not significantly different, ranging from -0.68  0.24 to 0.54  0.35-kg CH4-C ha-1 (Figure 3.9 E-H). These values were lower than annual emissions of CH4 (2.81 to 1.61-kg CH4-C ha-1) reported for continuously cropped fields with potato/legume rotation recorded by Thiel et al. (2017) that was also carried out in the WFRD. It was not entirely surprising that I found no differences in seasonal cumulative CH4 emissions. Similarly, Ruan and Robertson (2013) found no significant differences in CH4 emissions after fields in CRP for 22 years were converted to soybean on loamy soils in Michigan, USA. On the other hand, Dobbie and Smith (1996) found decreased CH4 oxidation after 3 years in GLSA compared to continuously cropped fields on loamy sand soils in the UK. These differences were found largely to be a function of edaphic conditions on the site including water content, temperature, and diffusivity, which did not seem to vary widely in my study from year to year or between treatments (Figure 2.1; Appendix B). While ammonium sulfate fertilizers which were applied on my fields have been found to inhibit CH4 oxidation, resulting in increased methanogenesis (Hanson and Hanson, 1996), I did not observe any differences between the N fertilized and unfertilized AC. These factors may explain why there were no differences in CH4 emissions between treatments.   54  3.5.3 Nitrous Oxide   I observed that N2O daily fluxes varied across several dates (Figure 3.8 C), and cumulative seasonal emissions were different across in all season with 2G treatments typically emitting more N2O (Figure 3.9 I-J). Daily average N2O fluxes ranged from -1.44 to 208-g N2O-N ha-1 (Figure 3.8 C), and seasonal emissions ranged from 0.17  0.03 to 0.82  0.31-kg N2O-N ha-1 in the production season and 0.45  0.11 to 4.15  0.65-kg N2O-N ha-1 in the non-production season (Appendix F). Most emissions occurred during the 2017 non-production season (May – October) (Figure 3.9 D). Cumulative N2O emissions were higher in 2G + N compared to AC and AC + N in the 2017 production season and non-production season (Figure 3.9 I-J). In the 2018 production season 2G + N treatments emitted more N2O emissions than AC and 3G + N, and AC produced more emissions than 3G (Figure 3.9 K). In the 2018 non-production season 2G + N produced more N2O emissions than all other treatments (Figure 3.9 L). These values were lower than annual emissions of N2O reported for continuously cropped fields with potato/legume rotation in the WFRD (7.45 to 8.17-kg N2O-N ha-1) (Thiel et al., 2017). Results in 2017 and 2018 were confounding, supporting my hypothesis in 2017 as 2G + N increased N2O emissions, but not in 2018 as 3G + N had lower N2O emissions than the other treatments. A study completed by Ball et al. (2002) in Aberdeen, UK, found that 3-year-old grass and clover GLSA decreased emissions of N2O compared to continuously cropped brassica fields after tillage. However, Ruan and Robertson (2013), found that tillage and fertilization of fields in CRP for 22 years led to higher N2O emissions compared to fertilized annual crops nearby. Abraha et al. (2018) also found increased 5-year cumulative N2O emissions in fields converted to continuous corn after 22 years in CRP. The increased N2O emissions in both of these studies were attributed to decomposition of the increased C and N from the CRP, which has been shown 55  to increase denitrification (Luo et al., 1999). Similarly, the 2G and 3G treatments contributed to soil C though aboveground biomass, with more GLSA biomass in 2017 compared to 2018 (Table 2.2). This may correspond to the higher N2O emissions experienced in 2017 compared to 2018. Similarly, the amount of C contained in crop residues could have been a confounding factor, as bean crop residue typically has a lower ratio of C:N (<25:1) (De Neve and Hofman, 1996) compared to potatoes (37:1) (Huang, 2004). This lower C:N ratio has been shown to increase N2O emissions due to more rapid decomposition (Huang, 2004; Shan and Yan, 2013; Baggs et al., 2000). These results suggest that 2-year GLSAs could significantly contribute to increased N2O emissions, while 3-year GLSAs were found to decrease emissions in both seasons after GLSA. However, these differences may be affected by type of crop and crop residue management.  56   Figure 3.8 Comparison of average (n=3) daily gas fluxes of carbon dioxide (CO2) (A), methane (CH4) (B), and nitrous oxide (N2O) (C) from 2017 to 2019 in the productive field for treatments of annually cropped (AC), annually cropped with N fertilizer (AC + N), 2-year grassland set-aside with N fertilizer (2G + N), and 3-year grassland set-aside with N fertilizer (3G + N). Red asterisks indicate significant differences for that date by Tukey’s Honest Significant Difference test (HSD, p<0.05).  57   Figure 3.9 Comparison of average (n=3) cumulative seasonal GHG emissions over the 2017-2019 production and non-production season for treatments of annually cropped (AC), annually cropped with N fertilizer (AC + N), 2-year grassland set-aside with N fertilizer (2G + N), and 3-year grassland set-aside with N fertilizer (3G + N). Panels A-D are for carbon dioxide (CO2), panels E-H are for methane (CH4), and panels I-L are for nitrous oxide (N2O). Different letters indicate significant differences by Tukey’s Honest Significant Difference test (HSD, p<0.05).     58  3.6 Implications for Local Farmers   The goals of my study were to determine if 2- and 3-year GLSAs can improve soil quality and crop yield. My study found similar results to others in the WFRD, that these GLSAs can potentially improve soil quality and yields depending on crop type and whether the soils are high in Na. Similar to other studies, I found that GLSAs have the potential to improve soil quality in terms of MWD in the first year of incorporation of 2G and 3G treatment, and POXC in 3G treatments. However, these effects seem to be dependent on inherent soil properties and soil management associated with subsequent crop rotations. My results suggest that farmers with fields affected by high Na content (i.e., unproductive fields), should keep GLSA for at least 3 years, as no benefits to MWD and POXC were seen for the unproductive field with 2G. Use of GLSAs in conjunction with other beneficial management practices (i.e., subsoiling, surface laser leveling, subsurface drainage, reduced tillage, and cover cropping) may yield further improvements in unproductive fields. It may also be useful to grow crops that require less tillage than the potatoes (2018 test crop) to extend the benefits of GLSA-improved soil quality to subsequent cropping seasons.  Contrary to my hypothesis and other studies, I am unable to conclude that 2G and 3G treatments contribute to increasing average PAN through the production season or in the post-season. This is likely due to the low proportion of N-fixing species (i.e., red clover) in the GLSA seed mix. However, results indicated that GLSAs could have potential to increase N mineralization earlier in the season compared to continuous cropping. My findings that 2G + N had lower concentrations of NO3—N compared the AC + N treatment in the 2017 post-harvest may suggest that the high C:N ratio of GLSA biomass may help prevent N leaching during winter rainfall and that less N application is required for these crops. Although in 2017, the 2G 59  treatments increased bean yield in the productive field, it did not in the unproductive field, nor did the 2G or 3G treatments improve potato yield. This suggests that the GLSAs potential to improve crop yields likely depends on the selection of subsequent crop as well as the soil properties at the time of GLSA establishment. My results showing increases in both CO2 and N2O following GLSA suggests ascribing any C mitigation benefit to GLSA requires a more thorough GHG accounting. Given that I observed some benefits to the subsequent production crops and no negative impacts, this study supports the continued use of short-term GLSAs as a conservation practice in the WFRD to improve soil quality and provide other benefits associated with crop rotation and perennial cover. It should be noted that GLSAs could be used in combination with other beneficial management practices to further increase soil quality and improve yields.    60  Chapter 4: General Conclusions  4.1 Conclusions  The DF&WT carries out the GLSA Stewardship program with an aim to provide farmers in the WFRD with a conservation practice to increase soil quality, provide wildlife habitat, and help farmers transition to organic production. This research was completed to evaluate the effects of 2- and 3-year-old GLSAs on MWD, POXC, PAN, crop yield and quality, and GHGs in subsequent cropping seasons through a controlled field experiment on one productive and one unproductive field in the WFRD. I found that there is potential for 2G to improve MWD at post-harvest by 0.97-1.43-mm in the productive field, and for 3G to improve MWD at post-tillage by 0.22-0.59-mm in both productive and unproductive fields, in the year of GLSA incorporation. These results varied based on soil properties and management practices used for crop production. MWD increased in the productive field after 2G and 3G, but for the unproductive field, increases were only found after 3G. I found increased POXC in 3G by 96-112-mg kg soil-1 compared to annually cropped treatments at post-tillage for the unproductive field, and at mid-season for the productive field. This suggests that 3G may increase POXC compared to 2G and AC treatments in both fields, and that the timing of these increases may be influenced by soil conditions before GSLA establishment. I found no significant differences in average PAN between 2G and 3G treatments compared to AC. I also found lower residual soil NO3--N in 2G + N compared to AC + N in 2017 in the productive field; however, there were no differences in residual soil NO3--N in 2018. This suggests that GLSAs in the WFRD do not supply as much PAN to subsequent crops as other GLSA programs, and do not contribute to residual soil NO3--N. This may be due to the 61  relatively low abundance of clover in the GLSA seed mix, which limits the amount of N supplied through GLSA biomass. Furthermore, the high C:N ratio of GLSA biomass can reduce PAN through immobilization of N. However, there was an increase in NO3--N and NH4+-N early in the season for 2G treatments in 2017. This early increase may have been associated with rapid turnover of biomass after GLSA incorporation. Although benefits to average PAN were not found, there was an increase in the yield of beans in 2017 in the productive field associated with 2G treatments. 2G+N and 2G treatments increased bean yield by 450 and 760-kg ha-1 compared to AC + N and AC treatments respectively. This may be due to N benefits that were not captured, from biological nitrogen fixation, or may be due to other soil quality factors associated with GLSA. Crop N results were not consistent, with N concentration increasing due to GLSA in the productive field for beans, and in the unproductive field for potatoes. This suggests that there is potential for GLSA to improve crop quality in terms of N concentration in the first and second year after incorporation. Pest damage to potatoes was negligible (<10%) and not significantly different between treatment means; however, results were confounding, with increased in the productive field due to GLSA and decreased due to fertilizer in the unproductive field. Further work using plots that are better isolated from neighboring GLSA would help determine if this is likely to be a problem for farmers.  In this study, cumulative seasonal CO2 emissions increased in the 2018 production season for 2G + N and 3G + N treatments by 1071 and 1043-kg CO2-C ha-1 compared to AC + N treatment. Interestingly, CO2 emissions were lower in the unproductive season in 3G + N compared to 2G + N treatments. There were no significant differences between cumulative seasonal CH4 emissions between treatments. N2O emissions were significantly different between treatments; however, differences were relatively small. Interestingly, fluxes of N2O from 3G + N 62  treatments were typically lower than 2G + N treatments, while 2G + N treatments remained relatively high throughout both years. These results suggest that 2G + N contributes to GHGs in the production and non-production season in the first and second year after incorporation, that 3G + N contributes to GHGs in the production season following incorporation, but not the non-production season, and that N fertilizer application on AC treatments did not significantly increase GHGs. 4.2 Strengths and Challenges of Research My study utilized two experimental sites, which presented both benefits and challenges. Operating on experimental sites as opposed to working farms in the WFRD allowed me to control many aspects of field work including: timing of plot establishment, fertilizer rates, crop management, and sampling times. Results are therefore more likely to reflect actual differences in GLSAs and be less influenced by differences in crop management. I was also able to operate on two representative field types of the WFRD, which should offer realistic and relevant results to farmers and policy makers in the area. One major limitation of the study is that I was not able to establish a true control of annual cropping within the completely randomized block design, as GLSA was seeded across both fields in 2015. Although GLSA aboveground biomass was removed, the effects of the belowground biomass from GLSA were still present in the AC treatments. However, in 2018, after one production season the AC treatment was likely more similar to true annual cropping, because of tillage and decomposition of SOC. This limitation means that the AC treatment likely has some beneficial effects from the GLSA belowground biomass and turnover of GLSA root and shoot biomass from previous two years, which could have masked treatment effects in my study, but these effects are likely less in 2018. Also, because only one representative productive 63  and unproductive field were included in the study, I am limited to making conclusions about only these two study sites, and results may not reflect how all fields in the WFRD respond to GLSA.  4.3 Directions for Future Research Future research should remain a collaborative effort between farmers in the WFRD, the DF&WT, and researchers to better understand the effects of short-term GLSA on crop productivity and soil quality. This type of collaborative research approach is important for providing useful insight for farmers, and the translation of findings into effective policy making decisions. Since my study was only able to examine the effects of GLSA on two crop types, further study on the effects of GLSA on other crop types could be useful to determine how best to utilize and extend benefits of GLSAs. I was only able to monitor the effect of GLSAs on GHGs in the productive field following incorporation of GLSA; however, it would be useful to measure GHGs during initial establishment and growth phases of GLSA to assess the net impacts of GLSA on GHGs. A paired laboratory study could be completed to examine GHGs and N mineralization rates under controlled settings with variable levels of N fertilizer application and GLSA biomass to further understanding of GLSAs in the WFRD.64  Bibliography  Abraha, M., Gelfand, I., Hamilton, S. K., Chen, J., and Robertson, P. G. (2018). Legacy effects of land use on soil nitrous oxide emissions in annual crop and perennial grassland ecosystems. Ecological Applications, 28(5), 1362–1369. https://doi.org/10.1002/eap.1745 Addiscott, T., Whitmore, A., and Powlson, D. (1991). Farming, fertilizers and the nitrate problem. C.A.B. International, 170 pp.  Acharya, B.S., Rasmussen, J., and Eriksen, J. (2012). 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PLoS. 10(1). https://doi.org/10.1371/journal.pone.0115649 78  Appendices  Appendix A Average (n=3) potato crop yield (kg ha-1) for treatments of annually cropped (AC), 2-year grassland set-aside (2G), 3-year grassland set-aside (3G), without N fertilizer and with N fertilizer (AC + N, 2G + N, 3G + N) corresponding to each diameter size class according to diameter (cm) of potatoes for the productive and unproductive field in 2018. Standard errors are shown in brackets.  Field Treatment Potato Yield in Corresponding Size Class (kg ha-1) < 2.54-cm 2.54-3.81-cm 3.81-5.73-cm 5.73-7.62-cm >7.61-cm Productive Field AC 94.17 (70.66) 676.67 (135.14) 7294.17 (2089.72) 11427.5 (1779.89) 2912.5 (1056.92) AC + N 79.17 (17.22) 831.67 (335.15) 5400 (1119.59) 15786.67 (1927.49) 13235 (3728.61) 2G  58.33 (40.03) 580 (216.4) 4347.5 (951.7) 11684.08 (959.18) 5832.5 (3090.83) 2G + N 53.33 (26.82) 522.5 (180.83) 3543.33 (535.63) 17547.5 (197.63) 7810 (2397.99) 3G  51.67 (25.99) 525.83 (159.33) 6315 (607.8) 7166.67 (905.84) 526.67 (526.67) 3G + N 69.17 (32.29) 460.83 (160.64) 3505.83 (672.11) 12382.5 (3017.29) 8146.67 (2030.93) Unproductive Field AC 154.17 (69.89) 596.67 (280.56) 5425 (378.52) 11597.5 (2805.7) 2040.83 (2040.83) AC + N 135 (32.24) 675.83 (69.17) 6276.67 (936.95) 16622.5 (2533.53) 3143.33 (56.06) 2G  79.17 (41.89) 392.5 (101.68) 3714.17 (931.26) 13124.17 (1841.58) 2796.67 (228.63) 2G + N 58.33 (7.95) 838.33 (39.01) 5241.67 (612.79) 14569.17 (376.46) 6262.5 (1393.31) 3G  30 (6.29) 479.17 (241.23) 6414.17 (685.01) 15057.5 (3133.04) 3656.67 (2454.25) 3G + N 74.17 (37.43) 948.33 (257.82) 7251.67 (1424.73) 16806.67 (865.03) 12030.83 (5241.67)     79  Appendix B Gas chamber measurements shown as means of all collars on measurement dates. Panel (A) shows mean barometric pressure (pBar) (averaging 101.24 0.02 kPa), panel (B) shows air temperature in the gas chamber collar (averaging 24.13 0.40 C from March to October and 7.05 0.32 C from October to March), panel (C) shows soil temperature at 10-cm depth (averaging 6.39 C), and panel (D) shows soil moisture at 10-cm depth from June 2017 to April 2019 (averaging 15.67%).      80  Appendix C Average of plant available nitrogen (n=3) nitrate (NO3--N) and ammonium (NH4+-N) in the productive and unproductive fields at 0-15 and 15-30-cm depths during the 2017 and 2018 growing seasons. Standard errors are shown in brackets, and bolded values and different letters show significant differences between treatments by Tukey’s Honest Significant Difference test (HSD, p<0.05).   Field Date Treatment NO3--N (kg ha-1) NH4+-N (kg ha-1) 0-15-cm 15-30-cm 0-15-cm 15-30-cm Productive Field 2017-05-17 AC 2.6 (0.14) b 0.86 (0.06) a 1.71 (0.2) a 2.35 (0.44) a AC + N 2.62 (0.15) a 0.87 (0.06) a 1.7 (0.2) a 2.31 (0.43) a 2G  1.29 (0.08) c 0.97 (0.05) a 1.27 (0.24) b 3.5 (0.34) a 2G + N 1.52 (0.07) bc 0.78 (0.05) a 1.07 (0.13) a 4.17 (0.45) a 2017-06-14 AC 4.46 (0.08) a 2.22 (0.08) a 6.19 (1.01) a 3.13 (0.17) a AC + N 4.46 (0.08) a 2.22 (0.08) a 6.19 (1.01) a 3.13 (0.17) a 2G  5.11 (0.61) a 1.64 (0.04) b 5.86 (0.64) a 2.61 (0.07) a 2G + N 6.16 (0.33) a 2.48 (0.16) a 11.26 (3.03) a 1.96 (0.05) b 2017-06-28 AC 10.21 (0.51) a 4.41 (0.18) b 1.31 (0.52) a 0.87 (0.36) a AC + N 10.21 (0.51) a 4.41 (0.18) b 1.31 (0.52) a 0.87 (0.36) a 2G  12.36 (0.46) a 5.25 (0.24) ab 1.74 (0.2) a 0.93 (0.43) a 2G + N 12 (0.48) a 5.96 (0.25) a 1.1 (0.16) a 0.19 (0.07) a 2017-07-12 AC 14.81 (0.86) a 5.42 (0.24) a 8.06 (2.01) a 1.68 (0.4) a AC + N 15.4 (0.98) a 7.98 (0.6) a 4.64 (1.76) a 6.67 (2.47) a 2G  15.17 (0.82) a 5.34 (0.34) a 4.47 (1.46) a 3.18 (1.03) a 2G + N 19.93 (1.85) a 6.22 (0.39) a 4.98 (1.25) a 1.35 (0.45) a 2018-07-26 AC 16.41 (1.02) a 8.1 (0.51) a 4.98 (0.71) ab 1.93 (0.33) a AC + N 15.16 (1.65) a 6.21 (0.57) a 6.47 (1.44) a 6.86 (2.63) a 2G  16.72 (1.33) a 5.84 (0.31) a 2.86 (0.33) b 3.56 (0.87) a 2G + N 13.17 (1.32) a 6.48 (0.34) a 5.22 (1.02) ab 2.46 (0.32) a 2017-08-09 AC 15.43 (2.96) b 8.35 (1.59) a 19.48 (3.5) a 9.97 (2.96) a AC + N 9.12 (1.86) b 6.79 (1.22) a 26.81 (7.15) a 9.74 (2.87) a 2G  16.19 (3.4) b 6.79 (1.29) a 14.45 (4.68) a 10.57 (3.23) a 2G + N 23.46 (3.79) a 7.49 (1.13) a 20.84 (4.76) a 8.1 (2.02) a 2017-08-23 AC 25.03 (2.52) b 9.25 (1.09) ab 6.95 (0.27) a 4.94 (0.33) a AC + N 31.74 (3.26) ab 10.76 (1.17) a 19.69 (1.7) a 6.03 (0.26) a 2G  25.96 (2.49) a 9.17 (1.12) ab 6.48 (0.43) a 8.55 (1.82) a 2G + N 22.54 (1.91) ab 7.62 (0.93) b 9.02 (0.31) a 4.49 (0.17) a 2017-09-07 AC 18.04 (3.1) ab 8.98 (1.69) a 4.86 (0.4) b 3.44 (0.33) a AC + N 21.97 (3.94) a 6.24 (1) ab 6.97 (0.87) ab 3.53 (0.41) a 2G  15.19 (2.74) b 4.49 (0.65) b 5.39 (0.56) b 4 (0.55) a 2G + N 18.06 (2.35) ab 5.11 (0.59) b 15.15 (2.08) a 6.46 (2.26) a 2017-09-21 AC 15.49 (2.73) c 4.49 (0.52) b 4.68 (0.83) b 3.12 (0.74) ab AC + N 32.61 (6.47) a 5.98 (0.88) ab 22 (4.21) a 6.33 (1.81) ab 2G  20.06 (3.71) bc 6.12 (1.01) ab 4 (0.58) b 2.39 (0.42) b 2G + N 22.46 (3.46) b 5.98 (0.76) a 16.17 (4.52) a 5.25 (1.14) a 2018-05-22 AC 0.14 (0.02) a 0.22 (0.03) a 0.96 (0.14) a 1.45 (0.15) a 81  AC + N 0.09 (0.01) a 0.28 (0.04) a 0.66 (0.04) a 1.82 (0.25) a 2G  0.19 (0.04) a 0.51 (0.09) a 1.39 (0.25) a 3.32 (0.47) a 2G + N 0.13 (0.01) a 0.2 (0.02) a 0.94 (0.05) a 1.32 (0.14) a 3G 0.13 (0) a 0.24 (0.05) a 0.87 (0.02) a 1.5 (0.33) a 3G + N 0.22 (0.04) a 0.19 (0.02) a 1.54 (0.27) a 1.27 (0.12) a 2018-06-05 AC 0.36 (0.21) a 0.47 (0.22) a 2.53 (1.5) a 2.98 (1.33) a AC + N 0.25 (0.13) a 0.15 (0.05) a 1.73 (0.88) a 0.96 (0.33) a 2G  9.89 (9.5) a 3.11 (2.79) a 9.01 (6.36) a 3.59 (1.7) a 2G + N 1.11 (0.85) a 0.63 (0.32) a 7.79 (5.95) a 4.02 (2.01) a 3G 0.41 (0.13) a 0.3 (0.1) a 2.88 (0.93) a 1.99 (0.63) a 3G + N 2.37 (2.16) a 2.02 (1.83) a 17.3 (15.78) a 14.28 (13.05) a 2018-06-18 AC 0.27 (0.09) a 0.17 (0.12) a 1.95 (0.64) a 1.15 (0.87) a AC + N 3.86 (2.55) a 0.31 (0.19) a 11.24 (8.59) a 1.99 (1.19) a 2G  0.31 (0.26) a 0.1 (0.04) a 2.16 (1.84) a 0.66 (0.24) a 2G + N 6.27 (5.45) a 0.84 (0.41) a 8.83 (4.47) a 5.54 (2.74) a 3G 0.48 (0.35) a 0.13 (0.08) a 3.49 (2.58) a 0.94 (0.6) a 3G + N 6.5 (3.83) a 2.46 (1.76) a 28.9 (23.8) a 17.04 (12.51) a 2018-07-03 AC 0.17 (0.08) b 0.41 (0.33) a 1.23 (0.58) a 2.6 (2.07) a AC + N 21.73 (15.82) a 1.49 (1.2) a 20.43 (11.13) a 9.39 (7.45) a 2G  0.25 (0.1) b 0.12 (0.05) a 1.74 (0.72) a 0.8 (0.34) a 2G + N 12.67 (8.35) ab 0.81 (0.37) a 10.94 (5.66) a 5.27 (2.35) a 3G 0.23 (0.12) b 0.21 (0.07) a 1.58 (0.81) a 1.39 (0.52) a 3G + N 11.34 (8.12) ab 0.67 (0.34) a 9.62 (5.39) a 4.41 (2.22) a 2018-07-16 AC 0.1 (0.03) a 0.22 (0.08) a 0.74 (0.19) a 1.46 (0.57) a AC + N 5.67 (3.56) a 0.17 (0.06) a 16.87 (10.64) a 1.14 (0.44) a 2G  0.57 (0.42) a 0.12 (0.04) a 4.11 (3.11) a 0.81 (0.31) a 2G + N 3.97 (3.71) a 0.22 (0.09) a 4.21 (2.47) a 1.47 (0.6) a 3G 0.38 (0.18) a 0.21 (0.04) a 2.68 (1.26) a 1.37 (0.24) a 3G + N 2.94 (1.78) a 0.28 (0.16) a 20.93 (12.75) a 1.96 (1.14) a 2018-07-30 AC 0.35 (0.19) a 0.11 (0.05) a 2.41 (1.32) a 0.73 (0.32) a AC + N 2.95 (2.59) a 0.08 (0.03) a 4.09 (1.81) a 0.52 (0.18) a 2G  0.19 (0.06) a 0.1 (0.05) a 1.39 (0.45) a 0.62 (0.3) a 2G + N 0.52 (0.14) a 0.31 (0.08) a 3.65 (1.01) a 1.99 (0.54) a 3G 0.73 (0.53) a 0.14 (0.04) a 5.14 (3.75) a 0.91 (0.23) a 3G + N 1.03 (0.5) a 0.13 (0.07) a 7.38 (3.66) a 0.88 (0.45) a 2018-08-13 AC 0.41 (0.11) a 0.23 (0.06) a 2.92 (0.82) a 1.52 (0.35) a AC + N 14.13 (13.65) a 0.83 (0.59) a 12.51 (9.17) a 5.24 (3.65) a 2G  0.3 (0.07) a 0.23 (0.11) a 2.15 (0.48) a 1.5 (0.7) a 2G + N 9.53 (9.04) a 14.47 (14.24) a 9.28 (5.9) a 10.22 (8.62) a 3G 0.81 (0.3) a 0.15 (0.03) a 5.71 (2.06) a 1 (0.15) a 3G + N 2.47 (1.46) a 1.19 (0.83) a 17.16 (10.07) a 7.67 (5.27) a 2018-08-27 AC 3.17 (2.24) ab 1.99 (1.09) a 22.87 (16.43) a 13.47 (7.72) a AC + N 9.82 (8.38) b 16.02 (9.35) a 15.2 (9.97) a 17.61 (8.61) a 2G  14.9 (10.77) b 1.22 (0.57) a 18.41 (10.02) a 8.07 (3.85) a 2G + N 7.92 (3.8) a 18.84 (14.39) a 56.23 (27.1) a 36.98 (21.95) a 3G 5.43 (2.42) ab 1.49 (0.68) a 38.36 (17.11) a 9.83 (4.55) a 82  3G + N 8.03 (3.72) a 4.32 (2.58) a 57.07 (26.68) a 29.34 (18.23) a 2018-09-13 AC 0.74 (0.4) a 0.25 (0.04) a 5.25 (2.78) a 1.63 (0.31) a AC + N 0.51 (0.11) a 0.27 (0.04) a 3.55 (0.74) a 1.75 (0.3) a 2G  0.54 (0.24) a 0.24 (0.03) a 3.85 (1.74) a 1.58 (0.2) a 2G + N 4.19 (2.89) a 1.81 (1.03) a 29.49 (20.28) a 12.2 (7.15) a 3G 0.47 (0.13) a 0.25 (0.05) a 3.29 (0.93) a 1.66 (0.35) a 3G + N 0.63 (0.17) a 1.01 (0.73) a 4.43 (1.15) a 7.01 (5.25) a Unproductive Field 2017-05-17 AC 0.16 (0.03) a 0.35 (0.04) c 3.21 (0.14) c 6.62 (0.47) c AC + N 0.16 (0.03) c 0.35 (0.04) a 3.19 (0.14) a 6.57 (0.46) d 2G  0.08 (0.01) d 0.1 (0.02) d 10.72 (2.98) d 13.4 (1.03) b 2G + N 0.11 (0.02) b 0.15 (0.02) b 9.18 (2.56) b 13.42 (0.89) a 2017-06-14 AC 0.96 (0.03) c 1.09 (0.03) a 1.91 (0.28) b 0.94 (0.11) c AC + N 0.96 (0.03) d 1.09 (0.03) c 1.91 (0.28) c 0.94 (0.11) bc 2G  1.37 (0.12) b 1.24 (0.12) a 1.69 (0.26) d 1.48 (0.18) ab 2G + N 1.39 (0.09) a 1.15 (0.09) b 1.71 (0.2) a 1.27 (0.15) a 2017-06-28 AC 4.77 (0.36) d 2.59 (0.17) c 2.28 (0.52) c 1.13 (0.16) a AC + N 4.77 (0.36) a 2.59 (0.17) d 2.28 (0.52) a 1.13 (0.16) c 2G  4.53 (0.13) c 3.08 (0.19) a 2.75 (0.78) d 0.9 (0.12) d 2G + N 4.47 (0.1) b 2.91 (0.15) b 2.23 (0.61) b 0.76 (0.1) b 2017-07-12 AC 5.47 (0.8) a 2.85 (0.57) a 6.66 (3.27) b 1.82 (0.49) a AC + N 6.59 (1.17) a 3.11 (0.53) a 1.13 (0.25) b 3.82 (0.91) a 2G  5.29 (0.77) a 3.08 (0.53) a 1.97 (0.29) b 1.5 (0.3) a 2G + N 7.25 (1) a 2.87 (0.41) a 29.25 (7.13) a 4.24 (1.39) a 2018-07-26 AC 13.22 (2.07) b 6.74 (1.11) a 2.73 (0.45) b 1.24 (0.27) a AC + N 10.39 (1.43) b 8.04 (1.11) a 1.97 (0.27) b 2.58 (0.39) a 2G  13.87 (1.44) a 7.25 (0.86) a 1.77 (0.2) b 0.93 (0.21) a 2G + N 14.69 (1.26) ab 7.14 (0.59) a 22.31 (5.34) a 8.4 (4.24) a 2017-08-09 AC 17.62 (0.9) b 11.29 (0.79) b 25.82 (10.87) ab 3.6 (0.64) b AC + N 21.46 (1.62) b 14.94 (1.58) a 40.51 (12.56) a 11.65 (3.42) a 2G  18.27 (0.76) a 9.64 (0.58) b 1.72 (0.37) b 4.07 (1.65) b 2G + N 18.6 (0.44) ab 12.1 (0.55) b 3.84 (0.62) b 2.38 (0.35) b 2017-08-23 AC 23.48 (1.22) a 16.44 (1.34) a 1.39 (0.41) a 1.4 (0.61) a AC + N 22.81 (1.17) a 19.85 (1.86) a 4.05 (1.3) a 0.2 (0.05) a 2G  25.78 (2.49) a 14.14 (0.62) a 1.96 (0.5) a 0.86 (0.15) a 2G + N 26.58 (1.96) a 13.29 (0.78) a 1.98 (0.32) a 0.83 (0.2) a 2017-09-07 AC 25.04 (1.41) a 14.82 (0.68) a 3.99 (0.39) a 3.18 (0.17) a AC + N 25.3 (1.26) a 13.77 (1.19) b 11.56 (3.55) a 3 (0.28) a 2G  22.09 (1.59) a 12.6 (0.78) ab 5.52 (1.16) a 3.4 (0.3) a 2G + N 24.42 (1.88) a 12.38 (0.87) ab 11.18 (3.75) a 3.66 (0.22) a 2017-09-21 AC 28.92 (3.47) a 12.86 (1.22) a 6.33 (1.32) a 4.97 (0.72) a AC + N 30.6 (2.28) a 13.43 (2.04) b 7.07 (1.35) a 3.59 (0.55) a 2G  24.23 (1.63) a 11.05 (1.13) ab 8.29 (1.66) a 4.25 (0.97) a 2G + N 27.24 (3.17) a 10.85 (1.22) ab 5.42 (0.62) a 6.01 (1.53) a 2018-05-22 AC 0.07 (0.01) a 0.31 (0.16) a 0.48 (0.11) a 1.76 (0.87) a AC + N 0.19 (0.03) a 0.11 (0.04) a 1.27 (0.21) a 0.67 (0.18) a 83  2G  0.21 (0.08) a 0.1 (0.05) a 1.48 (0.58) a 0.6 (0.25) a 2G + N 0.19 (0.03) a 0.15 (0.03) a 1.3 (0.16) a 0.93 (0.15) a 3G 0.17 (0.1) a 0.17 (0.01) a 1.11 (0.64) a 1.05 (0.11) a 3G + N 0.13 (0) a 0.09 (0) a 0.92 (0) a 0.64 (0) a 2018-06-05 AC 7.57 (5.63) a 2.49 (0.79) a 15.28 (4.36) a 17.08 (5.9) a AC + N 1.49 (0.46) a 2.04 (0.69) a 10.2 (3.14) a 13.41 (4.35) a 2G  3.54 (1.73) a 2.91 (1.86) a 24.27 (11.55) a 19.24 (12.54) a 2G + N 16.72 (14.09) a 7.63 (6.02) a 25.49 (9.93) a 13.34 (4.38) a 3G 5.65 (3.36) a 3.76 (1.61) a 38.21 (22.25) a 25.58 (11.03) a 3G + N 4.08 (1.83) a 5.14 (4.23) a 27.9 (12.6) a 8.13 (3.2) a 2018-06-18 AC 0.29 (0.06) a 0.2 (0.08) a 1.99 (0.45) a 1.36 (0.62) a AC + N 4.68 (3.48) a 0.11 (0.02) a 9.73 (5.13) a 0.71 (0.13) a 2G  0.14 (0.03) a 0.08 (0.02) a 0.92 (0.19) a 0.52 (0.15) a 2G + N 7.45 (7.28) a 0.18 (0.05) a 6.28 (5.16) a 1.2 (0.37) a 3G 0.96 (0.58) a 0.32 (0.18) a 6.44 (3.87) a 1.95 (1.02) a 3G + N 14.06 (13.44) a 1.54 (1.37) a 12.3 (8.13) a 8.7 (7.51) a 2018-07-03 AC 0.08 (0.04) a 0.14 (0.09) a 0.57 (0.26) b 0.87 (0.52) a AC + N 26.22 (11.81) a 0.43 (0.32) a 18.08 (7.81) a 2.49 (1.77) a 2G  0.12 (0.07) a 0.05 (0.05) a 0.78 (0.48) b 0.35 (0.33) a 2G + N 20.57 (12.19) a 0.55 (0.29) a 32.46 (19.64) ab 3.53 (1.83) a 3G 1.26 (1.02) a 0.25 (0.1) a 8.38 (6.77) b 1.63 (0.66) a 3G + N 11.51 (8.52) a 0.41 (0.17) a 24.36 (12.17) ab 2.63 (1.01) a 2018-07-16 AC 0.11 (0.04) a 0.14 (0.04) a 0.74 (0.24) a 0.88 (0.26) a AC + N 16.95 (13.32) a 3.42 (2.11) a 30.7 (21.04) a 20.98 (12.99) a 2G  0.14 (0.06) a 0.05 (0.02) a 0.92 (0.42) a 0.33 (0.16) a 2G + N 16.59 (11.52) a 0.57 (0.35) a 23.02 (13.22) a 4.06 (2.59) a 3G 0.69 (0.27) a 0.15 (0.1) a 4.69 (1.81) a 0.87 (0.53) a 3G + N 26.1 (12.89) a 10.7 (9.9) a 18.49 (8.06) a 15.68 (10.54) a 2018-07-30 AC 0.09 (0.05) a 0.09 (0.05) a 0.63 (0.34) a 0.6 (0.35) a AC + N 0.37 (0.29) a 0.09 (0.06) a 2.46 (1.93) a 0.58 (0.43) a 2G  0.08 (0.03) a 0.05 (0.03) a 0.54 (0.22) a 0.35 (0.2) a 2G + N 0.13 (0.08) a 0.05 (0.04) a 0.89 (0.51) a 0.38 (0.27) a 3G 0.56 (0.34) a 0.08 (0.05) a 3.77 (2.24) a 0.55 (0.33) a 3G + N 0.38 (0.22) a 0.12 (0.06) a 2.46 (1.42) a 0.76 (0.4) a 2018-08-13 AC 0.55 (0.2) a 1.1 (0.95) a 3.8 (1.41) a 6.25 (5.2) a AC + N 18.4 (17.97) a 5.39 (5.22) a 14.37 (11.52) a 4.43 (3.37) a 2G  0.45 (0.16) a 0.14 (0.04) a 3.01 (1.05) a 0.95 (0.27) a 2G + N 0.61 (0.28) a 0.26 (0.14) a 4.18 (1.94) a 1.59 (0.75) a 3G 1.27 (0.56) a 0.15 (0.04) a 8.45 (3.71) a 1.03 (0.27) a 3G + N 1.58 (0.79) a 0.86 (0.51) a 10.88 (5.3) a 6.03 (3.76) a 2018-08-27 AC 0.65 (0.24) b 1.24 (0.94) a 4.46 (1.7) b 7.19 (5.16) a AC + N 21.85 (12.75) a 1.5 (1.29) a 25.94 (11.37) a 10.11 (8.69) a 2G  0.46 (0.2) b 0.21 (0.03) a 3.03 (1.28) b 1.36 (0.15) a 2G + N 32.8 (14.85) a 5.77 (4.85) a 23.11 (9.92) a 8.83 (4.56) a 3G 1.69 (0.5) b 0.17 (0.06) a 11.25 (3.26) b 1.15 (0.42) a 84  3G + N 1.6 (0.82) ab 0.96 (0.46) a 10.91 (5.52) b 6.62 (3.41) a 2018-09-13 AC 0.34 (0.14) a 0.16 (0.02) a 2.28 (0.89) a 1.04 (0.15) a AC + N 3.82 (3.41) a 0.2 (0.07) a 4.6 (2.04) a 1.32 (0.52) a 2G  0.28 (0.08) a 0.18 (0.02) a 1.86 (0.53) a 1.2 (0.12) a 2G + N 0.68 (0.33) a 0.22 (0.09) a 4.64 (2.29) a 1.43 (0.61) a 3G 1.58 (0.66) a 0.26 (0.1) a 10.44 (4.35) a 1.84 (0.77) a 3G + N 1.4 (0.55) a 0.26 (0.08) a 9.53 (3.62) a 1.65 (0.45) a    85  Appendix D Analysis of variance of average plant available nitrogen (n=3) of nitrate (NO3--N) and ammonium (NH4+-N) in the productive and unproductive fields at 0-15 and 15-30-cm depths during the 2017 and 2018 growing seasons. Bolded values show significant differences between treatments by Tukey’s Honest Significant Difference test (HSD, p<0.05).  Plant  Available  Nitrogen Date Field At 0-15-cm depth At 15-30-cm depth GLSA Fertilizer GLSA*Fertilizer GLSA Fertilizer GLSA*Fertilizer f p-value f p-value f p-value f p-value f p-value f p-value NO3--N 2017-05-17 Productive Field 1.06 0.32 5.11 0.04 4.95 0.04 0.01 0.91 2.75 0.13 0.09 0.77 Unproductive Field 930.67 <0.01 15858.29 <0.01 695.24 <0.01 1990.95 <0.01 2796.42 <0.01 554.50 <0.01 2017-06-14 Productive Field 5.25 0.03 1.28 0.27 1.28 0.27 136.58 <0.01 0.01 0.91 2.96 0.11 Unproductive Field 33653.60 <0.01 4418.16 <0.01 14963.00 <0.01 34.39 <0.01 120.45 <0.01 81.26 <0.01 2017-06-28 Productive Field 8.19 0.01 0.00 0.98 0.00 0.98 0.18 0.68 16.31 <0.01 16.31 <0.01 Unproductive Field 1889.38 <0.01 18881.05 <0.01 18181.20 <0.01 1673.67 <0.01 446.66 <0.01 675.77 <0.01 2017-07-12 Productive Field 1.00 0.33 0.76 0.40 0.08 0.78 16.35 <0.01 1.95 0.18 1.95 0.18 Unproductive Field 0.06 0.81 7.83 0.01 0.39 0.54 0.01 0.91 0.00 1.00 0.17 0.69 2017-07-26 Productive Field 0.58 0.46 1.86 0.19 0.04 0.84 0.92 0.35 5.73 0.03 1.58 0.23 Unproductive Field 20.78 <0.01 0.96 0.34 1.36 0.26 7.00 0.02 0.27 0.61 1.02 0.33 2017-08-09 Productive Field 13.54 <0.01 0.66 0.43 11.55 <0.01 0.99 0.33 0.08 0.77 5.09 0.04 Unproductive Field 8.09 0.01 7.56 0.01 3.68 0.07 12.84 <0.01 9.00 <0.01 1.27 0.28 2017-08-23 Productive Field 2.00 0.17 0.10 0.75 8.50 <0.01 0.08 0.78 0.27 0.61 4.47 0.05 Unproductive Field 0.72 0.41 3.75 0.07 0.02 0.88 0.16 0.69 0.16 0.69 3.96 0.07 2017-09-07 Productive Field 5.55 0.03 4.23 0.05 0.13 0.72 7.71 0.01 0.00 0.95 1.85 0.19 Unproductive Field 0.30 0.59 1.89 0.19 0.02 0.90 1.53 0.23 4.78 0.04 4.60 0.05 2017-09-21 Productive Field 4.66 0.04 98.14 <0.01 35.21 <0.01 5.91 0.03 3.42 0.08 1.78 0.20 Unproductive Field 0.39 0.54 2.02 0.17 0.00 0.98 1.06 0.32 5.11 0.04 4.95 0.04 2018-05-22 Productive Field 2.62 0.14 0.25 0.63 6.04 0.03 1.59 0.27 2.83 0.14 4.83 0.05 Unproductive Field 0.42 0.68 0.18 0.69 0.43 0.67 0.31 0.75 0.32 0.59 0.77 0.50 2018-06-05 Productive Field 1.11 0.37 0.70 0.42 0.90 0.43 0.92 0.43 0.65 0.44 0.99 0.40 Unproductive Field 3.12 0.09 0.16 0.70 0.04 0.96 0.21 0.82 4.16 0.07 0.42 0.67 2018-06-18 Productive Field 1.12 0.36 1.30 0.28 0.84 0.46 1.53 0.26 3.76 0.08 1.58 0.25 Unproductive Field 0.25 0.78 5.00 0.05 0.14 0.87 1.23 0.33 0.92 0.36 0.75 0.50 2018-07-03 Productive Field 2.16 0.17 27.78 <0.01 2.28 0.15 0.72 0.51 4.33 0.06 0.16 0.85 Unproductive Field 0.63 0.55 13.27 <0.01 0.75 0.50 0.06 0.94 8.23 0.02 1.09 0.37 2018-07-16 Productive Field 1.18 0.35 8.00 0.02 1.15 0.36 0.20 0.83 1.72 0.22 0.84 0.46 Unproductive Field 0.16 0.86 9.65 0.01 0.44 0.65 1.25 0.33 7.72 0.02 1.12 0.36 2018-07-30 Productive Field 2.23 0.16 1.26 0.29 0.01 0.99 2.41 0.14 1.00 0.34 2.33 0.15 Unproductive Field 1.68 0.23 0.97 0.35 0.93 0.43 0.25 0.65 0.34 0.79 0.66 0.54 2018-08-13 Productive Field 0.60 0.57 4.90 0.05 0.05 0.95 0.21 0.82 3.18 0.10 0.17 0.85 Unproductive Field 0.86 0.45 1.75 0.22 0.50 0.62 1.07 0.38 0.27 0.62 0.77 0.49 2018-05-27 Productive Field 8.33 0.01 7.95 0.02 5.22 0.03 0.58 0.58 10.81 <0.01 1.84 0.21 Unproductive Field 1.86 0.21 33.22 <0.01 4.83 0.03 0.62 0.56 2.20 0.17 0.11 0.89 2018-08-13 Productive Field 1.50 0.27 1.52 0.25 1.57 0.26 1.51 0.26 4.75 0.05 1.64 0.24 Unproductive Field 1.58 0.25 0.50 0.50 0.37 0.70 0.28 0.76 0.11 0.75 0.17 0.84 NH4+-N 2017-05-17 Productive Field 18.32 <0.01 13.67 <0.01 22.22 <0.01 1.18 0.30 0.59 0.46 0.04 0.84 86  Unproductive Field 30365.04 <0.01 216236.82 <0.01 1377.52 <0.01 4867.02 <0.01 60.19 <0.01 896.79 <0.01 2017-06-14 Productive Field 0.40 0.53 0.64 0.43 0.64 0.43 27.56 <0.01 4.44 0.05 4.44 0.04 Unproductive Field 213.67 <0.01 574.18 <0.01 85142.20 <0.01 35.84 <0.01 1.12 0.33 2.24 0.52 2017-06-28 Productive Field 0.04 0.85 1.31 0.27 1.31 0.27 0.95 0.34 1.40 0.25 1.40 0.25 Unproductive Field 14392.49 <0.01 245550.65 <0.01 87160.76 <0.01 2682.69 <0.01 4057.22 <0.01 255.00 <0.01 2017-07-12 Productive Field 3.14 0.11 2.40 0.16 0.00 0.96 1.27 0.28 0.94 0.34 4.66 0.04 Unproductive Field 8.13 0.01 17.58 <0.01 14.17 <0.01 0.05 0.82 4.40 0.05 0.00 0.95 2017-07-26 Productive Field 2.01 0.18 10.15 0.01 0.17 0.68 0.99 0.33 2.23 0.15 4.91 0.04 Unproductive Field 12.69 <0.01 24.16 <0.01 11.31 <0.01 3.10 0.10 4.35 0.05 2.96 0.10 2017-08-09 Productive Field 0.06 0.80 4.20 0.06 0.23 0.64 2.77 0.13 0.12 0.73 0.29 0.61 Unproductive Field 18.29 <0.01 1.78 0.20 1.89 0.19 7.93 0.01 6.99 0.02 6.35 0.02 2017-08-23 Productive Field 1.69 0.21 4.14 0.06 3.66 0.07 0.95 0.34 1.19 0.29 2.34 0.14 Unproductive Field 0.33 0.57 0.05 0.83 2.22 0.16 0.73 0.41 0.61 0.45 0.18 0.68 2017-09-07 Productive Field 4.16 0.06 10.87 <0.01 1.84 0.19 0.34 0.57 3.07 0.10 2.96 0.10 Unproductive Field 0.32 0.58 5.55 0.03 0.02 0.89 5.10 0.04 0.39 0.54 1.14 0.30 2017-09-21 Productive Field 2.93 0.11 116.44 <0.01 0.17 0.68 1.86 0.19 3.23 0.09 5.11 0.04 Unproductive Field 0.29 0.60 0.49 0.50 2.09 0.17 1.24 0.28 0.20 0.66 2.48 0.13 2018-05-22 Productive Field 2.62 0.14 0.25 0.63 6.04 0.03 1.20 0.36 2.51 0.16 3.82 0.08 Unproductive Field 0.39 0.69 0.22 0.65 0.47 0.64 0.35 0.71 0.38 0.56 0.78 0.49 2018-06-05 Productive Field 1.11 0.37 0.70 0.42 0.90 0.44 0.91 0.44 0.63 0.45 0.99 0.41 Unproductive Field 0.74 0.50 0.26 0.62 1.81 0.21 0.92 0.43 0.86 0.38 0.41 0.67 2018-06-18 Productive Field 1.12 0.36 1.30 0.28 0.84 0.46 1.60 0.25 4.17 0.07 1.66 0.24 Unproductive Field 0.28 0.76 2.66 0.13 0.28 0.76 1.25 0.33 0.88 0.37 0.76 0.49 2018-07-03 Productive Field 2.16 0.17 27.78 <0.01 2.28 0.15 0.78 0.49 4.18 0.07 0.18 0.84 Unproductive Field 0.99 0.41 22.30 <0.01 1.35 0.30 0.01 0.99 7.89 0.02 0.89 0.44 2018-07-16 Productive Field 1.18 0.35 8.00 0.02 1.15 0.36 0.17 0.85 1.74 0.22 0.80 0.47 Unproductive Field 0.48 0.63 17.38 <0.01 0.39 0.69 0.88 0.45 2.17 0.17 0.85 0.46 2018-07-30 Productive Field 0.77 0.49 1.50 0.25 0.81 0.47 2.59 0.12 1.04 0.33 2.37 0.14 Unproductive Field 1.66 0.24 0.94 0.36 0.90 0.44 0.94 0.56 1.98 0.20 0.92 0.54 2018-08-13 Productive Field 0.45 0.65 2.78 0.13 0.53 0.60 0.91 0.44 1.23 0.29 0.88 0.44 Unproductive Field 0.93 0.42 1.08 0.32 0.97 0.41 1.20 0.34 0.91 0.36 0.54 0.60 2018-05-27 Productive Field 0.57 0.58 0.03 0.86 0.91 0.43 0.88 0.45 5.86 0.04 0.88 0.44 Unproductive Field 7.56 0.01 30.66 <0.01 8.24 <0.01 0.80 0.48 1.84 0.20 1.06 0.38 2018-08-13 Productive Field 1.50 0.28 1.54 0.25 1.58 0.26 1.62 0.25 4.93 0.05 1.76 0.22 Unproductive Field 0.67 0.53 1.19 0.30 0.98 0.41 0.32 0.73 0.24 0.64 0.10 0.91    87  Appendix E Cropping history of the productive field and unproductive field from 2012 to 2016.   Year Productive Field Unproductive Field 2016 GLSA GLSA 2015 GLSA GLSA 2014 Beans Potatoes 2013 Potatoes Peas 2012 Beans  Potatoes 2011 Potatoes Potatoes                       88  Appendix F Daily average greenhouse gas (GHG) fluxes (n=3) of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from June 2017 to March 2019 for the productive field. Standard errors are shown in brackets, and bolded values and different letters show significant differences between treatments by Tukey’s Honest Significant Difference test (HSD, p<0.05).  Date Treatment Daily CO2-C Flux  Daily CH4-C Flux Daily N2O-N Flux                   kg ha-1                g ha-1 2017-06-14 AC 38.9764 (1.7796) a 0.6 (0.9) a 2.2 (0.4) b AC + N 41.2719 (5.3711) a -1.1 (0.5) a 0.7 (0.1) c 2G + N 33.5541 (1.9944) a 0.9 (1.5) a 3.3 (0.2) a 2017-06-21 AC 34.3638 (1.7519) a 1.3 (1.3) a 2.6 (0.6) a AC + N 34.2175 (4.169) a -0.7 (0.4) a 1.4 (0.1) a 2G + N 22.4479 (2.3187) b 2.1 (1.2) a 1 (0.5) a 2017-06-28 AC 38.56 (7.5041) a 2.2 (1.8) a 0.7 (0.2) a AC + N 35.3253 (3.6518) a 1.4 (1.8) a 0.2 (0.2) a 2G + N 38.5564 (4.5094) a -1.4 (0.2) a 1.7 (0.4) a 2017-07-05 AC 35.0074 (4.5657) a 0.7 (1) a 1.7 (0.3) a AC + N 28.6968 (2.1754) a 2.2 (0.6) a 1.4 (0.2) a 2G + N 39.4315 (4.1797) a 2.6 (1) a 1.4 (0.4) a 2017-07-10 AC N/A   N/A   N/A   AC + N 27.437 (9.282) a 2.5 (0.5) a 0.6 (0.9) a 2G + N 22.7833 (1.7966) a 3.1 (0.1) a -0.1 (0.9) a 2017-07-12 AC 27.9248 (2.0616) a -0.1 (1.1) a 1.2 (0.3) a AC + N 33.9522 (6.1891) a -0.8 (1.2) a 0.8 (0.7) a 2G + N 33.9826 (6.4048) a 0.6 (1.3) a 0.6 (0.5) a 2017-07-13 AC 33.9292 (3.1746) a -1.1 (1.4) a 0.7 (0.3) a AC + N 45.1655 (11.7842) a -0.1 (2.3) a 1 (0.7) a 2G + N 33.71 (3.6128) a 0.9 (1.1) a 1.1 (0.6) a 2017-07-19 AC 30.6379 (3.0441) a -0.9 (1) a 0.7 (0.2) a AC + N 43.3713 (9.1486) a -0.1 (1.5) a 1.4 (0.8) a 2G + N 41.8816 (5.365) a 2.5 (0.9) a 1.7 (0.4) a 2017-07-26 AC 29.9964 (2.8079) a -0.8 (1.2) a 1.2 (0.3) a AC + N 36.3472 (7.1701) a 1.2 (0.8) a 1.2 (0.9) a 2G + N 35.0048 (5.4995) a 0.3 (1.2) a 1.9 (0.3) a 2017-08-02 AC 40.669 (5.4846) a 0.7 (0.6) a 2.2 (0.5) a AC + N 44.8833 (8.569) a 0.7 (1.1) a 3.3 (1) a 2G + N 50.3968 (7.5107) a 1 (1) a 2.1 (0.9) a 2017-08-09 AC 19.9912 (0.9516) a 0.5 (2.6) a 2.4 (0.2) b AC + N 31.6706 (5.6923) a 2.8 (1.1) a 1.9 (0.2) b 2G + N 32.773 (6.3988) a 1.1 (1.6) a 3.7 (0.2) a 2017-08-24 AC 17.3217 (2.7233) a -1.7 (2.1) a 1.2 (0.1) a AC + N 20.2142 (3.6521) a -0.4 (1.2) a 1.7 (0.7) a 2G + N 17.0287 (2.0117) a 0.9 (0.6) a 2.7 (0.9) a 2017-08-31 AC 24.365 (2.433) a 1.1 (0.9) a 1.2 (0.1) a AC + N 21.1048 (2.8243) a 2.1 (0.9) a 1.4 (0.4) a 2G + N 33.495 (4.6556) a 1.1 (1) a 3.1 (1) a 2017-09-07 AC 21.7796 (3.2423) a 2.9 (0.8) a 2.2 (0.3) a AC + N 24.1427 (1.9783) a 1.8 (1.3) a 1.9 (0.3) a 2G + N 29.4892 (1.7196) a -0.2 (0.8) a 4.2 (1.3) a 2017-09-14 AC 17.8937 (1.3032) a -4.1 (0.9) a 1.2 (0.2) a AC + N 20.2689 (6.9039) a -2.1 (0.7) a 1.6 (0.4) a 2G + N 33.6231 (4.5564) a -0.5 (2.1) a 3.3 (1.2) a 2017-09-21 AC 31.7416 (2.149) a -3.3 (1.1) a 1.4 (0.5) a AC + N 31.5473 (3.9589) a -0.8 (1.7) a 1.8 (0.6) a 2G + N 28.0787 (2.6122) a -2.5 (1.8) a 3.7 (1.6) a 2017-09-28 AC 22.5613 (2.2284) a -2.9 (1.6) a 1.2 (0.1) a AC + N 24.5103 (2.6565) a 0.4 (0.8) a 2.6 (0.9) a 2G + N 22.9725 (2.197) a -2.5 (1.4) a 4.1 (1.4) a 2017-10-17 AC 33.2168 (10.0566) a -1.9 (1.4) a 0.7 (0.2) a AC + N 36.0067 (8.6806) a -4.5 (0.9) a 2.6 (0.6) a 2G + N 14.0645 (2.7928) a -3.9 (1.4) a 2.8 (0.9) a 2017-11-09 AC 10.0658 (1.0791) a -2.5 (1) a 3.1 (0.7) a AC + N 8.1506 (1.0877) a -2.6 (1.1) a 4 (0.6) a 2G + N 8.4819 (1.1744) a -1.6 (0.4) a 7.2 (1.6) a 89  2017-11-30 AC 1.4497 (0.7234) a 0.5 (2.4) a 0 (0.2) a AC + N 3.3782 (0.9887) a -0.7 (1.8) a 3.2 (1.9) a 2G + N 1.8704 (0.9724) a 1 (2.1) a 2.4 (1) a 2017-12-14 AC 7.5114 (1.6612) a -1.2 (1.6) a 79.3 (23.5) a AC + N 9.2532 (1.4655) a 0.8 (1.3) a 84.4 (19.8) a 2G + N 8.0532 (1.2528) a -0.4 (0.7) a 148.8 (37.2) a 2018-01-11 AC -0.0089 (0.1465) b 0.7 (1.4) a -0.2 (0.1) a AC + N 0.6781 (0.2673) ab -1.6 (1.2) a 0 (0.1) a 2G + N 1.3876 (0.3067) a 1.7 (1.8) a -0.1 (0.2) a 2018-01-25 AC 1.1849 (0.5439) a -1 (0.6) a 0.3 (0.1) a AC + N 0.7427 (0.2908) a -1.2 (0.7) a 0 (0.1) a 2G + N 0.8219 (0.4176) a 0.4 (1) a 0 (0.2) a 2018-02-08 AC 4.8194 (1.8338) a -3.6 (1.4) a 0.1 (0.2) a AC + N 7.2649 (2.247) a -1.6 (1) a -0.2 (0.2) a 2G + N 7.3855 (1.6713) a -0.4 (0.7) a 0.5 (0.3) a 2018-02-22 AC 7.6877 (3.4227) a 3.3 (4.1) a 2.2 (1.5) a AC + N 3.502 (0.775) a 0 (1.5) a 1.8 (0.8) a 2G + N 1.9643 (0.6772) a 0.1 (1.9) a 2.4 (0.2) a 2018-03-08 AC 0.9651 (0.3215) a -3.1 (4.3) a -0.1 (0.2) a AC + N 2.3974 (1.1002) a 3.8 (3.3) a 0.1 (0.2) a 2G + N 4.0352 (1.8258) a -5.6 (4.1) a 0.4 (0.2) a 2018-03-22 AC -1.1389 (0.4528) a -0.6 (4) a 0 (0.4) a AC + N -0.0922 (0.44) a -21.4 (15.2) a -0.6 (0.7) a 2G + N 2.5926 (6.9509) a -9.5 (0.4) a 0.7 (1.3) a 2018-03-30 AC 4.2923 (2.2487) a -5.3 (4.1) a 0.7 (0.5) a AC + N 8.7947 (4.3646) a -7.7 (1.4) a 1.5 (0.5) a 2G + N 5.852 (3.081) a -2.4 (2.1) a 0.6 (0.5) a 2018-04-19 AC 11.254 (2.2519) a -10.6 (5.3) a 0.4 (0.6) a AC + N 12.4912 (2.5919) a -7 (2.8) a 0.4 (0.5) a 2G + N 18.6814 (2.8014) a -6.2 (1.1) a 0.2 (0.3) a 2018-05-03 AC 12.7839 (3.3296) a -9.7 (5.7) a 1.8 (1.4) a AC + N 21.7962 (10.6355) a -3.4 (1.5) a 3.3 (1.6) a 2G + N 23.4609 (9.4479) a -6.4 (1.6) a 5.6 (2.1) a 2018-05-15 AC 19.7028 (4.2061) a -12.4 (3.4) a 3.5 (1.8) a AC + N 22.4914 (5.6749) a -7.4 (2.4) a 17.7 (4.2) a 2G + N 24.403 (5.1326) a -7.4 (3.9) a 31 (13) a 2018-06-05 AC 18.203 (1.8996) b -2.6 (3.1) a 4.9 (1.6) ab AC + N 17.4849 (1.645) b -1.3 (2.9) a 11.7 (4.5) ab 2G + N 26.4204 (3.0446) ab -1.2 (1.6) a 24.1 (10.6) a 3G + N 32.5056 (4.087) a -3.8 (1.6) a 0 (0.6) b 2018-06-12 AC 18.3776 (1.2399) c -0.8 (3.5) a 3.6 (0.7) a AC + N 17.429 (1.7686) c -0.4 (2.6) a 7.4 (2.9) a 2G + N 26.3648 (2.0234) b -0.4 (2.1) a 14.1 (5.8) a 3G + N 35.515 (1.7586) a 1.6 (2.1) a 0.8 (0.5) a 2018-06-19 AC 23.3035 (2.1131) b -1.5 (2) a 3.4 (1.1) a AC + N 22.9202 (1.2743) b -4.5 (2.5) a 7.5 (2.9) a 2G + N 44.2589 (6.3049) a -1 (1.7) a 16.1 (6.3) a 3G + N 46.5196 (4.0289) a -4.7 (5.2) a 2 (1.4) a 2018-06-26 AC 18.6748 (1.2811) b -0.3 (2.4) a 3.3 (0.6) a AC + N 24.6905 (4.3327) b -1.1 (2.3) a 6.9 (1.9) a 2G + N 28.7728 (2.4873) ab -10.1 (2.4) a 9.6 (2.1) a 3G + N 39.9477 (4.5368) a -9.8 (2.9) a 4.2 (1.5) a 2018-07-03 AC 19.6577 (1.3086) b -4.7 (1.2) a 1.9 (0.3) a AC + N 21.0764 (2.9049) b -0.4 (2.8) a 4.6 (1.8) a 2G + N 26.0473 (3.7045) ab 0.6 (3.4) a 9.1 (3.8) a 3G + N 37.9630 (5.0284) a 0.9 (3.3) a 3.3 (1.5) a 2018-07-10 AC 21.0447 (1.792) b -7.4 (3.8) a 2.4 (0.5) a AC + N 24.8979 (3.9703) ab -4.2 (3.2) a 4.4 (1.4) a 2G + N 29.1408 (3.9579) ab -6.5 (1.6) a 6.8 (2.8) a 3G + N 38.8081 (5.4095) a -5.0 (2.0) a 2.5 (0.8) a 2018-07-17 AC 19.887 (1.3782) b -2.1 (2.8) a 1.2 (0.5) a AC + N 21.2286 (2.7629) b 1.5 (2.7) a 2.5 (1.0) a 2G + N 26.2522 (3.3881) ab -1.7 (2.1) a 4.0 (2.0) a 3G + N 35.0768 (4.2603) a -1.8 (3.6) a 1.1 (0.6) a 2018-07-24 AC 18.9058 (1.7223) a 1.6 (2.1) b 3.5 (2.9) a AC + N 18.5168 (3.2118) a 2.1 (3.3) a 2.3 (1.0) a 2G + N 26.2085 (4.5007) a -0.2 (1.6) b 3.1 (2.0) a 3G + N 25.0037 (3.9765) a -7.1 (1.5) b 2.1 (0.6) a 90  2018-08-07 AC 21.8706 (1.7318) a -2.6 (2.6) a 0.7 (0.4) a AC + N 18.3491 (1.3972) a -0.5 (2) a 0.6 (0.5) a 2G + N 25.8184 (3.4584) a 0.0 (1.7) a 1.8 (0.9) a 3G + N 22.8633 (3.2578) a -0.2 (1.6) a 0.5 (0.4) a 2018-08-14 AC 23.8192 (1.6582) a -2.2 (3.4) a 1.7 (0.4) ab AC + N 24.9156 (1.6025) a -1.5 (0.8) a 1.7 (0.4) ab 2G + N 33.9758 (6.875) a -4.7 (4.4) a 2.7 (0.6) a 3G + N 22.4345 (2.1627) a 3.5 (5.0) a 0.9 (0.2) b 2018-08-22 AC 16.1738 (1.4341) b -0.5 (3.1) a 0.8 (0.3) a AC + N 14.2479 (1.5394) ab 0.9 (1.9) a 0.3 (0.5) a 2G + N 22.4888 (2.7042) ab -0.1 (1.3) a 1.5 (1.0) a 3G + N 17.9060 (2.2525) a 4.4 (3.6) a 0.7 (0.3) a 2018-08-28 AC 14.8597 (1.5128) a 1.9 (2.7) a 2.2 (0.5) a AC + N 14.4941 (1.3175) a -3.6 (3.0) a 2 (0.2) a 2G + N 21.6562 (3.8302) a 8.1 (3.0) a 2.5 (0.7) a 3G + N 16.2732 (1.7147) a -4.1 (3.9) a 1.2 (0.5) a 2018-09-05 AC 11.2449 (0.9954) a 5.5 (4.6) a 0.1 (0.4) a AC + N 10.9057 (1.2774) a -0.8 (1.6) a 0.4 (0.5) a 2G + N 17.8481 (2.1863) a 2.1 (2.5) a 1.9 (1.4) a 3G + N 15.6033 (1.5923) a 0.6 (2.2) a -0.2 (0.3) a 2018-09-20 AC 20.0974 (2.3668) a 3.1 (3.5) a 2 (0.8) a AC + N 19.1128 (1.0846) a 2.7 (3.1) a 3.3 (0.4) a 2G + N 21.9957 (1.6744) a -7.1 (2.5) a 3.8 (0.6) a 3G + N 25.3246 (2.0644) a -0.6 (8.8) a 4.1 (0.5) a 2018-10-09 AC 24.7474 (5.1055) a -5.2 (2.4) a 5.7 (2.3) a AC + N 19.8665 (2.7672) a -2.0e (0.9) a 3.7 (1.7) a 2G + N 27.4752 (4.388) a -7.6 (3.8) a 8.1 (3.4) a 3G + N 20.3862 (4.204) a -5.9 (6) a 3.4 (1.8) a 2018-10-30 AC 14.5767 (2.8541) a -2.9 (2) a 3.1 (1.4) a AC + N 15.925 (2.2906) a -3.6 (1.5) a 1.2 (0.9) a 2G + N 16.0771 (3.4929) a -2.8 (1.6) a 4.1 (1.2) a 3G + N 14.9944 (2.3915) a -3.4 (3) a 2.2 (1.5) a 2018-11-20 AC 8.9355 (1.2158) a -4.1 (1.8) a 11.6 (5.6) ab AC + N 8.8872 (0.8807) a -0.3 (1.4) a 12.1 (5.5) ab 2G + N 10.8991 (0.8165) a -1.4 (3.7) a 36.5 (13.6) a 3G + N 8.9722 (0.7556) a -2.5 (1.4) a 4.4 (2.6) b 2018-12-11 AC -1.0667 (1.6873) b 9.6 (5.9) a 0.2 (0.4) a AC + N 0.791 (0.2593) ab -1 (1.9) a 0.9 (0.6) a 2G + N 2.4272 (0.589) a 0.3 (3.3) a 0.2 (0.5) a 3G + N 1.3813 (0.562) ab 2 (1.8) a 0.6 (0.6) a 2019-01-15 AC 6.6847 (1.2313) a 5.0 (4.6) a 0.9 (0.5) a AC + N 5.3577 (1.1076) a 3.3 (2.3) a 0.7 (0.4) a 2G + N 8.4088 (1.9354) a -1.9 (2.7) a 1.0 (0.1) a 3G + N 7.1368 (2.5923) a -2.8 (2.3) a 1.2 (0.6) a 2019-02-07 AC 16.9543 (2.7576) a 4.7 (4.1) a 2.1 (1) a AC + N 11.7504 (3.7715) a 8.1 (1.5) a 1.9 (0.8) a 2G + N 12.281 (3.2472) a 6.2 (4.9) a 4.2 (0.8) a 3G + N 7.4695 (2.7099) a 2 (2.6) a 4 (1) a 2019-02-26 AC 11.6701 (2.3109) a 5.8 (8.9) a 0.4 (0.5) a AC + N 8.5856 (4.0404) a -7.4 (6.1) a 1.6 (1.4) a 2G + N 14.6084 (3.4802) a 0.6 (1.8) a 4.0 (1.5) a 3G + N 4.8226 (1.1819) a 0.0 (3.6) a 3.9 (3) a 2019-03-19 AC 9.1105 (1.9978) a 8.3 (6.7) a 2 (0.4) b AC + N 9.6911 (1.9756) a 0.9 (1.1) a 2 (0.7) b 2G + N 19.4842 (8.7813) a -1.2 (1.7) a 1.4 (0.9) b 3G + N 14.7199 (2.1351) a -2.6 (2.7) a 7.1 (1.9) a  

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