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Greenhouse gas exchange above potato and pea fields in the lower Fraser Valley in British Columbia, Canada Quan, Ningyu 2021

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 GREENHOUSE GAS EXCHANGE ABOVE POTATO AND PEA FIELDS IN THE LOWER FRASER VALLEY IN BRITISH COLUMBIA, CANADA by  Ningyu Quan  B.Sc., China Agricultural University, 2018  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Soil Science)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   January 2021  © Ningyu Quan, 2021 ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:  Greenhouse gas exchange above potato and pea fields in the lower Fraser Valley in British Columbia, Canada  submitted by Ningyu Quan in partial fulfillment of the requirements for the degree of Master of Science in Soil Science  Examining Committee: Dr. T. Andrew Black, Land and Food Systems, UBC Supervisor  Dr. Sean Smukler, Land and Food Systems, UBC Supervisory Committee Member  Dr. Rachhpal Jassal, Land and Food Systems, UBC Supervisory Committee Member Dr. Thorsten Knipfer, Land and Food Systems, UBC Additional Examiner   Additional Supervisory Committee Members: Dr. Sara Knox, Department of Geography, UBC Supervisory Committee Member iii  Abstract The three main biogenic greenhouse gases (GHGs), nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2), are associated with agricultural production and strongly affected by environmental factors, which makes mitigation of GHG emissions a more challenging task in intensive agricultural system in the context of global climate change. Quantification of GHG emissions is of great interest in agroecosystems to better understand flux exchange between agroecosystems and the atmosphere and to provide knowledge for climate change related policy making. However, in Canada, these studies are largely limited to Ontario and the Canadian Prairie provinces, and GHG emissions in the main cropping systems in British Columbia have scarcely been studied. Therefore, this study is aimed at making continuous measurements of N2O, CO2 and CH4 from potato and pea crops in the lower Fraser Valley using the eddy covariance (EC) technique. Furthermore, to cope with the issue of slight spatial inhomogeneity at this study site, flux footprint analysis was used, coupled with EC and closed-chamber measurements. I found that flux footprint correction had the largest effect on N2O flux compared with CO2 flux because of more pronounced difference of N2O flux between two different land surfaces (crop and edge areas). After flux footprint correction, the potato and pea crops were both weak CO2 sinks with annual net ecosystem exchange values being -57 and -97 g C m-2 yr-1, respectively. After taking carbon (C) export via crop harvest into account, the potato crop shifted from being a C sink to a C source of 375 g C m-2 yr-1, while the pea crop became near neutral sequestering only 19 g C m-2 yr-1. Annual GHG budget was quantified by converting N2O and CH4 to CO2 equivalents using global warming potential on a 100-year timescale, which is 298 for N2O and 34 for CH4. The annual GHG budgets were 481 and 200 g CO2e m-2 yr-1 for the potato and pea crops respectively. For both potato and pea crops, N2O contributed the largest proportion to annual total CO2e and outweighed iv  the CO2 uptake from the atmosphere, making both crop fields net sources of GHGs. v  Lay summary Agricultural soils are a significant source of greenhouse gases (GHGs), thus posing a threat to global climate change. Developing Beneficial Management Practices (BMPs) to mitigate climate change requires reliable estimates of GHG emissions. This study quantified nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2) emissions from potato and pea crops in the lower Fraser Valley, British Columbia. With carbon (C) export (produce removed from the field) taken into account, the potato crop was a C source releasing 375 g C m-2 yr-1, while the pea crop was near neutral sequestering only 19 g C m-2 yr-1. The annual totals of CO2e of three GHGs (emitted) for potato and pea crops were 481 and 200 g CO2e m-2 yr-1, respectively, with N2O contributing the largest proportion to annual total CO2e and outweighing the CO2 uptake from the atmosphere.  vi  Preface With the assistance and support of the UBC Biometeorology and Soil Physics Group, I was responsible for instrumentation maintenance, soil sampling, data analysis and some of the manual chamber measurements. The construction and installation of the eddy covariance (EC) system was carried out by Zoran Nesic, Jugoslav Kitanovic and Brian Wang. The soil and climate sensors were installed with the assistance of Brian Wang. The experiment comparing net radiation (Rn) and soil heat flux (G) in the potato field and edge area was also performed with the assistance of Brian Wang. The leaf area index (LAI) and crop height (hc) of potato crop were measured by Oscar Zimmerman. The manual chamber measurements were made by Paula Resque Porto and Chantel Chizen from May 27, 2018 to Jun 28, 2019. The code for flux calculation of raw data and gap filling procedure were programmed by members of the UBC Biometeorology and Soil Physics Group and have been used in the analysis of data from the Group’s other flux sites. Data from the Vancouver International Airport (YVR) was provided by Environment Canada. This research has not been previously published wholly or in part.  vii  Table of Contents  Abstract ......................................................................................................................................... iii Lay summary ..................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures ...............................................................................................................................xv List of Acronyms and Symbols .............................................................................................. xxxii Acknowledgements ............................................................................................................... xxxvii Dedication ............................................................................................................................... xxxix Chapter 1: Introduction ............................................................................................................... 1 1.1 Agricultural greenhouse gas (GHG) emissions .......................................................... 1 1.2 Potato and pea production in Delta, BC...................................................................... 4 1.3 Agricultural Greenhouse Gas Program (AGGP) ........................................................ 4 1.4 Measurement of GHG emissions ................................................................................ 5 1.4.1 Eddy covariance (EC) technique ........................................................................ 5 1.4.2 Dynamic closed chamber method ....................................................................... 6 1.4.3 Flux footprint model ........................................................................................... 7 1.5 Research objectives of this thesis ............................................................................... 8 Chapter 2: Methods ...................................................................................................................... 9 2.1 Study site ..................................................................................................................... 9 2.2 Eddy covariance (EC) measurement ......................................................................... 11 viii  2.2.1 Location of the EC system ................................................................................ 11 2.2.2 EC instrumentation and measurements ............................................................. 12 2.2.3 EC flux calculations .......................................................................................... 14 2.2.4 Flux quality control and data analysis .............................................................. 15 2.2.5 Flux gap-filling ................................................................................................. 15 2.2.6 NEE partitioning and environmental controls on C fluxes ............................... 16 2.2.7 Net ecosystem carbon budget (NECB) of crops ............................................... 17 2.2.8 Calculation of global warming potential (GWP) .............................................. 18 2.3 Manual FTIR chamber measurements ...................................................................... 18 2.3.1 Manual FTIR chamber measurements and calculations ................................... 18 2.3.2 Gap-filling of chamber fluxes ........................................................................... 20 2.4 Ancillary soil and climate measurements ................................................................. 20 2.5 Flux footprint analysis .............................................................................................. 22 2.5.1 Flux footprint model ......................................................................................... 22 2.5.2 Flux footprint corrections of EC fluxes ............................................................ 23 Chapter 3: Results and Discussion ............................................................................................ 27 3.1 Climate and soil variables ......................................................................................... 27 3.1.1 Climate measurements ...................................................................................... 27 3.1.2 Soil measurements ............................................................................................ 29 3.2 Wind direction and speed.......................................................................................... 30 3.3 Radiation balance components ................................................................................. 32 3.4 Energy balance components and energy balance closure ......................................... 33 3.4.1 Energy balance components ............................................................................. 33 ix  3.4.2 Energy balance closure (EBC) .......................................................................... 34 3.5 Flux footprint analysis .............................................................................................. 36 3.6 N2O flux .................................................................................................................... 38 3.6.1 Temporal variation of EC-measured N2O flux ................................................. 38 3.6.2 Comparison of EC-measured and flux-footprint-corrected N2O fluxes ........... 40 3.7 CH4 flux .................................................................................................................... 45 3.8 CO2 flux .................................................................................................................... 48 3.8.1 Temporal variation of Re, GPP and NEE fluxes ............................................... 48 3.8.2 Comparison of EC-measured and flux-footprint-corrected Re ......................... 51 3.8.3 Sensitivity analysis of flux-footprint-corrected GPP with respect to k………..55 3.8.4 Sensitivity analysis of flux-footprint-corrected NEE with respect to k………. 59 3.8.5 Annual carbon balance and net ecosystem carbon budget of potato and pea crops……… ...................................................................................................................... 64 3.9 Annual GHG budgets of potato and pea crops ......................................................... 68         3.10     Environmental controls on C fluxes………………………………………………. 70                3.10.1      Relationship between PAR and GPP………………………………………… 70             3.10.2      Relationship between ecosystem respiration and soil temperature………….  73 Chapter 4: Conclusions .............................................................................................................. 76 4.1 Summary of key findings .......................................................................................... 76 4.2 Management recommendations ................................................................................ 77         4.3       Implications for future research…………………………………………………….77  Bibliography .................................................................................................................................79 Appendices ....................................................................................................................................91 x  Appendix A Location of the study site ................................................................................. 91 Appendix B Site photos showing instrumentation and field conditions ............................... 92 B.1 Instrumentation of the eddy covariance (EC) system ................................. 92 B.2                Soil and climate sensors .............................................................................. 93 B.3 Photos of the potato field ............................................................................ 94 B.4 Photos of the field during the non-growing season .................................... 95 B.5 Phtotos of the pea field ............................................................................... 96 B.6 Photos of the edge area ............................................................................... 97 Appendix C Management operations on the farm ................................................................ 98 Appendix D Friction velocity (u*) threshold determination.................................................. 99 Appendix E Energy balance closure experiment in summer 2018 ..................................... 100 Appendix F Manual chamber measurements of GHG fluxes ............................................. 102 Appendix G Tarpaulin experiment ..................................................................................... 104 Appendix H Leaf area index (LAI) and canopy height (hc) measurements of potatoes ..... 105 H.1 Leaf area index (LAI) of potatoes ............................................................. 105 H.2 Canopy height (hc) of potatoes .................................................................. 105 Appendix I Comparison of LGR and LI-7200 gas analyzer measurements of latent heat fluxes ........................................................................................................................................... 106 Appendix J Flux footprint climatology ............................................................................... 107  xi  List of Tables Table 1: Comparison of mean monthly air temperature (𝑇a) and precipitation (𝑃) at the site with the 30-year (1981 – 2010) normal at the Vancouver International Airport (YVR) climate station (49.19°N, 123.18°W). ................................................................................................................... 29 Table 2: Coefficients and model parameters for the linear relationship between half-hourly N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O). .............. 42 Table 3: Monthly total values of N2O fluxes measured by EC (𝐹N2O) and N2O fluxes corrected for the edge effect (𝑄c_N2O) during the study period..................................................................... 43 Table 4: Coefficients and model parameters for the linear relationship between monthly total N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O). ...... 45 Table 5: Monthly total values of CH4 fluxes measured by EC (𝐹CH4) during the study period. .. 47 Table 6: Monthly total values of net ecosystem exchange measured by EC (NEE), partitioned ecosystem respiration (Re) and partitioned gross primary production (GPP) without flux footprint correction during the study period. ............................................................................................... 51 Table 7: Coefficients and model parameters for the linear relationship between half-hourly nighttime net ecosystem exchange corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration measured by EC (Re) during the study period. ........................................................... 53 Table 8: Coefficients and model parameters for the linear relationship between monthly totals of nighttime ecosystem respiration corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration (Re) during the study period. ....................................................................................... 54 Table 9: Monthly total values of ecosystem respiration (24-h Re) partitioned from EC-measured NEE and ecosystem respiration corrected for the edge effect (Qc_ nee) during the study period. . 54 xii  Table 10: Coefficients and model parameters for the linear relationships between half-hourly gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the study period. ....................................................................................... 57 Table 11: Monthly total values of gross primary production (GPP) partitioned from EC-measured NEE and gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values during the study period. ......................................................................................... 58 Table 12: Coefficients and model parameters for the linear relationships between monthly total values of gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the study period. .............................................................. 59 Table 13: Coefficients and model parameters for linear relationships between half-hourly net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period. ............................................................................................................................ 61 Table 14: Monthly total values of net ecosystem exchange measured by EC (NEE) and net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values during the study period. ................................................................................................................ 62 Table 15: Coefficients and model parameters for linear relationships between monthly total values of net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period. ............................................................................................... 63 xiii  Table 16: Annual Re, GPP and NEE for the potato and pea crops without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values. The potato year was from May 15, 2018 to May 14, 2019 and the pea year was from Oct 1, 2018 to Sep 30, 2019.................................................................................................................................. 67 Table 17: Annual NEE, carbon removal from crop harvest and NECB for the potato and pea crops without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values. The potato year was from May 15, 2018 to May 14, 2019 and the pea year was from Oct 1, 2018 to Sep 30, 2019. .................................................................... 68 Table 18: CO2, N2O and CH4 expressed in CO2 equivalents and total CO2e for the potato and pea years (g CO2e m-2 yr-1) without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values. The potato year was from May 15, 2018 to May 14, 2019 and the pea year was from Oct 1, 2018 to Sep 30, 2019. The values in the brackets are the contributions of each GHG to total CO2e. ........................................................................ 70 Table 19: Coefficients and model parameters of rectangular hyperbolic relationship (Eq (2.7)) between half-hourly gross primary production (GPP) and photosynthetically active radiation (PAR) for the potato growing season, non-growing season and pea growing season without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values. ............................................................................................................................... 73 Table 20: Coefficients and model parameters of linear relationship between ln (Re) and ln (Qc_er) and Ts (ln𝑅e = 𝐴 + 𝐵𝑇s). ............................................................................................................. 75 Table 21: Coefficients and model parameters of the exponential-temperature relationship with a hyperbolic soil moisture relationship between Re and Qc_er and 𝑇s (𝑅e = (𝑎 + 𝑏𝜃 +𝑐𝜃)𝑅10𝑄10(𝑇s − 10)/10). ......................................................................................................... 75 xiv   Table C. 1: Farm operations for potato and pea crops during the study period. ........................... 98  Table E. 1: Slope, intercept and coefficient of determination of linear relationships between (Rn -G) and (H + λE) measured in the edge area and corrected using linear relationships between measurements in the potato field and in the edge area from DOY 249 to 254 in 2018. ............. 101  xv  List of Figures Figure 1: Three landscape components of the experimental site, a) crop areas (west and east), b) farm road and machinery turn around area (west and east) and c) water-filled ditch with dense grass (in the middle). b and c landscape components were defined as “edge areas” in this thesis........................................................................................................................................................ 11 Figure 2: The location of the EC tower (this figure is enlarged from Fig. 1). .............................. 12 Figure 3: The timeline of potato growing season (GS), non-growing season (NGS) and pea growing season (GS). The time periods of EC measurements, defined potato year and pea year were indicated by red, brown and green arrows, respectively. ..................................................... 18 Figure 4: Flowchart of the data analysis procedure for obtaining ecosystem respiration (𝑅e), gross primary production (𝑄c_gpp) and net ecosystem exchange (𝑄c_nee). Equation numbers are included in the brackets. ............................................................................................................... 25 Figure 5: Climate variables at the site during the study period. Panel a) shows daily average air temperature (Ta), panel b) shows 1-day precipitation (𝑃Daily) and cumulative precipitation (𝑃Cum), panel c) shows daily average photosynthetically active radiation (PAR), panel d) shows daily average daytime vapour pressure deficit (𝐷day). Daytime was determined by 𝑆d > 0. Climate measurements started from mid-July in 2018. The data of from mid-May to mid-July was filled by using data from YVR. .............................................................................................................. 28 Figure 6: Soil variables at the site during the study period. Panel a) shows daily average soil temperature (𝑇s) at the 5-cm depth (black solid line) and daily average air temperature (𝑇a) (blue solid line). Panel b) shows daily average volumetric water content (𝜃s) measured at the 5-cm depth. ............................................................................................................................................. 30 xvi  Figure 7: Wind rose during a) the entire study period, b) the potato growing season (May 15, 2018 to Sep 18, 2018), c) the non-growing season (Sep 19, 2018 to Jun 20, 2019) and d) the pea growing season (Jun 21, 2019 to Aug 23, 2019). ......................................................................... 31 Figure 8: Wind distributions for the west field (range from 210˚ to 330˚; blue colour) and east field (range from 50˚ to 130˚; orange colour) during the daytime (solid) and nighttime (shaded) for three time periods, potato growing season (GS), non-growing season (NGS) and pea growing season (GS). .................................................................................................................................. 32 Figure 9: Monthly mean radiation components of a) upwelling longwave (𝐿u; black dashed line), downwelling longwave (𝐿d; blue dashed line), upwelling shortwave (𝑆u; black solid line), downwelling shortwave (𝑆d; blue solid line) and net radiation (𝑅n; red solid line); b) albedo at the site during the study period. .......................................................................................................... 33 Figure 10: Monthly mean energy balance components of a) net radiation (𝑅n; red solid line), sensible heat flux (𝐻; black thick solid line), latent heat flux (𝜆𝐸; black thin solid line) and soil heat flux (𝐺; black dashed line); b) monthly Bowen ratio (𝛽) at the site during the study period........................................................................................................................................................ 34 Figure 11: Energy balance closure (EBC) calculated as the slope of the linear regression of half-hourly 𝐻 + 𝜆𝐸 vs. 𝑅n – 𝐺 measurements at the site. EBC was 0.63 during the study period. The solid black line is the linear regression and the dashed line is the 1:1 line. .................................. 35 Figure 12: Half-hourly flux footprint contributions (f) during the study period calculated using the two-dimensional Flux Footprint Prediction (FFP) model (Kljun et al., 2015) for two landscape components, the crop (subscript c) (a and b) and edge areas (subscript e) (c and d), and the total of these two components (e and f). The red coloured bars are the two wind-direction ranges selected for filtering the data (N2O, CO2 and CH4 fluxes) for the east and west fields xvii  (50~130˚ for the east field and 210~330˚ for the west field). In the case of the CH4 fluxes, it was to minimize the impact from the ditch even though no flux footprint correction was made. ....... 37 Figure 13: Daily average N2O fluxes measured by EC (𝐹N2O) during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer (F) application events and tillage (T) events, respectively. The horizontal line during July 2019 represents the time period when the LGR spectrometer malfunctioned. The black dashed line from mid-May to mid-June was obtained using chamber measurements and linear interpolation. ....................................................................................................................... 39 Figure 14: Daily average N2O fluxes measured by EC (𝐹N2O; black solid line) and N2O fluxes corrected for the edge effect (𝑄c_N2O; red solid line) using the flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer (F) application events and tillage (T) events, respectively. The horizontal line during July 2019 represents the time period when the LGR spectrometer malfunctioned. The red dashed line (𝑄c_N2O) from mid-May to mid-June was obtained using chamber measurements made in the crop and linear interpolation. The black dashed line (𝐹N2O) from mid-May to mid-June was xviii  obtained using estimated 𝑄c_N2O and a fixed coefficient ( 𝑄c_N2O/𝐹N2O = 1.38 during the potato GS). ............................................................................................................................................... 41 Figure 15: The linear relationship between half-hourly N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O) during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid line is the linear regression line and the dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 2. ................................................................................................. 42 Figure 16: Comparisons of total N2O fluxes measured by EC (𝐹N2O) and N2O fluxes corrected for the edge effect (𝑄c_N2O) during the potato growing season (west and east fields as a whole), non-growing season (west and east fields as a whole) and pea growing season (east field only). The darker columns indicate 𝐹N2O and the lighter columns indicate 𝑄c_N2O. ............................... 44 Figure 17: The linear relationship between monthly total N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O) during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The black solid line is the linear regression line and the dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 4. The data points don’t include those for May and June in 2018 because it didn’t involve EC measurements or flux footprint corrections until mid-June. ........................... 44 Figure 18: Daily average CH4 fluxes measured by EC (𝐹CH4) during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing xix  seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer (F) application events and tillage (T) events, respectively. The horizontal line during July 2019 represents the time period when the LGR spectrometer malfunctioned. The dashed line from mid-May to mid-June was obtained using chamber measurements made in the crop and linear interpolation. ........................................................................................................ 46 Figure 19: Comparisons of total CH4 fluxes measured by EC (𝐹CH4) during the potato growing season (west and east fields as a whole), non-growing season (west and east fields as a whole) and pea growing season (east field only). ..................................................................................... 48 Figure 20: Daily average net ecosystem exchange measured by EC (NEE; black solid line), partitioned ecosystem respiration (Re; red solid line) and partitioned gross primary production (GPP; blue solid line). The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The dashed lines from mid-May to mid-June was obtained based on chamber measurements (Re) made in the crop and a rough estimation from the progression behavior of GPP at the beginning of the crop growing season. .......................................................................................................... 50 Figure 21: Diurnal patterns of a) downwelling shortwave radiation (Sd) and b) EC-measured NEE on selected days (from July 10 to July 15 in 2018) (west and east potato fields as a whole)........................................................................................................................................................ 50 Figure 22: Daily average ecosystem respiration partitioned from EC-measured NEE (Re; black solid line) and ecosystem respiration corrected for the edge effect (Qc_ er; red solid line) using the xx  flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The red dashed line (𝑄c_ er) from mid-May to mid-June was obtained using chamber measurements made in the crop and linear interpolation. The black dashed line (Re) from mid-May to mid-June was obtained using estimated 𝑄c_er and a fixed coefficient ((𝑄c_er/𝑅e = 1.22) during the potato GS). .................................................................................................................................... 52 Figure 23: The linear relationship between half-hourly nighttime ecosystem respiration corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration measured by EC (Re) during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid line is the linear regression line and the dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 7. ........................................................... 53 Figure 24: The linear relationship between monthly totals of nighttime ecosystem respiration corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration (Re) during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 8. The data points don’t include those for May and June in 2018 because it didn’t involve EC measurements or flux footprint corrections until mid-June. ......................................................... 53 xxi  Figure 25: Daily average gross primary production partitioned from EC-measured NEE (GPP; black solid line) and gross primary production corrected for the edge effect (Qc_gpp; red solid line) with a k (k = Qe_ gpp / Qc_ gpp) value of 1.0 using the flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The red dashed line (Qc_gpp) from mid-May to mid-June was obtained based on a rough estimation from the progression behavior of GPP at the beginning of the crop growing season and linear interpolation. The black dashed line (GPP) from mid-May to mid-June was obtained using estimated Qc_gpp and a fixed coefficient (Qc_gpp /GPP = 0.98 during the potato GS). ........................................................................................................... 56 Figure 26: Linear relationships between half-hourly gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 10. .............................................................................. 57 Figure 27: Linear relationships between monthly total values of gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the xxii  study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 12. ......................................................... 59 Figure 28: Daily average net ecosystem exchange measured by EC (NEE; black solid line) and gross primary production corrected for the edge effect (Qc_nee; red solid line) with a k (k = Qe_ gpp / Qc_ gpp) value of 1.0 using the flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The red dashed line (Qc_nee) from mid-May to mid-June was obtained from estimated Qc_er and Qc_gpp. The black dashed line (NEE) from mid-May to mid-June was obtained from estimated Re and GPP. ........................................................................................... 60 Figure 29: Linear relationships between half-hourly net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 13. ............................................................................................... 61 Figure 30: Linear relationships between monthly total values of net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) xxiii  k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 15. .............................................................................. 63 Figure 31: Cumulative fluxes of a) net ecosystem exchange (NEE), b) gross primary production (GPP) and c) ecosystem respiration (Re) during the study period. The black solid lines in panels a, b and c indicate EC-measured NEE, partitioned GPP and Re during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The coloured dashed lines in panels a, b and c indicate NEE (Qc_nee), GPP (Qc_gpp) and Re (Qc_er) corrected for the edge effect. The potato growing season, non-growing season and pea growing season are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red horizontal arrows at the top of the figure indicate the duration of the potato year (May 15, 2018 to May 14, 2019) and pea year (Oct 1, 2018 to Sep 30, 2019) defined in this study. .................. 67 Figure 32: Rectangular hyperbolic relationship (Eq (2.7)) between half-hourly gross primary production (GPP) and photosynthetically active radiation (PAR) for the potato growing season (a-e), nongrowing season (f-j) and pea growing season (k-o) for different k values from 0.8 to 1.8. The first column (a, f and k) shows EC-measured GPP, and the other columns show flux footprint corrected Qc_gpp.  Coefficients and model parameters are shown in Table 19. .............. 72 Figure 33: Linear relationship a) between ln (Re) and Ts; b) between ln (Qc_er) and Ts. Coefficients and model parameters are shown in Table 20. .............................................................................. 74  xxiv  Figure A. 1: Reynelda Farm indicated by a red star is located on the Westham Island, which is on the southwest edge of the Fraser River delta. ............................................................................... 91  Figure B. 1: The eddy covariance (EC) system located above the water-filled ditch. The red squares indicate the location of four fans which were used to maintain a constant trailer temperature for the LGR spectrometer to work properly. ............................................................ 92 Figure B. 2: Photos of the sonic anemometer (a), air sampling inlet tubes of the LGR and LI-7200 instruments (c), and trailer with LGR spectrometer inside (d). ........................................... 92 Figure B. 3: Photos of the LGR spectrometer (a),  pump for the spectrometer (b), site computer (c), and communication system for daily transmission of data to the UBC Biometeorology and Soil Physics Group (d). ................................................................................................................. 93 Figure B. 4: Photo of the location of the soil sensors. Instead of being installed in the cropped area, soil sensors were installed near the grass in the edge area. .................................................. 93 Figure B. 5: Photos of a) the soil sensors measuring soil temperatures and soil moistures at the 5-cm, 20-cm and 60-cm depths and b) the net radiometer installed in the edge area above the grass........................................................................................................................................................ 94 Figure B. 6: Photos of the potato field on a) Jun 19, 2018, b) Jul 17, 2018, c) Jul 27, 2018, d) Aug 15, 2018, e) Sep 11, 2018 (one week before potato harvest) and f) Sep 26, 2018 (one week after potato harvest). ..................................................................................................................... 94 Figure B. 7: Photos of field conditions on a) Feb 5, 2019 (snow cover), b) Feb 19 , 2019 (poor drainage during winter), c) Mar 22, 2019 (soil cracks in spring) and d) Jun 10, 2019 (after tillage). .......................................................................................................................................... 95 xxv  Figure B. 8: Photos of the pea field on a) Jun 28, 2019, b) Jul 4, 2019, c) Jul 16, 2019, d) Aug 13, 2019, e) Aug 23, 2019 (pea harvest date) and f) Sep 29, 2019. .................................................... 96 Figure B. 9: Photos of the edge area during the study period. a and b show the edge areas which consisted of water-filled ditch with grass and farm road both in the west and east during the potato growing season in 2018, respectively. c and d show the dead grass in the edge area during winter months until the end of March. e, f and g show the regrowth of grass in April, May and June. The significant difference between crop and edge areas can be seen. h shows peas were planted adjacent to the grass without being left a farm road as pre-planting of peas (see photo b))........................................................................................................................................................ 97  Figure D. 1: Relationship between nighttime ecosystem respiration (Re) and friction velocity (u*) during the study period. u* was binned in 20 classes from 0 to 0.5 m s-1. .................................... 99  Figure E. 1: Photos of a) a net radiometer mounted 1 m above the potatoes (down-facing view included three potato rows and exposed soil surface) and b) soil heat flux plate inserted in the soil adjacent to a potato row. ...................................................................................................... 100 Figure E. 2: a) Net radiation (Rn) and b) soil heat flux (G) measured in the edge area (before correction; black solid line) and corrected (red solid line) using linear relationships between measurements in the potato field and in the edge area from DOY 249 to 254 in 2018. ............. 100  Figure F. 1: Chamber measurements of N2O fluxes in the edge area from May 27, 2018 to Aug 23, 2019. The data points are the average value of N2O fluxes in the west and east edge areas and xxvi  error bars are also shown. Missing error bars are due to missing measurements or outliers being removed....................................................................................................................................... 102 Figure F. 2: Chamber measurements of CH4 fluxes in the edge area from May 27, 2018 to Aug 23, 2019. The data points are the average value of CH4 fluxes in the west and east edge areas and error bars are also shown. Missing error bars are due to missing measurements or outliers being removed....................................................................................................................................... 102 Figure F. 3:Chamber measurements of CO2 fluxes in the edge area from May 27, 2018 to Aug 23, 2019. The data points are the average value of CO2 fluxes in the west and east edge areas and error bars are also shown. Missing error bars are due to missing measurements or outliers being removed....................................................................................................................................... 103  Figure G. 1: Photos of grass immediately northwest of the EC tower covered by tarps. ........... 104  Figure H. 1: Effective leaf area index (LAI) of potatoes. Data points are shown as the average value of 3 measurements and error bars are also shown. ............................................................ 105 Figure H. 2: Canopy height (hc) of potatoes. Data points are shown as the average value of 3 measurements and error bars are also shown. ............................................................................. 105  Figure I. 1: Comparison of latent heat flux (𝜆𝐸) measurements between the Los Gatos Research (𝜆𝐸𝐿GR) and LI-COR (𝜆𝐸LI7200) gas analyzers. The regression line is the black solid line and the dashed line is the 1:1 line. ........................................................................................................... 106  xxvii  Figure J. 1: Daytime flux footprint climatology (before filtering for wind directions determined for the west field) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 107 Figure J. 2: Daytime flux footprint climatology (after filtering for wind directions determined for the west field, 210˚ to 330˚) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .............................................................................................................................. 107 Figure J. 3: Nighttime flux footprint climatology (before filtering for wind directions determined for the west field) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint xxviii  contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 108 Figure J. 4: Nighttime flux footprint climatology (after filtering for wind directions determined for the west field, 210˚ to 330˚) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................. 108 Figure J. 5: Daytime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 109 Figure J. 6: Daytime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured xxix  fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .............................................................................................................................. 109 Figure J. 7: Nighttime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 110 Figure J. 8: Nighttime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................. 110 Figure J. 9: Daytime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines xxx  indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 111 Figure J. 10: Daytime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 111 Figure J. 11: Nighttime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 112 Figure J. 12: Nighttime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the xxxi  distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. .................................................................................................................................... 112  xxxii  List of Acronyms and Symbols  Acronyms and symbols Units Definitions A AAFC AGGP BC BMP 𝑐p C 𝐶export 𝐶import CH4 CO2 CO2e c D EBC EC e 𝐹c 𝐹CH4 FCRN m-2     kJ kg-1 K-1  g C m-2 yr-1 g C m-2 yr-1   g CO2e m-2 yr-1  kPa    μmol m-2 s-1 μmol m-2 s-1  Soil surface area of the chamber collar Agriculture and Agri-Food Canada Agricultural Greenhouse Gas Program British Columbia Beneficial Management Practice Specific heat of air Carbon Carbon export from the field Carbon import into the field Methane Carbon dioxide Carbon dioxide equivalent Crop area Vapour pressure deficit Energy balance closure Eddy covariance Edge area Carbon dioxide flux Methane flux Fluxnet Canada Research Network xxxiii  𝐹N2O 𝐹s FTIR 𝑓 𝐺 GHG GPP GPPmax GRA GS GWP 𝐻 ℎc IPCC IRGA 𝑘 LAI LES 𝐿d 𝐿𝑢 MDV μmol m-2 s-1 μmol m-2 s-1   W m-2  μmol m-2 s-1 μmol m-2 s-1   g g-1 W m-2 m    m2 leaf m-2 ground  W m-2 W m-2 W m-2 Nitrous oxide flux Scalar flux Fourier Transform Infrared Spectrometer Flux footprint contribution Soil heat flux Greenhouse gas Gross primary production Ecosystem assimilation capacity Global Research Alliance Growing season Global warming potential Sensible heat flux Canopy height Intergovernmental Panel on Climate Change Infrared gas analyzer Ratio of edge grass GPP to crop GPP Leaf area index Large eddy simulation Downwelling longwave radiation Upwelling longwave radiation Mean diurnal variation xxxiv  𝑚s N NDC NECB NEE NEP NGS NH4NO3 𝑃 PAR PST 𝑄 𝑄10  𝑄c_N2O 𝑄c_er 𝑄c_gpp 𝑄c_nee 𝑄e_N2O 𝑄e_er 𝑄e_gpp 𝑄e_nee g mol-1    μmol m-2 s-1 μmol m-2 s-1   mm; mm day-1 μmol m-2 s-1  μmol m-2 s-1   μmol m-2 s-1 μmol m-2 s-1 μmol m-2 s-1 μmol m-2 s-1 μmol m-2 s-1 μmol m-2 s-1 μmol m-2 s-1 μmol m-2 s-1 Molar mass of scalar flux Nitrogen Nationally determined contribution Net ecosystem carbon budget Net ecosystem exchange Net ecosystem production Non-growing season Ammonium nitrate Precipitation Photosynthetically active radiation Pacific standard time Surface flux Exponential increase in ecosystem respiration with a 10℃ increase in temperature N2O flux in the crop area Ecosystem respiration in the crop area Gross primary production in the crop area Net ecosystem exchange in the crop area N2O flux in the edge area Ecosystem respiration in the edge area Gross primary production in the edge area Net ecosystem exchange in the edge area xxxv  𝑅10 𝑅a 𝑅e 𝑅n SOC 𝑆d 𝑆u 𝑠 𝑇a 𝑇s 𝑇sonic UBC 𝑢 𝑢∗ 𝑉 𝑣 WPL 𝑤 𝑧m 𝛼 𝛽 𝜃s μmol m-2 s-1 μmol m-2 s-1 μmol m-2 s-1 W m-2  W m-2 W m-2 mmol mol-1 ℃ ℃ ℃  m s-1 m s-1 m3 m s-1  m s-1 m μmol CO2 (μmol photons)-1  m3 m-3 Reference ecosystem respiration rate at 10℃ Autotropic respiration Ecosystem respiration Net radiation Soil organic carbon Downwelling shortwave radiation Upwelling shortwave radiation Mixing ratio Air temperature Soil temperature Sonic temperature University of British Columbia Horizontal wind speed Friction velocity Volume of the chamber headspace Cross wind speed Webb-Pearman-Leuning corrections Vertical wind speed Measurement height Initial light response efficiency Bowen ratio Soil volumetric water content xxxvi  𝜆 𝜆𝐸 𝜌 𝜌d kJ kg-1 W m-2 kg m-3 kg m-3 Latent heat of vaporization of water Latent heat flux Air density Dry air density  xxxvii  Acknowledgements I am truly fortunate to be supervised by Dr. Andrew Black, whose passion and dedication to science have inspired me a lot and motivated me to learn as much as I can. I couldn’t remember how much time he has dedicated to talking with me about my project and helping me clear my confusions about some complicated questions. Without these, I couldn’t have achieved so much progress during my MSc study. He made my journey in the science world the most precious part of my graduate school life. I would also like to thank Drs. Rachhpal Jassal, Sean Smukler and Sara Knox for their insightful comments and valuable input to this work. Their extensive knowledge and scientific enthusiasm have enlarged my vision of science and enriched my study.  The challenging construction and maintenance of the eddy covariance (EC) tower in my study site could not have been done without engineering skills of Zoran Nesic and Jugoslav Kitanovic. I would like to express my special thanks to Zoran Nesic, who is extremely patient and is always willing to help. I could always benefit a lot from his technical advice and knowledge. Without these, I couldn’t have succeeded in the difficult tasks in the field. I would also like to thank Paula Resque Porto, Brian Wang and Oscar Zimmerman who provided technical support in the field. I really enjoyed and also miss the time when we were doing the field work together. It is an unforgettable and precious experience during my graduate student life. Special thanks are owed to my colleagues Chitra Chopra, Patrick Pow and Sung-Ching Lee, who always kindly shared their knowledge and provided constructive suggestions, which contributed a lot to my thesis writing. Their help and support made difficult times when I was learning new things much easier to get through.  xxxviii  I offer sincere gratitude to Stan Reynolds and Hugh Reynolds, who were very helpful and collaborative and let me work on their farm. I really appreciate their understanding of the importance of studying greenhouse gas (GHG) emissions from local high-value crops at a farm scale. It was a pleasure to work with them and to have their involvement in this climate-change related research project. This study was a part of the Agricultural Greenhouse Gases Program (AGGP) funded by Agriculture and Agri-food Canada (AAFC). I was supported by Research Assistantship through this program and an NSERC Discovery Grant (T.A. Black). Additional scholarships and travel grants were provided by the UBC Faculty of Land and Food systems and the UBC Faculty of Graduate and Postdoctoral Studies.    xxxix  Dedication To my loving family, who has supported me throughout my years of education, both morally and financially. 1  Chapter 1: Introduction 1.1 Agricultural greenhouse gas (GHG) emissions As the global population expands rapidly, there is a surge in global food demand. A 100-110% increase in global crop demand from 2005 to 2050 has been predicted (Tilman et al., 2011). This drastic increase has exerted considerable pressures on agroecosystems (Kanianska, 2016). While agroecosystems play a critical role in global food supply, they also release GHGs into the atmosphere which contributes to climate change (Hartmann et al., 2013). Furthermore, global climate change involves changes in precipitation and an increased frequency of extreme weather events that are likely to occur during this century (Seneviratne et al., 2012), thus presenting a significant challenge to agricultural production. Three main biogenic GHGs, nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2), are produced by the agricultural industry and are strongly affected by environmental factors, which makes mitigation of GHG emissions a more challenging task in intensive agricultural systems in the context of global climate change (Smith et al., 2007).  N2O, a potent GHG, has a 100-year global warming potential (GWP) value which is 298 times that of CO2, and makes a 6.24% contribution to overall global radiative forcing (Myhre et al., 2013). Besides this, serious concern over stratospheric ozone destruction arising from atmospheric photochemical reactions with N2O has drawn substantial attention (Chapius-Lardy et al., 2006). Anthropogenic N2O emissions increased at a rate of 0.6 ± 0.2 Tg N yr-1 per decade, of which up to 87% results from direct emission from agriculture (71%) and indirect emission from anthropogenic nitrogen additions to soils (16%) (Tian et al., 2020). Over the recent decade (2007-2016), 3.8 (2.5–5.8) Tg N yr-1 have been released to the atmosphere as direct emissions from agriculture (Tian et al., 2020). N2O emissions are associated primarily with the processes of 2  denitrification and nitrification (Mosier, 1998). In addition to direct emissions from soils and manure management systems, applied nitrogen fertilizer can also be lost to the atmosphere via multiple pathways such as volatilization, leaching and runoff, denitrification (producing NO, N2O, and N2 gases), and immobilization (Coskun et al., 2017; Di & Cameron, 2002). As a result of highly inefficient nitrogen (N) use in modern agricultural systems, typically 50–70% of applied N is lost to the environment (Cassman et al., 2002; Ladha et al., 2005), creating a huge opportunity to mitigate N2O emissions by improving N use efficiency.  CH4, with a GWP value 34 times that of CO2, contributes around 15% to total radiative forcing (Myhre et al., 2013). It has been reported that global emissions from agriculture and waste (landfills and wastewater) for the period 2008–2017 are estimated to be 206 Tg CH4 yr-1 (range 191–223), representing 56% of total anthropogenic emissions (Saunois et al., 2020). CH4 flux from the soil to atmosphere is mainly driven by CH4 oxidation and methanogenesis (Bender & Conrad, 1995), and the ability of soils to oxidize CH4 is strongly affected by land use (Hütsch, 1998). Soils ranging from tropical forest to tundra can consume atmospheric methane, while wetlands and paddy soils can produce methane (Topp & Pattey, 1997). In the agricultural sector, reductions in CH4 emissions are mainly focused on improving rumen fermentation efficiency, increasing the productivity of dairy animals and water management practices for crops such as rice (Boadi et al., 2004). CO2, which contributes about 70% of total radiative forcing (Myhre et al., 2013), is strongly affected by land use management in terrestrial ecosystems. After the major agricultural expansion ended in the early 20th century, most agricultural soils in temperate regions, since then, have been cultivated for ~50 to 100 years or more (Paustian et al., 2000). These soils are thought to be near equilibrium (Cole et al., 1993). However, net losses of soil organic carbon 3  (SOC) resulting from new land use conversions to agriculture remain high in much of the tropics (Paustian et al., 2000). Soil carbon (C) levels are controlled by the balance between C inputs from plant residues whose C is fixed via photosynthesis and C losses primarily through decomposition (Paustian et al., 2000). Therefore, agricultural soils represent an opportunity to sequester C under management practices directed towards increasing plant residue inputs and reducing decomposition rates (Paustian et al., 2000). To summarize, agricultural management practices can play an important role in mitigating radiative forcing by increasing soil organic carbon (SOC), and decreasing CH4 and N2O emissions (Qian et al., 2003).  Canada is a large country, whose agricultural sector accounted for 8.1% of total national GHG emissions in 2018, released 59 Mt CO2 eq to the atmosphere. In 2018, agriculture accounted for 31% of national CH4 emissions and 76% of national N2O emissions (Environment and Climate Change Canada, 2020). The main drivers of these emissions are livestock populations and the application of inorganic nitrogen fertilizers to agricultural soils in the Prairie Provinces (Environment and Climate Change Canada, 2020). In May 2015, Canada announced its intentions to reduce GHG emissions by 30% below 2005 levels by 2030, and confirmed its commitments in its Nationally Determined Contribution (NDC) to the Paris Agreement. Therefore, considerable research has been directed toward mitigating GHG emissions that are generated by agricultural systems. However, these studies have been largely limited to Ontario (Wagner-Riddle et al., 2007) and the Canadian Prairies including the provinces of Manitoba, Saskatchewan, and Alberta (Asgedom & Kebreab, 2011; Liebig et al., 2005; Rochette et al., 2018), and GHG emissions in the main cropping systems in many other parts of Canada are scarcely being studied. Such efforts are greatly needed for the purpose of completing GHG 4  inventories and developing strategies to mitigate GHG emissions while developing climate-change-related policies. 1.2 Potato and pea production in Delta, BC Agriculture is a crucial contributor to the economy of Delta, with total gross farm receipts of almost $170 million in 2010 (Statistics Canada 2011). The fertile soils in the lower Fraser Valley allows for intensive arable cropping such as field vegetables and berries. Potatoes (Solanum tuberosum), as one of the main crops in Delta, account for about 50% of BC’s total area in potato production (British Columbia Agriculture & Food Climate Action Initiative 2013). In BC, 73% and 27% of produced potatoes were utilized as fresh and seed potatoes, respectively (Agricultural and Agri-Food Canada, 2018a). In 2017, the farm average price of potatoes in BC was C$597.89 per tonne, ranking first provincially in Canada, and was nearly twice that of national average value (C$294.54 per tonne) (Agricultural and Agri-Food Canada, 2018b). Therefore, it is generally recognized that potatoes are an important and high-value crop in BC. Peas (Pisum sativum) in BC are grown on 507 hectares accounting for 7.8% of total vegetables grown for harvest (hectares) (Statistics Canada, 2016) and are often planted following a potato crop as a typical crop rotation. 1.3 Agricultural Greenhouse Gas Program (AGGP) In late 2002, Canada ratified the Kyoto Protocol, which is a milestone in affirming commitments to the United Nations Framework Convention on Climate Change. These mainly deal with GHG emissions reduction and carbon sequestration. It has since been universally recognized that related research on filling knowledge gaps, and networks to share the knowledge, need to be promoted and supported. In order to achieve the common goal, substantial resources from the federal and provincial governments have been directed to supporting research on GHG 5  mitigation. Global Research Alliance (GRA) on Agricultural Greenhouse Gases initiated the Agricultural Greenhouse Gases Program (AGGP) for funding research grants in Canada in 2010 - 2011, in partnership with Agriculture and Agri-Food Canada (AAFC) to help promote understanding and knowledge transfer of GHG mitigation technologies.  This research on “Quantification and mitigation of GHG emissions from high-value agricultural production systems in BC”, led by a team of scientists in the Faculty of Land and Food Systems at the University of British Columbia (UBC) is part of the second phase of AGGP’s five-year $27-million initiative. The main aim, in collaboration with farmers in Delta, is to quantify and mitigate GHG emissions in agricultural crops, leading to beneficial management practices (BMPs) that will benefit producers in Canada.  1.4 Measurement of GHG emissions Importance of quantifying GHG emissions is well recognized because it is a pre-requisite to estimating annual GHG budgets of different ecosystems. Furthermore, different techniques allow measurements appropriate for specific temporal and spatial scales. In this thesis, the eddy covariance (EC) technique and non-steady-state closed-chamber method were employed to measure GHG emissions at the farm-field (102-104 m2) and point (~0.1-1 m2) scales, respectively. 1.4.1 Eddy covariance (EC) technique The EC method is an effective micrometeorological technique widely used for the quantification of fluxes at the landscape level, providing reliable GHG budgets that integrate across variable soil and vegetative cover. By measuring high-frequency fluctuations of vertical wind velocity and GHG concentrations above the field, half-hourly GHG fluxes can be calculated ( Baldocchi et al., 1988). Due to the desired features of providing nonintrusive, 6  continuous, real-time flux measurements, more than 650 EC towers globally have been set-up to measure net ecosystem (CO2) exchange (NEE) at the ecosystem scale in a wide range of ecosystems (Baldocchi, 2014). This increasingly contributes to improvements in the understanding of the exchange of energy, water vapour and GHGs between ecosystems and the atmosphere (Beer et al., 2010). In recent years, with trace gas sensors offering high accuracy and sufficient temporal resolution becoming available (Wienhold et al., 1994), quantification of N2O and CH4 emissions by EC measurements has become a topic of considerable interest. One notable limitation of EC flux measurements results from calm conditions resulting in low atmospheric mixing at night. It has been reported that advection may be predominant during calm and stable nights (Gu et al., 2005). This leads to GHGs produced near the ground possibly being transported laterally and not being measured by the EC tower (Burba et al., 2017; Aubinet, 2008). In addition, there would be large uncertainties occurring in the estimation of GHG budgets if a heterogeneous surface is present that is not representative of the mean landscape surface distribution in the studied ecosystem (Schmid & Lloyd, 1999). 1.4.2 Dynamic closed chamber method Another widely used flux measurement approach is the non-steady-state closed-chamber method. Advantageously, the closed-chamber method is relatively low in power consumption, flexible to operate and can be used in remote, logistically difficult-to-access areas. It’s also increasingly used to determine localized soil GHG emissions from each contributing landscape component, as a crucial supplementary technique, to verify EC integrated fluxes over a heterogeneous area (Famulari et al., 2010; Schrier-Uijl & Kroon, 2010). On the other hand, however, chamber design and experiment planning need to be carefully considered to minimize potential errors that can occur when using this method (Livingston and Hutchinson, 1995; 7  Davidson et al., 2002; Welles & Mcdermitt, 2001). Such errors involve inaccurate determination of the headspace volume (Livingston and Hutchinson, 1995), gas leakage issues during the measurements (Hutchinson and Livingston, 2001; Livingston et al., 2006), temperature changes of the soil and the atmosphere beneath the chamber (Wagner et al., 1997). 1.4.3 Flux footprint model While the requirements of homogeneous surfaces and stationary conditions need to be met to conduct turbulent flux measurements, it must be recognized that some degree of spatial variability and inhomogeneity are inevitable over naturally vegetated surfaces in most cases (Nagy et al., 2006; Levy et al., 2020; Molodovskaya et al., 2011). In combination with a flux footprint (upwind source area contributing to the EC flux measurement) model, the EC fluxes that integrate over the source area can be interpreted using the surface flux of each landscape component of the source area and its contribution (the flux footprint) to EC flux (Schmid, 1994). Over heterogeneous landscapes, the size of the flux footprint depends on the location and height of the sensor, and changes with atmospheric stability and wind direction (Schmid, 1994, 1997). There are different types of footprint models including two-dimensional analytical, Lagrangian particle trajectory, large-eddy simulation (LES), and closure models (Vesala et al., 2010). Among these, the most widely used are the analytical models, especially those of Kormann and Meixner (2001), Kljun et al. (2004), and Hsieh et al. (2000), due to their relatively low computational complexity and their applicability in a wide variety of conditions (Foken & Leclerc, 2004).  8  1.5 Research objectives of this thesis So far, little is known about annual GHG emissions from local high-value cropping systems in British Columbia. This thesis is about EC measurements of N2O, CH4 and CO2 fluxes over a field managed as a potato-pea crop rotation, However, the EC footprint comprised a farm road, machinery turn-around strip and ditch (referred to as the edge area in this thesis) with cropped areas on both sides, and the effect of the non-cropped area must be taken into account to make robust estimates of annual GHG budgets. Accordingly, the objectives of this thesis are: 1. To make continuous measurements of N2O, CH4 and CO2 fluxes from potato and pea fields in the lower Fraser Valley using the EC technique. 2. To calculate the flux footprints to account for the effects on EC-measured N2O and CO2 fluxes of field edge, field size and wind direction. 3. To determine the source flux of potato and pea fields by combining EC measurements, flux footprint analysis and chamber measurements. 4. To estimate the annual carbon budgets and GHG budgets of potato and pea crops. 9  Chapter 2: Methods 2.1 Study site The study site is located on Reynelda Farm (49°05'25.0"N, 123°09'47.9"W), which is a conventionally managed farm located on Westham Island which is on the southwest edge of the Fraser River delta (Appendix A). This farm is a typical of cropland in the area and was planted with potatoes, peas and silage corn during the study period in 2018 and 2019. The soils in this region are Gleysols and the soil texture is silt loam. The soils are fertile and are commonly used for intensive arable cropping such as field vegetables and berries. However, the soils of the area tend to be naturally poorly drained and are prone to ponding in the winter. In addition, they are prone to degradation due to issues of compaction, rain erosion and low soil organic matter content particularly in annual cropping systems that use intensive tillage practices (Hermawan & Bomke, 1996). The thirty-year (1981–2010) climate record of a nearby weather station, Vancouver International Airport (49°11'42.0"N, 123°10'55.0"W), indicated an average annual air temperature of 10.4 C, rainfall of 1152.8 mm, and snowfall of 38.1 cm (Environment and Climate Change Canada). A large proportion (~80%) of precipitation occurs between October and April, contributing to the temporal variability in the soil moisture conditions that are associated with GHG emissions (Pihlatie et al., 2004; Saarnio et al., 2013; Schaufler et al., 2010).  The experimental site mainly comprised three landscape components according to microtopography: water-filled ditch with dense grass, relatively dry soil strips serving as farm roads and relatively dry crop fields in summer months (Fig. 1) (see Appendix B.3-5 for photos of potato field, pea field and field in the non-growing season). For simplicity, the water-filled ditch with dense grass and soil strips were defined as “edge area” (see Appendix B.6 for photos of the edge area) as a whole in this thesis, considering that the edge area was within the EC footprint but 10  was not desired in estimating the GHG budgets of the actual crop fields. Fig. 1 shows the three landscape components with the edge area located between the west and east crop fields.  In 2018, potatoes (Goldrush Russet and Satina) were grown in both west and east fields as fresh produce. The potato fields were fertilized two days before and during potato planting with a total application of 110 kg N ha-1. The yield of potato tubers was 34.6 t ha-1 in wet matter. In 2019, peas (Serge) were grown in the east field and corn was grown in the west field. The pea field was fertilized with 34 kg N ha-1 of synthetic NH4NO3 fertilizer broadcast and had a yield of 10.4 t ha-1 wet peas. After crop harvest, the biomass was left on the ground for both potatoes and peas. All the farm operations such as fertilizer applications and harvesting were not controlled by the research group (see Appendix C). Due to insufficient winds from the west field during the corn growing season and insufficient measurement height of EC tower (3.0 m) compared with the maximum height of corn (3.1 m), it was difficult to obtain a complete time series of GHG fluxes from the corn field and to make reliable estimates of GHG budgets. Therefore, the corn crop was not included in this thesis.    11   Figure 1: Three landscape components of the experimental site, a) crop areas (west and east), b) farm road and machinery turn around area (west and east) and c) water-filled ditch with dense grass (in the middle). b and c landscape components were defined as “edge areas” in this thesis.  2.2 Eddy covariance (EC) measurement 2.2.1 Location of the EC system The EC tower was located on a wooden platform above the water-filled ditch in the middle of the edge area to avoid interference with regular farm operations (Fig. 2). This also took into account the benefit of placing the EC tower between the two crop fields to receive upwind flux signals from both fields as wind shifted from one field to the other during the crop growing season. During the potato growing season and non-growing season, the west and east fields were managed the same. The width of the edge area adjacent to the west and the east fields was 10 m and 14 m, respectively. However, the width of the edge area adjacent to the east field changed from 14 m to only 6 m during the pea growing season since the farmer planted the peas without leaving a machinery turn around strip beside the water-filled ditch. 12   Figure 2: The location of the EC tower (this figure is enlarged from Fig. 1).  2.2.2 EC instrumentation and measurements Fluxes of N2O, CO2 and CH4 were measured continuously at the field scale by the EC sensors installed on a mast at a fixed height of 3 m (zm) from June 2018 to October 2019. The EC system consisted of a three-dimensional sonic anemometer (R3-50, Gill Instruments Ltd.), an enclosed-path infrared gas analyzer (IRGA) (LI-7200, LI-COR Inc.) and a continuous-wave laser spectrometer (LGR) (model 913-1054, Los Gatos Research Inc.). The R3-50 sonic anemometer measured the magnitudes of the three wind velocity components (u, v, w) and sonic temperature (Tsonic) at 20 Hz. The IRGA measured CO2 (𝜌𝒄) and water vapour (H2O) densities in air (𝜌𝑣) at 20 Hz. The LGR could only measure gas densities of N2O (𝜌N2O), CH4 (𝜌CH4) and H2O (𝜌𝑣) up to 10 Hz. Both instruments reported concentrations on a dry mole basis (i.e., mixing ratios) at the measurement frequency thus not requiring subsequent Webb-Pearman-Leuning (WPL) corrections (Webb et al., 1980). Sample air was drawn through two separate air sampling inlets positioned 15 cm below the centre of the R3-50 sonic anemometer array to avoid disturbance to the sonic measurements. The IRGA was mounted 2.5 m above the ground on the measurement mast and connected to a 1.3-m 13  long x 4 mm inner-diameter air-sampling tube (flow rate of 15 L min-1). A second air-sampling tube 3.8-m long tube x 4 mm inner diameter (flow rate of 20 L min-1) running parallel to the first one was connected to the LGR spectrometer which was in an insulated trailer where constant temperature (15~20 ℃ above the ambient air temperature (Ta)) was maintained by a fan located at each of 4 vent ducts (see Appendix B.1). The two sampling tubes were insulated and heated to 5~10 ℃ above the ambient air temperature (Ta) to prevent nighttime condensation inside the tubes. Before entering the IRGA and spectrometer, particulates in the sample air were removed with a reusable 2.0-μm stainless steel filter on the inlet, and weekly manual replacement of filters (after sonic bath cleaning) were performed to ensure adequate air flow in the tubing. Occasional calibrations of gas mixing ratios measured by the IRGA and spectrometer using reference gas of known CO2, N2O and CH4 concentrations supplied by Environment and Climate Change Canada were conducted when necessary, based on the performance of the instruments, to ensure quality measurements. All the real-time high-frequency data were stored on a USB stick and transferred every week in case recalculations of half-hourly flux data were required (i.e. calculation procedure changes or interruptions in site computer). The half-hourly EC fluxes were automatically calculated on the site computer and logged with CR3000 datalogger (Campbell Scientific Inc., Logan, UT) through its analog and digital (RS232) outputs. The calculated data was then transferred to the Biometeorology and Soil Physics Lab at the University of British Columbia (UBC) via cellular modem on a daily basis. 14  2.2.3 EC flux calculations Half-hourly fluxes were calculated from the high-frequency data. The EC method obtains the vertical turbulent flux density (𝐹EC) of N2O (𝐹N2O), CO2 (𝐹c), CH4 (𝐹CH4) and water vapour (E) at the measurement height (zm) using the following equation (Burba, 2013):                                                                                                                                           𝐹s = 𝜌d̅̅ ̅ ∙ 𝑤′𝑠′                                                                (2.1) where 𝜌𝑑 is the dry air density, w is the vertical wind speed and s is the mixing ratio of the GHG of interest. The primes denote the fluctuations from the mean values, while the overbars denote the average value during a specific time period (i.e. 30 min). Fluxes were calculated as block averages, i.e., no detrending. MATLAB® (The MathWorks, Inc.) was used for all EC related flux calculations and data processing. Air column (beneath the sonic array) storage was neglected since it was found to be less than 3% of 𝐹EC. The water vapour flux was measured by both the IRGA and LGR spectrometer to assess agreement between the instruments (kept to within +/- 10%) (see Appendix I). The sensible heat flux (H) was computed using the sonic temperature as follows (Burba, 2013):                                                                𝐻 = 𝜌𝑐𝑝𝑤′𝑇a′                                                               (2.2) where 𝜌 is the air density, 𝑐𝑝 is the specific heat of air and Ta is the air temperature which is approximated by sonic temperature (Tsonic). The latent heat flux (𝜆𝐸), where λ is the latent heat of vaporization, and H were then used to assess energy balance closure (EBC) for each half hour which can be written as (Foken et al., 2008):                                                            EBC = (𝜆𝐸 + 𝐻)/(𝑅𝑛 − 𝐺)                                            (2.3) 15  where Rn is the net radiation and G is the soil heat flux corrected for the change in soil heat storage between the heat flux plates installed at the 3-cm depth and the surface.   2.2.4 Flux quality control and data analysis Quality control was performed on the half-hourly data after the covariance calculations. Spikes in flux data were removed (Humphreys et al., 2003) and any values outside the sensible boundaries (N2O: -1~8 nmol m-2 s-1; CO2: -35~5 μmol m-2 s-1; CH4: -0.1~0.1 μmol m-2 s-1) were discarded. We also rejected bad data when unexpected sensor or electricity supply failures occurred. In addition, the data with the friction velocity less than 0.1 m s−1 (see Appendix D) was discarded to exclude measurements occurring when turbulent mixing was not sufficient (Laville et al., 1999). Observations showed that the friction velocity (𝑢∗) threshold was not affected by which crop was present. To separate fluxes from the two crop fields according to wind directions, 210° to 330° and 50° to 130° wind direction ranges were used to determine the winds from the west and the east crop fields. These wind direction windows were based on the criteria that more than 60% of total flux footprint contribution from the crop and the edge areas together should be achieved. This was intended not only to avoid large effects of the surrounding non-studied area for which I assumed GHG fluxes to be zero, but also avoid too much data loss. The data were selected for these two ranges for further data analysis. During the 2019 growing season, the peas were planted in the east field, so data were only selected for winds from 50° to 130°. 2.2.5 Flux gap-filling After data screening, small gaps shorter than two half-hours were filled by linear interpolation and longer gaps were filled using mean diurnal variation (MDV) over 5 to 14-day cycles.(Falge et al., 2001; Nemitz et al., 2018).  16  2.2.6 NEE partitioning and environmental controls on C fluxes Net ecosystem exchange (NEE) can be partitioned into two components as:                                                           NEE = −NEP = 𝑅e − GPP                                              (2.4) where NEE is equal to 𝐹c because, as mentioned earlier, changes in CO2 storage in the air column beneath the EC measurement height were negligible, NEP is net ecosystem production, GPP is gross primary production, and 𝑅e is ecosystem respiration. A negative NEE value indicates CO2 uptake by the ecosystem from the atmosphere and a positive NEE value indicates CO2 release from the ecosystem to the atmosphere. The NEE partitioning procedure follows the standard FLUXNET Canada Research network (FCRN) protocol with a moving window approach (Barr et al., 2004).  To investigate the effect of environmental factors on 𝑅e, the logarithmic transformation,  ln𝑅e = 𝐴 + 𝐵𝑇s, which allows the assumption of normality and homoscedasticity to be met for linear least squares regression was used to obtain the parameters in the exponential relationship between Re and Ts as follows (Humphreys et al., 2005):  𝑅𝑒 = 𝑅10𝑄10(𝑇s−10)/10                                               (2.5) where 𝑅10 is the reference respiration rate at 10℃, 𝑄10 is the exponential increase in 𝑅e with a 10℃ increase in temperature and 𝑇s is the soil temperature at the 5-cm depth.      Another empirical relationship based on Eq (2.5) involves soil moisture (𝜃s) and is expressed as (Gaumont-Guay et al., 2006): 𝑅e = (𝑎 + 𝑏𝜃 +𝑐𝜃)𝑅10𝑄10(𝑇s−10)/10                                    (2.6) where 𝜃 is the volumetric water content at the 5-cm depth, and 𝑎, 𝑏 and 𝑐, are coefficients obtained from a hyperbolic relationship with 𝑅e.  The relationship between PAR and GPP was modelled by a rectangular hyperbolic curve 17  derived from Michaelis–Menten kinetics and is commonly used in ecosystem analyses (Burrows et al., 2005): GPP =𝛼×PAR×GPPmax𝛼×PAR+GPPmax                                                       (2.7) where α is the initial light response efficiency and GPPmax is the asymptotic maximum rate of GPP at increasing PAR (i.e., the ecosystem assimilation capacity). 2.2.7 Net ecosystem carbon budget (NECB) of crops In this thesis, due to the fact that EC measurements during the non-growing season following pea harvest was not complete, I used a common non-growing season to calculate the annual carbon budget, net ecosystem carbon budget and GHG budget for potato and pea crops. The timeline of EC measurements and defined potato and pea years are illustrated in Fig. 3. The net ecosystem carbon budget (NECB) in this thesis is defined as  (Waldo et al., 2016): NECB = −NEE + Cimport − Cexport                                      (2.8) where Cimport and Cexport are import via seeding and/or organic fertilizer and export via harvested biomass, respectively. Due to the fact that organic fertilizer was not applied to the study site and seeding rate was not available, the term Cimport in this equation was not considered for this thesis. Cexport was determined by the C content of the potato tubers/peas and pods, and yields of potatoes and peas. 18   Figure 3: The timeline of potato growing season (GS), non-growing season (NGS) and pea growing season (GS). The time periods of EC measurements, defined potato year and pea year were indicated by red, brown and green arrows, respectively.  2.2.8 Calculation of global warming potential (GWP) The GWP of a GHG is its ability to trap heat compared to CO2 which has a value of 1. The GWPs of N2O and CH4 (i.e., GWPN2O and GWPCH4 ) for a 100-year time scale are 298 and 34 (Myhre et al., 2013), respectively. The CO2 equivalent flux of each GHG is obtained by multiplying its flux by its mass-based GWP. The annual contribution of the crop to atmospheric warming (or cooling) can be obtained by summing the CO2 equivalent fluxes of the GHGs over a year as follows (Lee et al., 2017):                        𝐹CO2e =  𝑚CO2𝐹CO2 + GWPCH4𝑚CH4𝐹CH4 + GWPN2O𝑚N2O𝐹N2O                      (2.9) where 𝐹CO2𝑒 is the sum of the CO2 equivalent mass fluxes of CO2, CH4 and N2O. 𝐹 is the study-period averaged molar flux and 𝑚 is the molar mass of each GHG. 2.3 Manual FTIR chamber measurements  2.3.1 Manual FTIR chamber measurements and calculations Surface fluxes (Q) of CO2, N2O and CH4 from the edge area and the crop fields were measured every two weeks using closed dynamic non-steady state chambers from May 2018 to August 2019. 19  Three opaque cylindrical PVC collars (i.d. 20 cm, height 15 cm) were installed on both sides of the EC tower. One of these three collars were located in the edge area and the other two were located in the crop field, with one being in the plant-row and the other being in the interrow to estimate the GHG emissions from the whole field. Existing vegetation that interfered with tight sealing of the chamber lid was cut to a proper level below the lid. Collars were removed for farm operations; otherwise, they were left at a fixed place throughout the entire study period. A DX4040 portable Fourier Transform Infrared (FTIR) Spectrometer gas analyzer (Gasmet) (Gasmet Technologies Group, Helsinki, Finland) was paired with the chambers to measure soil surface fluxes of N2O, CO2 and CH4 following the procedure described by Schiller and Hastie (1994). Teflon ® tubing (id. 4 mm; flow rate of 2~3 L min-1) connected the metal lid to a silica gel container to remove excess moisture from the air stream. Air then passed through a 7-μm stainless steel filter before being drawn into the gas analyzer and then returned to the chamber. To improve the consistency of gas flux measurements, gas samples were collected during the period between 9:00 AM and 5:00 PM (PST). Before each sampling campaign, the Gasmet readings of all of gases were verified for zero level by flushing N2 gas through the system to ensure accurate gas flux readings and tested for leakage in the laboratory. The calibration procedure was conducted weekly to ensure good measurements in the field. While making measurements in the field, the FTIR was turned on for 5 minutes before making all measurements on each sampling date to warm up the gas analyzer and to stabilized background atmospheric gas concentrations. Then the lid was placed on the PVC collar for 6 minutes with 9 measurements made per minute. Ancillary measurements including collar air temperature, soil temperature and soil volumetric water content at the 5-cm depth were made using soil thermometers and Fieldscout TDR 150 (Spectrum Technologies, Inc. Plainfield, IL) soil moisture meter, respectively, at the time of GHG 20  sampling. The height of the collar above the soil surface at four different locations were measured and averaged to determine the volume of the chamber head space. The fluxes were determined by developing a linear relationship between measured GHG mixing ratios and time since lid placement. Fluxes were calculated as follows: 𝑄 = 𝜌𝑑 𝑉𝐴𝑑𝑠𝑑𝑡                                                                (2.10) where Q is the surface GHG flux, 𝜌𝑑  is the molar density of dry air, 𝑉 is the volume of the chamber headspace, 𝐴 is the soil surface area in the headspace and 𝑑𝑠𝑑𝑡 is the slope of gas mixing ratio change over 6 min. 2.3.2 Gap-filling of chamber fluxes In the case of N2O flux, to synchronize with the half-hourly EC measurements, a complete time series of chamber-measured N2O fluxes from the edge on a half-hourly basis was obtained using a simple spline interpolation. It was interesting to note that the magnitude of the N2O fluxes on the edge was very small compared to those from the crop field. In the case of CO2, the complete time series of CO2 fluxes (𝑄e_er) was obtained using an empirical relationship between manual chamber measurements of 𝑄e_er and Ts expressed as a logarithmic transformation of Eq (2.5). In the case of CH4 flux, no gap-filling was applied as the flux was close to minimum detectable levels and it was not used in any flux calculations. 2.4 Ancillary soil and climate measurements All the soil and climate measurements were made in the edge area, not only to avoid disturbance from farm operations, but also to have the best representativeness of the actual crop field (see Appendix B.2). A four-component net radiometer (CNR1, Kipp & Zonen, Delft, The Netherlands) mounted 21  0.5 m above the grass measured incoming shortwave (Sd), outgoing shortwave (Su), incoming longwave (Ld) and outgoing longwave (Su) radiation. One quantum sensor (LI-190, LI-COR Inc., Lincoln, NE, USA) mounted at the 1-m height measured photosynthetically active radiation (PAR). Precipitation was measured using a tipping bucket rain gauge (TR-525M, Texas Electronics, Dallas, TX, USA) at the 1-m height. Air temperature (Ta) was measured at the 2.5-m height using a type-T (copper-constantan) thermocouple. Three copper-constantan thermocouples and three water content reflectometers (CS616, CSI) were installed to measure soil temperature and soil water content, respectively, at depths of 5, 20 and 60 cm. Three soil heat flux plates (CN3, Middleton Solar, Melbourne, VIC, Australia) were installed at the 3-cm depth to measure the ground heat flux (G). All the data were logged on a datalogger (CR3000, CSI) and a signal multiplexer (AM16/32, CSI), and was output on a half-hourly average basis. To obtain leaf area index (LAI) of potatoes, destructive sampling was conducted, and leaf lengths and widths were measured to compute leaf areas after calibrating several leaves using a portable leaf area meter (LI-3000, LI-COR Inc., Lincoln, NE, USA). Canopy height (hc) was also manually measured for potatoes in the 2018 growing season (see Appendix H). During the potato growing season in 2018, to investigate the relationship of Rn and G between crop and edge areas, measurements of Rn and G were made in the potato field on selected days. A four-component net radiometer (Model SN-500-SS, Apogee Instruments, Inc.) was mounted 1 m above the potatoes and the view of the down-facing pyranometer and pyrgeometer included 3 potato rows and exposed soil surface. Four soil heat flux plates (CN3, Middleton Solar, Melbourne, VIC, Australia) were installed adjacent to the potato rows (see Appendix E). One of the limitations of this experiment was that it could only partly represent the relationship of Rn and G between crop and edge areas as G could not be measured within the field due to interference by frequent farm 22  operations. 2.5 Flux footprint analysis 2.5.1 Flux footprint model The EC measurement integrated over this study site is the result of surface fluxes from the two components of the field: the crop and edge areas. The edge area is small compared to the crop area (Fig. 1) but its proximity to the EC mast can result in it significantly affecting the EC flux. A Flux Footprint Prediction model (FFP) proposed by Kljun et al. (2015) was applied to correct for this edge effect and to obtain the surface fluxes from the crop area of interest. For each half hour, I calculated the flux footprint over the whole domain around the tower at 1-m resolution. The EC flux over the integrated area can be expressed as follows (Kljun et al., 2015):   𝐹EC =  ∫ 𝑄(𝑥, 𝑦)𝑓(𝑥, 𝑦)𝑑𝑥𝑑𝑦                                            (2.11) where 𝐹EC is the flux measured by the EC system, 𝑄(𝑥, 𝑦) is the surface (i.e., source) flux at location (𝑥, 𝑦), 𝑓(𝑥, 𝑦) is the footprint contribution to the EC flux at location (𝑥, 𝑦) and 𝑑𝑥𝑑𝑦 is the unit size of the source area, which is 1 m2 in this case (see Appendix J for a set of figures showing the flux footprint climatology for the west potato, east potato and east pea fields). When the surface fluxes from each landscape component (the crop and edge areas) are available, the fluxes measured by EC can be expressed as follows: 𝐹EC = ∫ 𝑄cc (𝑥, 𝑦)𝑓c(𝑥, 𝑦)𝑑𝑥𝑑𝑦 + ∫ 𝑄ee (𝑥, 𝑦)𝑓e(𝑥, 𝑦)𝑑𝑥𝑑𝑦                 (2.12) where the subscript c denotes the crop area and e denotes the edge area adjacent to the crop area. To simplify the computation of the footprint contributions, 𝑄c and 𝑄e were assumed to remain constant within the crop and edge areas since both landscape components are relatively homogeneous. Therefore, the EC flux 𝐹EC can be expressed as: 𝐹EC = 𝑄c ∑ 𝑓c (𝑥, 𝑦) + 𝑄e ∑ 𝑓e (𝑥, 𝑦)                                       (2.13) 23  where ∑ 𝑓c (𝑥, 𝑦) and ∑ 𝑓e (𝑥, 𝑦) are the summations of the footprint contributions over the crop and edge areas according to the actual sizes of these two landscape components. 2.5.2 Flux footprint corrections of EC fluxes (1) CO2 The net ecosystem exchange measured by the EC system (NEE) can be expressed as follows: NEE = 𝑄c_nee𝑓c + 𝑄e_nee𝑓e                                            (2.14) where 𝑄c_nee and 𝑄e_nee are the values of net ecosystem exchange of the crop and edge areas, respectively. The flux footprint contributions of the crop and edge areas 𝑓c and 𝑓e, respectively, can be calculated for each half hour using an online Matlab program created by N. Kljun (Kljun et al., 2015).  An equation similar to Eq (2.14) can be written for ecosystem respiration (𝑅e) as measured by the EC system as follows:  𝑅e = 𝑄c_er𝑓c + 𝑄e_er𝑓e                                                (2.15) where 𝑄c_er  and 𝑄e_er  are the values of ecosystem respiration of the crop and edge areas, respectively. The values of 𝑓c and 𝑓e remain the same as those in Eq (2.14) for each half hour. Subtracting Eq (2.14) from Eq (2.15) results in the corresponding equation for the gross primary production as obtained from the EC measurements described as follows:  GPP = 𝑄c_gpp𝑓c + 𝑄e_gpp𝑓e                                          (2.16) where 𝑄c_gpp = 𝑄c_er - 𝑄c_nee and 𝑄e_gpp = 𝑄e_er - 𝑄e_nee which are the gross primary production values for the crop and edge areas, respectively. Two methods can be used to obtain 𝑄c_gpp. Method (1) defines the ratio of 𝑄e_gpp to 𝑄c_gpp as the GPP ratio, k.  Based on typical GPP values of the grass reported in the literature (e.g., Sheehy 24  and Peacock, 1975), I considered 4 values of k (0.8, 1.0, 1.5 and 1.8) in my analysis.  Also, a short experiment aimed at a rough estimation of the sink strength of grass by using tarpaulins to cover grass along the ditch edge just north-west of the flux tower was conducted in May 2019. The grass GPP was estimated to be 25~40 μmol m-2 s-1 (see Appendix G). Therefore, we have: 𝑘 = 𝑄e_gpp/𝑄c_gpp                                                            (2.17) Using Eq (2.17), Eq (2.16) can be rewritten as follows: GPP = 𝑄c_gpp𝑓c + 𝑘𝑄c_gpp𝑓e                                                  (2.18) Or GPP = 𝑄c_gpp(𝑓c + 𝑘𝑓e)                                                        (2.19) From Eq (2.19) we obtain an expression for 𝑄c_gpp as follows: 𝑄c_gpp = GPP/(𝑓c + 𝑘𝑓e)                                                   (2.20) where GPP = 𝑅e − NEE                                                         (2.21) During the nighttime when GPP = 0, 𝑅e is equal to NEE. Nighttime 𝑄e_er was obtained from a logarithmic relationship ( ln𝑄e_er = 𝐴 + 𝐵𝑇s) between daytime 𝑄e_er measured using chambers and 𝑇s. Nighttime  𝑄c_er, therefore, can be calculated using Eq (2.15) for each half hour. Then 𝑄c_er during the daytime can be modelled by developing a relationship between 𝑄c_er and corresponding Ts during the nighttime. Therefore, daytime 𝑅e can be calculated using Eq (2.15), and with NEE being directly measured by the EC system, GPP can be obtained using Eq (2.21). Then 𝑄c_gpp can be obtained using eq (2.20). Method (2) uses modelled 𝑄c_gpp (i.e. using the Michaelis–Menten equation (Burrows et al., 2005)) as below. 25  Combining Eqs (2.16) and (2.21) gives: GPP = 𝑅e − NEE = 𝑄c_er𝑓c + 𝑄e_er𝑓e − NEE                               (2.22) Equating the right-hand sides of Eqs (2.16) and (2.22) gives:  𝑄c_gpp = (𝑄c_er𝑓c + 𝑄e_er𝑓e − NEE − 𝑄e_gpp𝑓e)/𝑓c                      (2.23) Or  𝑄c_gpp = (𝑄c_er𝑓c − NEE − (𝑄e_gpp − 𝑄e_er)𝑓e)/𝑓c                   (2.24) However, to avoid complexity, I have used method (1) because of its simpler parameterization and low observed sensitivity to the value of k. The whole data analysis procedure for the CO2 flux is shown as a flowchart in Fig. 4.  Figure 4: Flowchart of the data analysis procedure for obtaining ecosystem respiration (𝑅e), gross primary production (𝑄c_gpp) and net ecosystem exchange (𝑄c_nee). Equation numbers are included in the brackets.     26  (2) N2O The contributions to the N2O flux measured by the EC system (𝐹N2O) from the crop and edge areas can be expressed as follows: 𝐹N2O = 𝑄c_N2O𝑓c + 𝑄e_N2O𝑓e                                         (2.25) where 𝑄c_N2O and 𝑄e_N2O are the surface fluxes of N2O in the crop and edge areas. 𝑄e_N2O was obtained using closed-chamber measurements. 𝑄c_N2O can be obtained by rearranging equation (2.25) as follows: 𝑄c_N2O = (𝐹N2O − 𝑄e_N2O𝑓e)/ 𝑓c                                     (2.26) (3) CH4 CH4 fluxes from the crop and edge areas (see Appendix G) were generally negligible, and were considered to occur only when the wind was from the north or south, i.e., along the water-filled ditch. Therefore, no flux footprint analysis was applied. 27  Chapter 3: Results and Discussion 3.1 Climate and soil variables 3.1.1 Climate measurements Climate variables during the study period are shown in Fig. 5. Since the measurement period was not a complete calendar year and was longer than 12 months, the annual values were calculated from Aug 1, 2018 to Jul 31, 2019, and compared with a nearby climate station (Table 1). During the selected 12 months, the annual mean Ta was 10.8 ℃ with the monthly mean Ta ranging from 1.0 ℃ (February 2019) to 18.2 ℃ (August 2018). The site received an annual P of 976 mm, of which approximately 80% occurred between 1 September 2018 and 28 February 2019. The largest monthly P was observed in December 2018, with a total of 201 mm which was even higher than the highest 30-year normal monthly P of 189 mm (November). Compared with the nearby Vancouver International Airport (YVR) climate station, the site experienced an annual mean Ta lower by 0.4 ℃ and an annual P lower by 134 mm (Table 1). While the monthly values of Ta at the site were similar to those at YVR, with the largest difference being only 0.8 ℃ in December 2018, it always measured lower P with the only exception being in September 2019. Compared with the 30-year (1981-2010) normal at the YVR climate station, the site had a much lower Ta in February 2019 (1.0 ℃) due to daily average Ta dropping below 0℃ on a few days in that month. Except that, the other winter months were slightly warmer than the normal. The largest difference in monthly P occurred in December 2018, with monthly P at the site being lower than that at the YVR climate station by 54 mm. Due to the malfunction of the rain gauge at the site, P from January to March in 2019 was gap-filled using data from the YVR climate station. Therefore, the difference in annual P might be larger than the observed value. Annual P was lower than the normal with the growing season (May to August) being drier in 28  2019 while December in 2018 was significantly wetter. 𝐷day was typically <1 kPa but was >1 kPa during the growing season.  Figure 5: Climate variables at the site during the study period. Panel a) shows daily average air temperature (Ta), panel b) shows 1-day precipitation (𝑃Daily) and cumulative precipitation (𝑃Cum), panel c) shows daily average photosynthetically active radiation (PAR), panel d) shows daily average daytime vapour pressure deficit (𝐷day). Daytime was determined by 𝑆d > 0. Climate measurements started from mid-July in 2018. The data of from mid-May to mid-July was filled by using data from YVR.          29  Table 1: Comparison of mean monthly air temperature (𝑇a) and precipitation (𝑃) at the site with the 30-year (1981 – 2010) normal at the Vancouver International Airport (YVR) climate station (49.19°N, 123.18°W). Year Month Site Ta (℃) YVR Ta (℃) 30-year Ta(℃) Site P (mm) YVR P (mm) 30-year P (mm) 2018 May 14.8 14.8 12.8 1.6 1.6 65.0 June 15.9 15.9 15.7 38.8 38.8 53.8 July* 19.3 19.1 18.0 1.2 5.4 35.6 August 18.2 18.5 18.0 6.7 16.2 36.7 September 14.6 14.6 14.9 105.6 111 50.9 October 10.5 9.8 10.3 92.8 110 120.8 November 8.1 7.6 6.3 189.5 203.8 188.9 December 5.7 4.9 3.6 200.6 254.8 161.9      2019 January 5.5 5.0 4.1 140.8 140.8 168.4 February 1.0 0.4 4.9 43.4 43.4 104.6 March 6.7 6.0 6.9 30.0 31.2 113.9 April 10.2 9.5 9.4 110.8 110.8 88.5 May 14.7 14.2 12.8 12.2 30.4 65.0 June 16.8 16.4 15.7 13.8 26.2 53.8 July 18.1 18.5 18.0 29.6 30.8 35.6 August 18.4 18.7 18.0 17.4 25.8 36.7 September 15.6 15.6 14.9 113.6 106.8 50.9 October 9.7 8.7 10.3 73.3 122.6 120.8  Annual**   10.8 10.4 10.4 975.7 1109.4 1189.0 *The climate measurements started from Jul 15, 2018. The monthly 𝑇a and 𝑃 at the site before this date were obtained from the YVR climate station. **Annual values were calculated for the 12-month time period from Aug 1, 2018 to Jul 31, 2019.  3.1.2 Soil measurements Site 𝑇s at the 5-cm depth followed the same pattern as 𝑇a, while being slightly higher due to the coverage of the grass (Fig. 6.a). Daily mean 𝑇s was always > 0 ℃ while daily mean 𝑇a dropped below 0 ℃. 𝜃s increased in September following heavy precipitation and reached the maximum (approximately 0.50 m3 m-3) in January 2019. A steep drop in occurred in February 2019, coinciding with the lowest 𝑇a which was <0℃. Other than that, 𝜃s remained high 30  throughout the spring and then gradually declined to approximately 0.25 m3 m-3 in the growing season (Fig. 6.b).  Figure 6: Soil variables at the site during the study period. Panel a) shows daily average soil temperature (𝑇s) at the 5-cm depth (black solid line) and daily average air temperature (𝑇a) (blue solid line). Panel b) shows daily average volumetric water content (𝜃s) measured at the 5-cm depth.   3.2 Wind direction and speed Fig. 7 shows the wind roses during the entire measurement period and three time periods separately. During the measurement period, the dominant winds came from the NW and SE directions. This also allowed air from both the west and east to be sampled by the EC system.  However, proportions of the winds from the west (32%) and the east (40%) for daytime and nighttime together were different. For potato GS and NGS, more winds came from the west field than from the east field during the daytime, while this trend was opposite during the nighttime (Fig. 8). For the pea GS, however, more winds came from the east field than from the west field both during the daytime and nighttime. Also, this diurnal pattern of winds might lead to a bias in estimating the source/sink strength of GHG fluxes from the west and east fields separately. It -100102030Ts and Ta (C)00.250.500.751s (m3 m-3)2018                                                                                             2019 J        A          S          O           N          D          J           F         M          A          M           J           J          A          S           O31  means the differences between two fields might not result from the inherent soil characteristics of two fields but due to different weather conditions possibly associated with winds coming from different wind directions. This can result in potentially larger effect on NEE, especially during the growing season, due to the strong diurnal patterns and radiation-driven physiological process such as photosynthesis of the crop.  The average wind speed during the study period was 2.4 m s-1. And the average wind speeds during the potato growing season, non-growing season and pea growing season were 2.3, 2.7 and 1.9 m s-1, respectively.  Figure 7: Wind rose during a) the entire study period, b) the potato growing season (May 15, 2018 to Sep 18, 2018), c) the non-growing season (Sep 19, 2018 to Jun 20, 2019) and d) the pea growing season (Jun 21, 2019 to Aug 23, 2019).  32   Figure 8: Wind distributions for the west field (range from 210˚ to 330˚; blue colour) and east field (range from 50˚ to 130˚; orange colour) during the daytime (solid) and nighttime (shaded) for three time periods, potato growing season (GS), non-growing season (NGS) and pea growing season (GS).  3.3 Radiation balance components Monthly 𝑅n was positive for the entire measurement period changing temporally with seasonality (Fig. 9). Sd exhibited the same behavior as 𝑅n. Both monthly 𝑅n and 𝑆d peaked in June 2019 at 185.5 W m-2 and 278.1 W m-2, respectively. The highest Ld and 𝐿u occurred in July 2019, reaching 358.7 W m-2 and 406.0 W m-2, respectively. Monthly albedo was relatively high in the winter months compared to the growing season likely due to the cover of snow. The highest monthly albedo was observed in February 2019 (0.29) because of the surface being covered by snow on few days and larger proportion of Sd being reflected by the surface than that on normal winter days. 33   Figure 9: Monthly mean radiation components of a) upwelling longwave (𝐿u; black dashed line), downwelling longwave (𝐿d; blue dashed line), upwelling shortwave (𝑆u; black solid line), downwelling shortwave (𝑆d; blue solid line) and net radiation (𝑅n; red solid line); b) albedo at the site during the study period.  3.4 Energy balance components and energy balance closure 3.4.1 Energy balance components As shown in Fig. 10, monthly average 𝐻 was < 𝜆𝐸 during most of the year but was > 𝜆𝐸 in July and August 2018 and in May, June and September 2019. Monthly 𝐻 was generally positive except from 1 November 2018 to 28 February 2019 and peaked at 76.2 W m-2 in June 2019, while 𝜆𝐸 peaked at 50.7 W m-2 one month later. Monthly Bowen ratio (𝛽) was highest in June 2019 (1.7) and was negative during winter months (from November 2018 to February 2019) due to H being negative. Negative H values during cold winter months were also observed by Humphreys et al. (2003). The most negative monthly value of 𝛽 occurred in December (-2.5). Monthly 𝐺 was positive between March and August and negative for the rest of the year. 34   Figure 10: Monthly mean energy balance components of a) net radiation (𝑅n; red solid line), sensible heat flux (𝐻; black thick solid line), latent heat flux (𝜆𝐸; black thin solid line) and soil heat flux (𝐺; black dashed line); b) monthly Bowen ratio (𝛽) at the site during the study period.   3.4.2 Energy balance closure (EBC) For a good agreement between available energy and turbulent flux, the EBC should approach 1.0. The EBC at my site was 0.63 (R2=0.84, RMSE=47.7 W m-2) (Fig. 11), which means the turbulent flux could have been underestimated by 37%. The lack of energy balance closure is a common problem of EC measurements and has been extensively studied. Wilson et al. (2002) has reported half-hourly averages λE + H underestimated A at most sites by approximately 20% by summarizing 50 site-years of data from 22 FLUXNET sites. At my site, the radiation sensor and soil heat flux plates for Rn and G measurements were installed in the edge area over a homogeneous grass patch instead of the crop area, to avoid any interference with farm operations. Measurements of Rn and G were made in the potato field on selected 7 days (in 2018 summer) to investigate the relationships of Rn and G between the crop and edge areas (see Appendix E). It was observed that peak mid-day Rn values were lower in the potato 35  field than in the edge area because the view of in-field net radiometer include exposed soil surface which has a higher albedo (Fig. E. 2). At night, the potato field lost less longwave radiation (Rn less negative) than the edge area due to the field soil being drier than that at the edge. Also, the magnitude of G was lower in the potato field than in the edge area due to coverage of the potato crop. The linear relationships were then applied to correct continuous measurements of Rn and G in the edge area and obtain the new EBC. It was found the EBC during these 7 days was higher at 0.71 after correcting for Rn and G than that before correction (0.67) (Table E. 1). Therefore, the EBC at my site during the study period could have also been improved. In addition to this, the poor EBC could also be explained by inhomogeneity of my site (i.e. crop and edge areas) (Parent & Anctil, 2012). Also, the available energy may be slightly different from the EC measurements of λE and H due to the very local nature of the Rn and G measurements.    Figure 11: Energy balance closure (EBC) calculated as the slope of the linear regression of half-hourly 𝐻 + 𝜆𝐸 vs. 𝑅n – 𝐺 measurements at the site. EBC was 0.63 during the study period. The solid black line is the linear regression and the dashed line is the 1:1 line. -400 -200 0 200 400 600 800 1000-400-20002004006008001000Rn - G (W m-2)H + E (W m-2)  1:1 lineH + E = 0.63 (Rn - G ) - 2.05R2 = 0.8436  3.5 Flux footprint analysis The Flux Footprint Prediction (FFP) model (Kljun et al., 2015) was applied to obtain the flux footprint contributions, f, from the crop and edge areas for both the west and east side of the EC system. The calculation was based on the actual area of each landscape component, thus generating site dependent flux footprint contributions for further analysis in this study. A flux footprint model of this kind has commonly been used to compare GHG fluxes measured by point-scaled chamber and landscape-scaled EC systems when upscaling chamber fluxes (Christensen et al., 1996; Levy et al., 2020; Molodovskaya et al., 2011; Schrier-Uijl et al., 2010). As shown in Fig. 12, f for each landscape component generally follows a uniquely shaped curve related to wind direction, indicating it is closely related to the wind direction. However, the f curve varies within a small range for a specific wind direction, which is attributed to varying atmospheric conditions. It is also apparent that the f curve for the west and the east fields (the left and right panels in Fig. 12) exhibited a mirror image symmetry due to the location of the EC system being between the two fields. The highest fc + fe (0.95) was observed when the wind direction was between 240° and 300°, with the f of the crop (0.6~0.85) being higher and the f of the edge (0~0.35) being lower than those for wind directions outside this range. A similar pattern was observed for the east area with the maximum total f of the crop and the edge area being approximately 0.92 when the wind direction was between 60° and 100°. This suggested that the west-east spans of the crop and the edge areas, both in the west (218 m) and in the east (177 m), were long enough to cover more than 90% of total f. However, the wind direction range within which the maximum total f of the crop and the edge area remained narrower for the east than for the west due to the north-south span of the east field (125 m) being much less than the west field (600 m). In addition, due to the fact that the edge area in the east (14 m) was wider than that in 37  the west (10 m), the f of the east crop area was slightly lower (0.5~0.78) while the f of its edge area (0.1~0.4) was higher. It is recommended by Mauder et al. (2013) that a minimum percentage of 70% originating from the target area is required to enable the calculation of flux footprint contributions originating from one or more target areas, and this threshold can vary depending on the user’s requirements. To avoid losing too much data, the threshold was selected as 60% for the total f of the crop and the edge area together. Based on this criterion, we decided to filter the data according to the wind direction ranges which are highlighted in pink in Fig. 12.  Figure 12: Half-hourly flux footprint contributions (f) during the study period calculated using the two-dimensional Flux Footprint Prediction (FFP) model (Kljun et al., 2015) for two landscape components, the crop (subscript c) (a and b) and edge areas (subscript e) (c and d), and the total of these two components (e and f). The red coloured bars are the two wind-direction ranges selected for filtering the data (N2O, CO2 and CH4 fluxes) for the east and west fields (50~130˚ for the east field and 210~330˚ for the west field). In the case of the CH4 fluxes, it was to minimize the impact from the ditch even though no flux footprint correction was made. 38  3.6 N2O flux 3.6.1 Temporal variation of EC-measured N2O flux As shown in Fig. 13, the potato and pea field (east) was a N2O source during the entire study period. We were unable to measure GHG fluxes using the EC system from the beginning of the potato growing season since the EC system had not been installed until mid-June in 2018. However, chamber measurements from late May and early June provided an indication of the response of the field to the fertilizer application and enabled us to roughly estimate the GHG emissions during that period (see Appendix F). After the fertilization in mid-May, 𝐹N2O peaked at approximately 3 nmol m-2 s-1 likely due to the increased substrate in the soil enhancing the denitrification process (Gillam et al., 2008). Relatively high soil temperatures during the summer months June and July favored soil microbial activity, resulting in high 𝐹N2O which remained elevated for about 8 weeks. 𝐹N2O then declined to less than 0.5 nmol m-2 s-1 for the rest of the potato growing season, which was likely attributed to N uptake by the crop. After tuber harvest (18 Sep, 2018) N2O production was stimulated by frequent rainfall events and multiple 𝐹N2O pulses were observed with the highest 𝐹N2O value being approximately 3 nmol m-2 s-1 in September. In addition, the presence of organic matter from potato crop residues can promote sufficient heterotopic respiration, thus creating or enlarging anaerobic microsites in which denitrification can take place (Tiedje et al., 1984; Smith, 1997). Surprisingly, similar N2O emission pulses after the fertilizer application in the pea field didn’t occur, possibly due to several factors such as a lower fertilization rate compared to that of potatoes, the crop growth difference, the environmental conditions or the result of complex interactions among these factors. However, similar 𝐹N2O response to rainfall events occurred in late September and 39  October 2019, with 𝐹N2O reaching 2.5 nmol m-2 s-1. Generally, daily average 𝐹N2O was higher than that from a nearby blueberry field (~1km away) fertilized with similar amount of fertilizer to that of the potato field (~110 kg N ha-1). This is likely due to the fertilizer being applied to the blueberry field 4 times throughout the study period, resulting in higher N use efficiency and thus lower N2O emissions (Pow et al., 2019). The highest monthly total 𝐹N2O (0.15 g N2O-N m-2month-1) was observed in October 2018 (Table 2). Total 𝐹N2O in the non-growing season was the highest, followed by the potato growing season and then the pea growing season. Large emissions during the non-growing season accounted for approximately 60% and 77% of the annual emissions for the potato year and the pea year, respectively. This emphasizes the importance of mitigating the N2O emissions especially during the non-growing season in a region where the weather during that period is characterized by heavy precipitation.  Figure 13: Daily average N2O fluxes measured by EC (𝐹N2O) during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer (F) application events and tillage (T) events, respectively. The horizontal line during July 2019 represents the time period when the LGR spectrometer malfunctioned. The black dashed line from mid-May to mid-June was obtained using chamber measurements and linear interpolation.  40  3.6.2 Comparison of EC-measured and flux-footprint-corrected N2O fluxes The relationships of half-hourly and monthly total 𝐹N2O with and without flux footprint correction accounting for the edge effect are shown in Figs. 15 and 17, respectively. The strong linear relationships for both half-hourly observed data (R2=0.97, Table 2) and monthly total values (R2=0.95, Table 4) provide insights into a fixed coefficient that can be directly applied to the fluxes measured by the EC system to correct for the inevitable effect from non-crop areas (i.e., the edge area in this study) throughout the entire study period. This coefficient is site specific but is applicable with proportional footprint contribution from the edge area being relatively constant when the surface flux of the non-crop areas is determined and the micrometeorological variables are measured for the flux footprint analysis. Flux footprint corrected 𝐹N2O values were higher than EC-measured values by approximately 43% for half-hourly values and 27% for monthly total values (Tables 2 and 4). The magnitude of the N2O fluxes from the edge area was between 0 and 1 nmol m-2 s-1 (see Appendix F), which was similar to the background 𝐹N2O measured by the EC system. With the edge area being a weak source while much higher EC-measured N2O flux pulses (>1.5 nmol m-2 s-1) were constantly observed during the study period, the latter will be lower than actual fluxes (𝑄c_N2O) from the crop. Therefore, the flux footprint correction raised the EC fluxes to an extent depending mainly on the f value of the edge area representing its contribution to the fluxes measured by the EC system (Fig. 16). Moreover, the effect of the width of the edge area on the correction is evident in Fig. 15. The width of the edge area in the east was larger than that in the west, resulting in a larger slope (Table 2) indicating a larger effect of the edge area on EC-measured N2O flux.  41   Figure 14: Daily average N2O fluxes measured by EC (𝐹N2O; black solid line) and N2O fluxes corrected for the edge effect (𝑄c_N2O; red solid line) using the flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer (F) application events and tillage (T) events, respectively. The horizontal line during July 2019 represents the time period when the LGR spectrometer malfunctioned. The red dashed line (𝑄c_N2O) from mid-May to mid-June was obtained using chamber measurements made in the crop and linear interpolation. The black dashed line (𝐹N2O) from mid-May to mid-June was obtained using estimated 𝑄c_N2O and a fixed coefficient (  𝑄c_N2O/𝐹N2O = 1.38 during the potato GS).     42   Figure 15: The linear relationship between half-hourly N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O) during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid line is the linear regression line and the dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 2.  Table 2: Coefficients and model parameters for the linear relationship between half-hourly N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O).  Parameters a (slope) B (intercept) R2 RMSE  (nmol m-2 s-1) n Together 1.43 -0.06 0.97 0.23 8184 West field 1.40 -0.08 0.98 0.18 3294 East field 1.51 -0.08 0.95 0.24 4890     43  Table 3: Monthly total values of N2O fluxes measured by EC (𝐹N2O) and N2O fluxes corrected for the edge effect (𝑄c_N2O) during the study period. Year Month Monthly uncorrected N2O fluxes  (g N2O-N m-2month-1) Monthly flux footprint- corrected N2O fluxes  (g N2O-N m-2 month-1)     2018  May (start from May 15) * 0.10 0.14   June 0.12 0.17 July 0.10 0.14 August 0.03 0.03 September 0.08 0.11 October 0.15 0.21 November 0.07 0.10 December 0.03 0.05     2019 January 0.02 0.04 February 0.05 0.08 March 0.10 0.15 April 0.08 0.11 May 0.07 0.11 June 0.05 0.08 July 0.04 0.05 August 0.02 0.04 September 0.06 0.07 October 0.12 0.14  Totals Potato GS1 0.37 0.51 NGS2 0.67 0.98 Pea GS3 0.07 0.11 *The potato growing season started from May 15 in 2018 when the potato crop was planted. The monthly total value for May 2018 includes only half of the month. 1Potato GS was from May 15, 2018 to Sep 18, 2018. 2NGS was from Sep 19, 2018 to Jun 20, 2019. 3Pea GS was from Jun 21, 2019 to Aug 23, 2019.  44   Figure 16: Comparisons of total N2O fluxes measured by EC (𝐹N2O) and N2O fluxes corrected for the edge effect (𝑄c_N2O) during the potato growing season (west and east fields as a whole), non-growing season (west and east fields as a whole) and pea growing season (east field only). The darker columns indicate 𝐹N2O and the lighter columns indicate 𝑄c_N2O.  Figure 17: The linear relationship between monthly total N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O) during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The black solid line is the linear regression line and the dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 4. The data points don’t include those for May and June in 2018 because it didn’t involve EC measurements or flux footprint corrections until mid-June.  45  Table 4: Coefficients and model parameters for the linear relationship between monthly total N2O fluxes corrected for the edge effect (𝑄c_N2O) and N2O fluxes measured by EC (𝐹N2O). Parameters a (slope) b (intercept) R2 RMSE (g N2O – N m-2 month-1) n Value 1.29 0.01 0.95 0.01 16  3.7 CH4 flux Over the entire study period, 𝐹CH4 values were generally centred around zero, with evidence of both production and consumption of small amounts of CH4 (Fig. 18). However, overall the potato and pea fields were weak sources of CH4. Daily average 𝐹CH4 was between 0 and 5 nmol m-2 s-1 for most of the observation period while some relatively high values larger than 5 nmol m-2 s-1 were occasionally observed. Some high 𝐹CH4  values likely resulted from the CH4 emissions from the water-filled ditch. However, the magnitude of 𝐹CH4 from the ditch could not be determined without careful and regular chamber measurements taken above the water surface of the ditch throughout the study period. In addition, the bare soil after the crop harvest experienced poorly drained periods of time after frequent rainfall events, which could also contribute to CH4 emissions due to the anaerobic soil conditions favoring CH4 production. However, these emissions are difficult to partition from the fluxes measured by the EC system without confirmed surface fluxes from the ditch. Similar magnitudes of total 𝐹CH4  ranging from 0.40 to 0.68 CH4-C m-2 were observed among the three periods (Fig. 19; Table 5).  46   Figure 18: Daily average CH4 fluxes measured by EC (𝐹CH4) during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer (F) application events and tillage (T) events, respectively. The horizontal line during July 2019 represents the time period when the LGR spectrometer malfunctioned. The dashed line from mid-May to mid-June was obtained using chamber measurements made in the crop and linear interpolation.             47  Table 5: Monthly total values of CH4 fluxes measured by EC (𝐹CH4) during the study period. Year Month Monthly CH4 fluxes (g CH4-C m-2 month-1)     2018 May (start from May 15) * 0.04 June 0.08 July 0.14 August 0.17 September 0.03 October 0.07 November 0.08 December 0.05      2019 January 0.14 February 0.06 March 0.07 April 0.07 May 0.08 June 0.06 July 0.22 August 0.17 September 0.03 October 0.09  Totals Potato GS1 0.46 NGS2 0.68 Pea GS3 0.40 *The potato growing season started from May 15 in 2018 when the potato crop was planted. The monthly total value for May 2018 includes only half of the month. 1Potato GS was from May 15, 2018 to Sep 18, 2018. 2NGS was from Sep 19, 2018 to Jun 20, 2019. 3Pea GS was from Jun 21, 2019 to Aug 23, 2019.  48   Figure 19: Comparisons of total CH4 fluxes measured by EC (𝐹CH4) during the potato growing season (west and east fields as a whole), non-growing season (west and east fields as a whole) and pea growing season (east field only).  3.8 CO2 flux 3.8.1 Temporal variation of Re, GPP and NEE fluxes Daily half-hourly mean GPP and Re showed seasonal variability and behaved slightly differently between potato and pea growing seasons (Fig. 20). GPP increased gradually after the crop emerged and evolved with crop development reaching the highest daily mean values of 10 and 9 g C m-2 during the potato and pea growing seasons, respectively. After the first half of the potato growing season, the phase of potato growth transition from vegetative growth to tuber bulking and then during maturation, GPP decreased until harvest as the vines turned yellow and lost leaves. It was also observed that the time variation in GPP corresponded to the progression of leaf area index (LAI) of the potato crop (see Appendix H.1). Unlike the potato crop, the pea vine was still green until harvest, so the steep drop of GPP occurred outside the defined pea growing 49  season. The highest monthly assimilation capacity was observed in July 2018 (274 g C m-2 month-1) for the potatoes and in August 2019 (246 g C m-2 month-1) for the peas (Table 6). Compared to temporal patterns of assimilation, crop‐specific seasonal differences in respiration were less pronounced. Maximum Re was only slightly higher in the pea growing season (5.0 μmol m-2 s-1) than that in the potato growing season (3.5 μmol m-2 s-1), possibly due to higher precipitation and correspondingly higher soil moisture in the pea growing season. After crop harvest, Re declined slowly, with decomposition of fresh residues left on the field concurrently happening at a slow rate under lower soil temperature conditions in September. Following that, non-growing season Re remained relatively low at approximately 1 μmol m-2 s-1 throughout the winter and increased to 2 μmol m-2 s-1 when Ts started to increase in spring. The difference between growing season and non-growing season Re is mainly due to the absence of autotrophic respiration (Ra) during the non-growing season because Ra accounts for 60~90% of total Re during the growing season (Suleau et al., 2011). Non-growing season Re which is mainly heterotrophic respiration and was 38% and 62% of annual Re in the potato year and pea year, respectively, given that the time period without active vegetation cover was very long (8 months for the potato year and 10 months for the pea year). Due to photosynthesis (daytime) and respiration (day and night), daily variation in NEE showed a strong diurnal pattern corresponding to available light (Fig. 21). Overall, the ecosystem stored carbon from June to July for the potatoes and from July to August for the peas, with GPP being higher than Re. The greatest net CO2 uptake were approximately 8 μmol m-2 s-1 (July 2018) for potato crop and 5 μmol m-2 s-1 for pea crop (August 2019). It was reported by Ceschia et al. (2010) that the number of days of active vegetation cover was identified as one of the factors influencing NEP (NEP = -NEE) of C3/C4 winter/summer crops. I found that the number of days when there was active vegetation cover and carbon was stored in the fields with potatoes and peas 50  was similar at 56-64 days, with NEE values comparable for the two crops (Table 6). In contrast, carbon loss dominated the non-growing season, with Re exceeding GPP.   Figure 20: Daily average net ecosystem exchange measured by EC (NEE; black solid line), partitioned ecosystem respiration (Re; red solid line) and partitioned gross primary production (GPP; blue solid line). The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The dashed lines from mid-May to mid-June was obtained based on chamber measurements (Re) made in the crop and a rough estimation from the progression behavior of GPP at the beginning of the crop growing season.   Figure 21: Diurnal patterns of a) downwelling shortwave radiation (Sd) and b) EC-measured NEE on selected days (from July 10 to July 15 in 2018) (west and east potato fields as a whole).     51  Table 6: Monthly total values of net ecosystem exchange measured by EC (NEE), partitioned ecosystem respiration (Re) and partitioned gross primary production (GPP) without flux footprint correction during the study period.  Year Month NEE  (g C m-2 month-1) GPP  (g C m-2 month-1) Re  (g C m-2 month-1) 2018 May* 33  10  43  June -68  157  89  July -173  274  101  August 12  80  92  September 31  42  73  October 18  18  36  November 14  16  30  December 15  3  18  2019 January 13  3  15  February 4  8  12  March 14  12  27  April 13  32  44  May 8  37  45  June 17  56  73  July -73  189  116  August -110  246  136  September 38  52  90  October 19  21  40  Totals Potato GS1 -174  529  376  NGS2 120  115  299  Pea GS3 -164  395  244  *The potato growing season started from May 15 in 2018 when the potato crop was planted. The monthly total value for May 2018 includes only half of the month. 1Potato GS was from May 15, 2018 to Sep 18, 2018. 2NGS was from Sep 19, 2018 to Jun 20, 2019. 3Pea GS was from Jun 21, 2019 to Aug 23, 2019.  3.8.2 Comparison of EC-measured and flux-footprint-corrected Re The calculation of flux footprint-corrected nighttime Re using Eq (2.15) was similar to that of N2O and didn’t involve the ratio k. The correlations between flux-footprint-corrected and EC-measured nighttime Re on half-hourly and monthly basis (Figs 23 and 24; Tables 7 and 8) were strong, while the slope for half-hourly observed data (1.36) was lower than that for N2O (1.43) 52  (Table 2), indicating a smaller edge effect. This is because similar magnitudes of Re in the edge area and crop would have reduced Re heterogeneity of the whole footprint area. Also, a larger edge effect quantified by slopes in Table 7 was observed for the east field due to a wider edge area before the pea growing season. During the pea growing season, however, the flux-footprint-corrected Re was lower than EC-measured Re, which is opposite to that for the potato growing season. This can be attributed to relative magnitudes of Re in crop and edge areas. A higher Re in the edge area than that in the crop area would lead to flux-footprint-corrected Re being lower than EC-measured Re, and vice versa.  Figure 22: Daily average ecosystem respiration partitioned from EC-measured NEE (Re; black solid line) and ecosystem respiration corrected for the edge effect (𝑄c_er; red solid line) using the flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The red dashed line (𝑄c_er) from mid-May to mid-June was obtained using chamber measurements made in the crop and linear interpolation. The black dashed line (Re) from mid-May to mid-June was obtained using estimated 𝑄c_er and a fixed coefficient ((𝑄c_er/𝑅e = 1.22) during the potato GS).   53   Figure 23: The linear relationship between half-hourly nighttime ecosystem respiration corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration measured by EC (Re) during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid line is the linear regression line and the dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 7.  Table 7: Coefficients and model parameters for the linear relationship between half-hourly nighttime net ecosystem exchange corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration measured by EC (Re) during the study period.  a (slope) b (intercept) R2 RMSE (μmol m-2 s-1) n Together 1.34 -0.29 0.96 0.32 4605 West field 1.24 -0.26 0.95 0.23 1792 East field 1.33 -0.24 0.95 0.34 2813   Figure 24: The linear relationship between monthly totals of nighttime ecosystem respiration corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration (Re) during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The dashed line is the 1:1 line. Coefficients and model parameters are shown in Table 8. The data points don’t include those for May and June in 2018 because it didn’t involve EC measurements or flux footprint corrections until mid-June. 54  Table 8: Coefficients and model parameters for the linear relationship between monthly totals of nighttime ecosystem respiration corrected for the edge effect (Qc_ er) and nighttime ecosystem respiration (Re) during the study period.  a (slope) b (intercept) R2 RMSE (g C m-2 month-1) n 1.10 -0.34 0.87 10.98 16  Table 9: Monthly total values of ecosystem respiration (24-h Re) partitioned from EC-measured NEE and ecosystem respiration corrected for the edge effect (Qc_ nee) during the study period.  Year Month Re  (g C m-2 month-1) Qc_er (g C m-2 month-1) 2018 May* 43  58  June 89  120  July 101  120  August 92  108  September 73  82  October 36  51  November 30  39  December 18  22  2019 January 15  19  February 12  14  March 27  31  April 44  48  May 45  62  June 73  77  July 116  98  August 136  107  September 90  86  October 40  53  Totals Potato GS1 376  461  NGS2 299  360  Pea GS3 244  207  *The potato growing season started from May 15 in 2018 when the potato crop was planted. The monthly total value for May 2018 includes only half of the month. 1Potato GS was from May 15, 2018 to Sep 18, 2018. 2NGS was from Sep 19, 2018 to Jun 20, 2019. 3Pea GS was from Jun 21, 2019 to Aug 23, 2019.   55  3.8.3 Sensitivity analysis of flux-footprint-corrected GPP with respect to k  To obtain flux footprint-corrected GPP and NEE when the photosynthetic strength of the grass in the edge area was not measured, a value k (k = Qe_ gpp / Qc_ gpp) was introduced in this study. A similar concept, i.e., using a ratio, was also used by Morin et al. (2017) for determining CH4 fluxes. This assumes that the grass and the crop follow similar physiological processes (i.e., photosynthesis and plant respiration) and grew in adjacent areas with same soil type. Based on typical GPP values of the grass reported in the literature (e.g., Sheehy and Peacock, 1975), I considered 4 values of k (0.8, 1.0, 1.5 and 1.8) in my analysis.  Also, a short experiment aimed at a rough estimation of the sink strength of grass by using tarpaulins to cover grass along the ditch edge just north-west of the flux tower was conducted in May 2019. The grass GPP was estimated to be 25~40 μmol m-2 s-1 (see Appendix G).        Flux-footprint-corrected GPP was closely associated with EC-measured GPP temporally (Fig. 25). The correlations between flux-footprint-corrected and EC-measured GPP were also strong, while the slopes of linear relationships were smaller than that for Re at both half-hourly and monthly level (Figs. 26 and 27; Tables 10~12). The reason is that the difference of GPP in the crop and edge areas is characterized by a fixed k value instead of changing temporally depending on actual measurements as made in the edge are in the case of Re. However, the fixed k value implying that the temporal patterns of GPP difference in the crop and in the edge area did not change throughout the entire study period is only an ideal assumption as the life cycles of crop and grass didn’t completely match during the entire study period (i.e., grass grew actively in spring before crops were planted, see Appendix B. 6). The edge effect resulting from different widths of the edge area is more pronounced when the k value is more different from 1.0, as observed as larger differences between slopes for west 56  and east field (Table 10).  Figure 25: Daily average gross primary production partitioned from EC-measured NEE (GPP; black solid line) and gross primary production corrected for the edge effect (Qc_gpp; red solid line) with a k (k = Qe_ gpp / Qc_ gpp) value of 1.0 using the flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The red dashed line (Qc_gpp) from mid-May to mid-June was obtained based on a rough estimation from the progression behavior of GPP at the beginning of the crop growing season and linear interpolation. The black dashed line (GPP) from mid-May to mid-June was obtained using estimated Qc_gpp and a fixed coefficient (Qc_gpp /GPP = 0.98 during the potato GS).     57   Figure 26: Linear relationships between half-hourly gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 10.  Table 10: Coefficients and model parameters for the linear relationships between half-hourly gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the study period. k  a (slope) b (intercept) R2 RMSE (μmol m-2 s-1) n 0.8 Together 1.13 0.04 0.99 0.41 16585 West field 1.15 0.04 0.99 0.45 7041 East field 1.13 0.03 0.98 0.43 9544 1.0 Together 1.09 0.03 0.99 0.32 16585 West field 1.09 0.04 0.99 0.35 7041 East field 1.10 0.02 0.99 0.33 9544 1.5 Together 1.00 0.01 0.99 0.27 16585 West field 0.97 0.03 0.99 0.25 7041 East field 1.04 0.00 0.99 0.23 9544 1.8 Together 0.95 0.00 0.99 0.30 16585 West field 0.91 0.02 0.99 0.22 7041 East field 1.00 0.00 0.99 0.24 9544 58  Table 11: Monthly total values of gross primary production (GPP) partitioned from EC-measured NEE and gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values during the study period. Year  Month GPP  (g C m-2 month-1) Qc_ gpp   (g C m-2 month-1)  k = 0.8 k = 1.0 k = 1.5 k = 1.8 2018 May* 10  12  11  10  10  June 157  178  171  157  149  July 274  318  302  269  252  August 80  97  91  79  73  September 42  48  44  38  35  October 18  34  32  28  27  November 16  26  25  23  21  December 3  10  9  8  8  2019 January 3  11  10  9  8  February 8  14  13  12  11  March 12  19  18  16  15  April 32  38  36  32  29  May 37  68  61  50  45  June 56  62  58  51  48  July 189  192  190  184  180  August 246  254  250  241  237  September 52  46  45  43  42  October 21  34  34  33  32  Totals Potato GS1 550  637  605  539  507  NGS2 179  278  259  223  207  Pea GS3 408  421  415  401  394  *The potato growing season started from May 15 in 2018 when the potato crop was planted. The monthly total value for May 2018 includes only half of the month. 1Potato GS was from May 15, 2018 to Sep 18, 2018. 2NGS was from Sep 19, 2018 to Jun 20, 2019. 3Pea GS was from Jun 21, 2019 to Aug 23, 2019.        59   Figure 27: Linear relationships between monthly total values of gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 12.  Table 12: Coefficients and model parameters for the linear relationships between monthly total values of gross primary production corrected for the edge effect (Qc_ gpp) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and gross primary production (GPP) partitioned EC-measured NEE during the study period.  k a (slope) b (intercept) R2 RMSE (g C m-2month-1) n 0.8 1.05 15.54 0.99 11.35 16 1.0 1.02 13.66 0.99 9.69 16 1.5 0.97 12.68 0.99 7.56 16 1.8 0.95 10.54 0.99 9.42 16   3.8.4 Sensitivity analysis of flux-footprint-corrected NEE with respect to k  Flux-footprint-corrected NEE is the result of flux-footprint-corrected Re and GPP. Similar patterns and trends to that for GPP were observed, while slopes of linear relationships were slightly higher than that of GPP due to a larger effect of Re (Figs. 29 and 30; Tables 13~15). The cumulative NEE values fell into a range that is determined by the k values of 0.8 and 1.8 60  (Fig. 31). This suggests that the positive (i.e. k values higher than 1) and negative (i.e. k values lower than 1) effects of the edge area would lead to flux footprint-corrected NEE being less negative and more negative than EC-measured NEE, respectively. Throughout the entire study period, with k values varying from 0.8 to 1.8, the cumulative NEE would consequently change from -270 to -30 g C m-2, while it should be recognized that the magnitude of grass GPP actually changed significantly (i.e., from 16 to 36 μmol m-2 s-1 when crop GPP is 20 μmol m-2 s-1) from k of 0.8 to k of 1.8.  Figure 28: Daily average net ecosystem exchange measured by EC (NEE; black solid line) and gross primary production corrected for the edge effect (Qc_nee; red solid line) with a k (k = Qe_ gpp / Qc_ gpp) value of 1.0 using the flux footprint analysis and the chamber measurements made in the edge area during the study period. The potato growing season (GS) (west and east fields as a whole), non-growing season (NGS) (west and east fields as a whole) and pea growing season (GS) (east field only) are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red and blue vertical arrows indicate fertilizer application events (F) and tillage events (T), respectively. The red dashed line (Qc_nee) from mid-May to mid-June was obtained from estimated Qc_er and Qc_gpp. The black dashed line (NEE) from mid-May to mid-June was obtained from estimated Re and GPP.  61   Figure 29: Linear relationships between half-hourly net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period. The orange open circles denote the data points for the west field (include potato GS and NGS) and the green open triangles denote those for the east field (include potato GS, NGS and pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 13.  Table 13: Coefficients and model parameters for linear relationships between half-hourly net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period.  k  a (slope) b (intercept) R2 RMSE (μmol m-2 s-1) n 0.8 Together 1.16 -0.17 0.98 0.70 7404 West field 1.11 -0.30 0.98 0.78 3321 East field 1.21 -0.20 0.97 0.76 4083 1.0 Together 1.12 -0.06 0.98 0.65 7404 West field 1.05 -0.15 0.98 0.66 3321 East field 1.18 -0.10 0.98 0.68 4083 1.5 Together 1.02 0.19 0.97 0.70 7404 West field 0.93 0.14 0.98 0.54 3321 East field 1.12 0.08 0.98 0.65 4083 1.8 Together 0.97 0.30 0.97 0.76 7404 West field 0.86 0.29 0.98 0.51 3321 East field 1.09 0.17 0.97 0.69 4083   62  Table 14: Monthly total values of net ecosystem exchange measured by EC (NEE) and net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values during the study period. Year Month NEE (g C m-2 month-1) Qc_nee  (g C m-2 month-1) k = 0.8 k = 1.0 k = 1.5 k = 1.8 2018 May 33  46  46  47  48  June -68  -58  -52  -37  -30  July -173  -198  -182  -149  -132  August 12  11  17  29  35  September 31  34  37  43  46  October 18  17  19  22  24  November 14  12  14  16  17  December 15  12  13  14  14  2019 January 13  8  9  10  10  February 4  -1  0  2  2  March 14  11  12  14  16  April 13  10  12  16  18  May 8  -6  0  12  16  June 17  14  18  26  29  July -73  -94  -92  -86  -82  August -110  -147  -143  -135  -130  September 38  40  41  43  44  October 19  19  19  20  21  Totals Potato GS1 -174  -176  -144  -78  -46  NGS2 120  82  101  137  153  Pea GS3 -164  -214  -208  -194  -187  *The potato growing season started from May 15 in 2018 when the potato crop was planted. The monthly total value for May 2018 includes only half of the month. 1Potato GS was from May 15, 2018 to Sep 18, 2018. 2NGS was from Sep 19, 2018 to Jun 20, 2019. 3Pea GS was from Jun 21, 2019 to Aug 23, 2019.          63   Figure 30: Linear relationships between monthly total values of net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The black solid lines are linear regression lines and the dashed lines are 1:1 lines. Coefficients and model parameters are shown in Table 15.  Table 15: Coefficients and model parameters for linear relationships between monthly total values of net ecosystem exchange corrected for the edge effect (Qc_ nee) for four k (k = Qe_ gpp / Qc_ gpp) values: a) k=0.8; b) k=1.0; c) k=1.5 d) k=1.8 and net ecosystem exchange measured by EC (NEE) during the study period.  k a (slope) b (intercept) R2 RMSE (g C m-2month-1) n 0.8 1.22 -13.5 0.97 12.53 16 1.0 1.16 -9.98 0.96 13.33 16 1.5 1.06 -5.71 0.94 15.34 16 1.8 1.04 -2.56 0.91 17.56 16      64  3.8.5 Annual carbon balance and net ecosystem carbon budget of potato and pea crops From an atmospheric point of view, EC-measured NEE allows the quantification of the balance of CO2 entering and leaving the ecosystem during the time period of interest (Chapin et al., 2006). The cumulative NEE curve reflects the temporal sink or source activity of the studied ecosystem (see Fig. 31). A negative slope on the cumulative NEE curve indicates that the ecosystem behaves as a carbon sink (GPP > Re), while a positive slope indicates that the ecosystem behaves as a carbon source (GPP < Re). In this study, as for in other croplands, the cumulative patterns were characterized by a short-duration but intensive carbon uptake period corresponding to crop growth and a carbon release period corresponding to bare soil (Fig. 31). For both of the potato and pea fields, carbon uptake slightly exceeded carbon release, resulting in net uptake of 70 g C m-2 yr-1 (i.e., NEE = -70 g C m-2 yr-1 ranging from -101 to 76 g C m-2 yr-1, depending upon the k value) and 30 g C m-2 yr-1 (i.e., NEE = -30 g C m-2 yr-1 ranging from -122 to -20 g C m-2 yr-1 depending upon the k value) for potato and pea fields, respectively (Table 16).  Anthoni et al. (2004) found that annual NEE for a potato crop in Germany with reduced tillage was -34 g C m−2 yr−1 (ranging from -49 to 29 g C m−2 yr−1), which is comparable to the potato crop in this study where EC measurements started after the potato crop was planted thereby missing some intensive pre-planting tillage. Gilmanov et al. (2004) reported that year‐round annual legume crops at 17 flux tower sites in North America and three sites in Europe demonstrated a wide range of net ecosystem production (NEP; NEP = -NEE), with an average of -90 g C m−2 yr−1 (ranging from sinks of 207 g C m−2 yr−1 to sources of 573 g C m−2 yr−1). The average value of annual NEP in their study indicated overall CO2 source tendency for legume crops, while the pea crop in this study was a weak CO2 sink. Annual GPP for potatoes with a 4-month growing season length (671 g C m-2 yr-1, range from 65  660 to 837 g C m-2 yr-1, depending upon k values) was similar to that for peas with a shorter 2-month growing season length (672 g C m-2 yr-1, range from 672 to 775 g C m-2 yr-1, depending upon k values), suggesting that the intensity and pattern of photosynthetic assimilation are different between the two crops, which is more associated with crop type and crop growth. When reporting NEE fluxes on an annual basis, uncertainties in NEE measurements using the EC method comes from a number of systematic as well as random errors (Hollinger and Richardson, 2005; Aubinet, 2008). Among these the highest uncertainties are likely related to nighttime fluxes (Aubinet et al., 2008) arising from low turbulence conditions. Falge et al. (2001) and Moureaux et al. (2006) have reported such uncertainty to be some tens (0~50) of g C m−2 yr−1 for croplands. Overall, values in Table 16 suggest that the potato and pea fields in this study were weak sinks of atmospheric CO2. To take into account non-CO2 carbon losses associated with produce removed from the field, the net ecosystem carbon budget (NECB) was obtained as the total organic carbon accumulation or loss from the ecosystem (Smith et al., 2010). This method allows a better understanding of the ecosystem carbon balance in a shorter time period (i.e. one year) because soil sampling techniques are usually not able to detect carbon storage changes in the short-term change in because changes in soil organic carbon are relatively small compared with soil C content (Meshalkina et al., 2018). In this study, C export via crop harvest was roughly estimated based on the information on yields as fresh weight provided by the farmers and the typical total carbon content in dry matter of potato tubers and peas. Both crops shifted from being carbon sinks to carbon sources (except that pea crop was still a sink under k = 0.8 and 1.0) due to the relatively weak uptake of atmospheric CO2 and the large effect of carbon removal by the harvests (Table 17). Similar results that a part of the carbon accumulation may be exported from the site has been reported and discussed widely for 66  cropland ecosystems (Ciais et al., 2010; Ceschia et al., 2010; Gilmanov et al., 2010). However, as Gilmanov et al. (2010) pointed out that the ability of managed agroecosystems to serve as sinks of CO2 from the atmosphere depends on the ultimate fate of the harvested biomass. The reason is that although carbon loss via harvest (in nongaseous form) exceeding CO2 uptake from the atmosphere, it doesn’t necessarily mean the agroecosystem behaves as a source of CO2. It can be illustrated by the fact that the carbon laterally transported to other locations may be eventually returned to the atmosphere in gaseous form. This release, however, often happens far away from the original place (i.e., the agroecosystem) where CO2 was assimilated. While estimating NECB, a larger uncertainty associated with calculating C input and C output are often expected given that the information about C input and output provided by farmers may be rough. Given that NEE and C export have the greatest impact on the annual C budget of croplands (Ceschia et al., 2010), the importance of more accurate C export estimate should be stressed in further studies. In addition, it should be noted that the C input from seed potatoes was not included because of unavailability of relevant information. With the C input accounting for only a small proportion (5% as reported by Ceschia et al. (2010)) of the NECB, however, it would not result in large impacts on the NECB in our study.    67   Figure 31: Cumulative fluxes of a) net ecosystem exchange (NEE), b) gross primary production (GPP) and c) ecosystem respiration (Re) during the study period. The black solid lines in panels a, b and c indicate EC-measured NEE, partitioned GPP and Re during the study period (west and east fields as a whole for both the potato GS and NGS; east field only for the pea GS). The coloured dashed lines in panels a, b and c indicate NEE (Qc_nee), GPP (Qc_gpp) and Re (Qc_er) corrected for the edge effect. The potato growing season, non-growing season and pea growing season are indicated by brown, grey and green colour bars, respectively. The boundaries of the colour bars during growing seasons are the planting dates and harvest dates of crops. The red horizontal arrows at the top of the figure indicate the duration of the potato year (May 15, 2018 to May 14, 2019) and pea year (Oct 1, 2018 to Sep 30, 2019) defined in this study.  Table 16: Annual Re, GPP and NEE for the potato and pea crops without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values. The potato year was from May 15, 2018 to May 14, 2019 and the pea year was from Oct 1, 2018 to Sep 30, 2019.  Crop  Re  (g C m-2 yr-1) GPP (g C m-2 yr-1) NEE (g C m-2 yr-1) Potatoes EC-measured 601 671 -70  k = 0.8 736 837 -101 k = 1.0 736 793 -57 k = 1.5 736 703 33 k = 1.8 736 660 76 Peas EC-measured 642  672   -30  k = 0.8 652 775 -122 k = 1.0 652 749 -97 k = 1.5 652 697 -45 k = 1.8 652 672 -20   68  Table 17: Annual NEE, carbon removal from crop harvest and NECB for the potato and pea crops without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values. The potato year was from May 15, 2018 to May 14, 2019 and the pea year was from Oct 1, 2018 to Sep 30, 2019.  Crop  NEE  (g C m-2 yr-1) Carbon removal from crop harvest (g C m-2 yr-1) NECB (g C m-2 yr-1) Potatoes EC-measured -70  432  362  k = 0.8 -101 432 331 k = 1.0 -57 432 375 k = 1.5 33 432 465 k = 1.8 76 432 508 Peas EC-measured -30  78  48  k = 0.8 -122 78 -44 k = 1.0 -97 78 -19 k = 1.5 -45 78 33 k = 1.8 -20 78 58  3.9 Annual GHG budgets of potato and pea crops Annual GHG budget was quantified by converting N2O and CH4 to CO2 equivalents using values of global warming potential on a 100-year timescale, which is 298 for N2O and 34 for CH4 (Myhre et al., 2013). Summation of the three GHG totals weighted by their GWP yields CO2e values of 240 g CO2e m-2 yr-1 (range from 128 to 777 g CO2e m-2 yr-1, depending upon k values) for the potato field and 287 g CO2e m-2 yr-1 (range from 109 to 483 g CO2e m-2 yr-1, depending upon k values) for the pea field (Table 18), respectively, indicating that both fields acted as net sources of GHGs at an annual basis. Similar patterns of the contribution of each GHG to the total CO2e were observed for both crop fields. While N2O contributed the largest proportion (60% for potatoes and 69% for peas) to the total annual GHG budgets, CH4 only accounted for a small proportion (less than 10%). Uptake of CO2 was offset by relatively large N2O emissions, contributing to global warming. Larger N2O emissions coupled with higher CO2 uptake in the potato field resulted in the total CO2e of potato field being similar to that of the pea field. Due to 69  the predominance of N2O and CO2 in estimating annual GHG budgets, caution should be taken when comparing these two crop years as they share common winter. The two crops received significantly different nitrogen fertilizer inputs and a considerable proportion (60% of total N2O emissions in the full potato year) of N2O emissions actively responding to climate variables (i.e. precipitation) occurred outside the growing season. Therefore, the subsequent time period with the absence of vegetation following pea harvest may have exhibited different patterns of N2O emissions under different climate conditions from that of the common winter in this study. Under similar climate conditions (P, 𝑇s and 𝜃s; see Table 1 and Figs. 5 and 6) in the months of September and October in both 2018 and 2019, a lower rate of N2O emissions likely resulting from a lower N application rate was recorded with continuing two months of measurements following pea harvest. This could provide the evidence of lower N2O emissions in reporting the actual full year for the pea crop (i.e. continuing EC measurements throughout the long time period of bare soil after pea harvest). In the case of CO2, given that Re was constantly low and did not exhibit an obvious temporal variation responding to changing climate during the winter, it may not have contributed much to the non-growing season Re in the pea year. However, it should be noted that the period following the preparation of the potato field for planting was not included in the potato year in our study. It is recognized that tillage used in field preparation, which is always intensive for potatoes in this region, might result in effects that should not be ignored (i.e. significant increase in Re and consequently higher NEE for the potato year). Overall, with these effects taken into account, the difference between annual GHG budgets of the two crops may still be relatively small if the year-round measurement for the pea crop were complete and included in our dataset. After flux footprint correction, total CO2e was much higher than that without correction for potatoes due to much higher corrected N2O flux except when k value was 0.8 (Table 18). With a 70  much higher corrected N2O flux and more negative CO2 flux for peas, total CO2e was lower after correction than before correction when k value was 0.8 and 1.0. However, this trend was opposite when k value was 1.5 and 1.8. For both crops, N2O is still the largest contributor to annual total CO2e after flux footprint correction. Table 18: CO2, N2O and CH4 expressed in CO2 equivalents and total CO2e for the potato and pea years (g CO2e m-2 yr-1) without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values. The potato year was from May 15, 2018 to May 14, 2019 and the pea year was from Oct 1, 2018 to Sep 30, 2019. The values in the brackets are the contributions of each GHG to total CO2e. Crop  CO2  N2O – CO2e CH4 – CO2e Total CO2e Potatoes EC-measured -257 (34%) 450 (60%) 48 (6%) 241 k = 0.8 -370 (35%) 642 (61%) 48 (4%) 320 k = 1.0 -209 (23%) 642 (71%) 48 (6%) 481 k = 1.5 121 (15%) 642 (79%) 48 (6%) 811 k = 1.8 279 (29%) 642 (66%) 48 (5%) 969 Peas EC-measured -109 (22%) 347 (69%) 50 (9%) 288 k = 0.8 -447 (45%) 506 (50%) 50 (5%) 109 k = 1.0 -356 (39%) 506 (55%) 50 (6%) 200 k = 1.5 -165 (23%) 506 (70%) 50 (7%) 391 k = 1.8 -73 (12%) 506 (80%) 50 (8%) 483  3.10 Environment controls on C fluxes 3.10.1 Relationship between PAR and GPP Rectangular hyperbolic light response functions were used to parameterize the relationship between GPP and PAR (Fig. 32). The dataset of the entire study period was split into three periods, 71  those with predominating plant activity in the two growing seasons, and that in the non-growing season. The initial slopes of the light response curves α were slightly higher in the two growing seasons (ranged from 0.2 to 0.4 μmol CO2 (μmol photons)-1) than in the non-growing season (ranged from 0.1 to 0.2 μmol CO2 (μmol photons)-1) (Table 19). The small α values for the non-growing season which were expected to be much closer to zero are likely due to the photosynthetic activity of the grass in the edge area are being measured by EC. Carbon assimilation rates at light saturation, GPPmax, varied among different time periods ranging from 2.32 (in the non-growing season) to 22.43 μmol m-2 s-1 (in the pea growing season). Within each time period group, the differences among EC-measured and flux footprint corrected GPP values under different k values were caused by the effect of flux footprint correction on the absolute value of GPP. It is also observed that R2 values of the curve model were relatively low for the potato growing season because the scattered data points were more controlled by lower GPP values occurring in the late growing season when assimilation capacity gradually dropped from the peak values. Therefore, the fact that GPPmax might have largely appeared to be between 20 and 30 μmol m-2 s-1 if only the crop peak growing period (i.e. June and July) was investigated in this model fit. However, this value is still much lower than 33~38 μmol m-2 s-1 reported by Anthoni et al. (2004) for a potato field in Germany. This can be explained by lower leaf area index (LAI) of potatoes at my study site. LAI is a critical biophysical property of vegetation that affects photosynthetic activity (Gitelson et al., 2014). The effective LAI recorded at the potato field in Germany (Anthoni et al., 2004) was 6 m2 m-2 (measured on Jul 6, 2002), which was about twice that at my site (2.93 ± 0.75 m2 m-2, measured on 6 July, 2018, see Appendix H.1). Emmel et al. (2020) also reported a strong linear relationship between GPPmax and PAR under direct and diffuse light conditions as a function of canopy height (hc). In the crops they studied (peas, potatoes, wheat, rapeseed), they reported 72  that the GPPmax of peas under diffuse light was between 15 and 20 μmol m-2 s-1, which was comparable to that at my site (20~23 μmol m-2 s-1). Moreover, GPPmax of potato (18~22 μmol m-2 s-1 with hc of ~0.5m) was also similar to that at my site (GPPmax was estimated as 20~30 μmol m-2 s-1, hc was around 0.45m in mid-July, see Appendix H.2) if only peak growing period was considered.  Figure 32: Rectangular hyperbolic relationship (Eq (2.7)) between half-hourly gross primary production (GPP) and photosynthetically active radiation (PAR) for the potato growing season (a-e), nongrowing season (f-j) and pea growing season (k-o) for different k values from 0.8 to 1.8. The first column (a, f and k) shows EC-measured GPP, and the other columns show flux footprint corrected Qc_gpp.  Coefficients and model parameters are shown in Table 19.             73  Table 19: Coefficients and model parameters of rectangular hyperbolic relationship (Eq (2.7)) between half-hourly gross primary production (GPP) and photosynthetically active radiation (PAR) for the potato growing season, non-growing season and pea growing season without flux footprint correction (EC-measured) and with flux footprint correction under four k (k = Qe_ gpp / Qc_ gpp) values.  GPP ratio (k) α GPPmax R2 RMSE n Potato growing season EC-measured 0.03 14.81 0.47 4.63 2370 0.8 0.03 16.69 0.47 5.24 2370 1.0 0.03 15.92 0.47 5.00 2370 1.5 0.03 14.29 0.46 4.49 2370 1.8 0.02 13.45 0.45 4.23 2370 Non-growing season EC-measured 0.01 2.73 0.52 0.93 7560 0.8 0.02 3.42 0.42 1.46 7560 1.0 0.02 3.09 0.44 1.30 7560 1.5 0.02 2.54 0.47 1.05 7560 1.8 0.02 2.32 0.48 0.95 7560 Pea growing season EC-measured 0.04 20.07 0.61 4.33 2446 0.8 0.04 22.43 0.60 4.77 2446 1.0 0.04 22.03 0.61 4.70 2446 1.5 0.04 21.11 0.61 4.56 2446 1.8 0.04 20.60 0.61 4.48 2446  3.10.2 Relationship between ecosystem respiration and soil temperature The relationship between Re and Ts was investigated using an exponential equation (ln𝑅e =𝐴 + 𝐵𝑇s ) for both 𝑅e and Qc_er (Fig. 33). It is apparent that both 𝑅e and Qc_er were positively correlated to Ts at the 5-cm depth with R2 values of 0.59 and 0.61, respectively (Table 20). Differences in R10 and Q10 values for 𝑅e and Qc_er may possibly be attributed to the effect of flux footprint correction on the absolute value of Re directly measured by EC. It was also observed that actual Re tends to be below the line derived from the exponential relationship after approximately 23 ℃. These high Ts values (larger than 23 ℃) coincided with lowest θs during the measurement period that occurred in July 2018 with relatively dry conditions (Fig. 6). This suggests that Re might have been constrained by low soil moisture and resultant less active soil microbial activity. Similarly, Jassal et al. (2008) found that RS was largely unaffected by temperature under soil water 74  stress conditions (soil water content at the 4-cm depth (θ) <0.11 m3 m−3, the lowest θ was about 0.07 m3 m−3) in an 18‐year‐old temperate Douglas‐fir stand. Therefore, a function incorporating soil moisture (𝑅e = (𝑎 + 𝑏𝜃 +𝑐𝜃)𝑅10𝑄10(𝑇s−10)/10 ) was also tested. The model parameters are shown in Table 21. However, the R2 values were not improved after introducing soil moisture. This might have been because other biotic factors such as soil organic C and plant growth rate, coupled with abiotic factors such as soil temperature and water-filled porosity, all control the supply of readily mineralizable substrates (Franzluebbers et al., 2002). Crop development, at the seasonal scale, acts as a more important driving variable for Re than temperature because autotrophic respiration could represent the largest proportion of Re during vegetative periods (Béziat et al., 2009). This adds temporal and spatial complexity to this studied ecosystem given that the life cycle of the crop interacted with changing soil variables and EC measurements give whole-ecosystem respiration. Further evidence is needed to understand the controls on each component of Re.  Figure 33: Linear relationship a) between ln (Re) and Ts; b) between ln (Qc_er) and Ts. Coefficients and model parameters are shown in Table 20.        75  Table 20: Coefficients and model parameters of linear relationship between ln (Re) and ln (Qc_er) and Ts (ln𝑅e = 𝐴 + 𝐵𝑇s).     A B R10  (μmol m-2 s-1) Q10 R2 RMSE (μmol m-2 s-1) n Re  -0.84 0.10 1.17 2.72 0.59 0.40 3954 Qc_er  -0.97 0.11 0.94 3.00 0.61 0.44 3954  Table 21: Coefficients and model parameters of the exponential-temperature relationship with a hyperbolic soil moisture relationship between Re and Qc_er and 𝑇s (𝑅e = (𝑎 + 𝑏𝜃 +𝑐𝜃)𝑅10𝑄10(𝑇s−10)/10).    a b c R10 (μmol m-2 s-1) Q10 R2 RMSE (μmol m-2 s-1) n Re 1.36 0.68 0.00 0.86 2.08 0.43 0.87 3954 Qc_er  0.26 0.27 0.31 1.24 1.65 0.42    1.00 3954   76  Chapter 4: Conclusions 4.1 Summary of key findings I measured greenhouse gas (GHG) emissions from potato and pea crops in Delta, BC. located in the lower Fraser Valley using the eddy-covariance (EC) technique. Due to the presence of the non-cropped area (farm road or edge) near the EC tower and resultant inhomogeneity of the footprint fetch, I corrected the edge effect and calculated GHG (only N2O and CO2) fluxes for the crop field alone, by combining EC measurements, manual chamber measurements and flux footprint analysis. Finally, I reported the annual carbon budget, net ecosystem carbon balance (NECB) and CO2e emissions of the potato and pea crops. The key findings are summarized as follows: 1. Significant N2O emissions were triggered by rainfall events during the non-growing season (with no winter cover crop in this study area), where post-harvest weather is characterized by frequent and heavy precipitation. Non-growing season N2O emissions accounted for > 60% of annual emissions, while the remaining emissions occurred following one-time fertilizer application in the growing seasons. 2. The magnitude of ecosystem respiration (Re) differed mainly between growing and non-growing seasons. Temporal variation in gross primary production (GPP) was closely associated with crop development. The number of days when there was active vegetation cover and carbon was stored in the potato and pea fields was similar at 56-64 days. 3. Re and N2O fluxes were generally higher after flux footprint corrections than those directly measured by EC due to smaller Re and N2O fluxes in the edge area compared with the cropped area. Flux footprint corrected GPP and NEE were higher or lower than EC-measured values, depending on the value of k (k = Qe_ gpp/Qc_ gpp) used. The edge effect was mainly 77  determined by the difference in the magnitude of GHG fluxes between crop and edge areas and the width of the edge area. 4. After flux footprint correction, the potato and pea crops were both weak CO2 sinks with annual NEE values being -57 and -97 g C m-2 yr-1 (k = 1), respectively. After taking C export via crop harvest into account, the potato crop shifted from being a C sink to being a C source losing 375 g C m-2 yr-1, while the pea crop became near neutral sequestering only 19 g C m-2 yr-1. 5. After flux footprint correction, the annual GHG budgets in terms of CO2e for the potato and pea crops were 481 and 200 g CO2e m-2 yr-1 (k = 1), respectively. For both potato and pea crops, N2O contributed the largest proportion to the annual total CO2e and offset CO2 uptake from the atmosphere, making both crop fields net sources of GHGs. 4.2 Management recommendations This study showed that N2O emissions during the non-growing season were significant due to the absence of a winter crop and frequent rainfall events, suggesting that planting cover crops would result in decreased annual N2O emissions (Petersen et al., 2011). Also, split application of N fertilizers could be considered as it could promote a better match with crop growth, thus enhancing N use efficiency (Burton et al., 2008).  4.3 Implications for future research While flux footprint analysis was successfully applied to EC-measured N2O and CO2 fluxes to account for the edge effect and to obtain actual surface fluxes from the crop area, the edge area itself wasn’t homogeneous because it comprised a grass strip, a water-filled ditch and a compacted farm road. The two collars located in the grass strip were assumed to represent the whole edge area, which may cause errors in surface flux measurements in the edge area. However, this could be improved by increasing the spatial resolution and measurement 78  repetitions of closed-chamber measurements. The k value which was assumed to be constant during the entire period although the life cycles of grass and the crop were not completely synchronous. 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Agricultural and Forest Meteorology, 113(1–4), 223–243. https://doi.org/10.1016/S0168-1923(02)00109-0  91  Appendices  Appendix A  Location of the study site  Figure A. 1: Reynelda Farm indicated by a red star is located on the Westham Island, which is on the southwest edge of the Fraser River delta.          92  Appendix B  Site photos showing instrumentation and field conditions B.1 Instrumentation of the eddy covariance (EC) system  Figure B. 1: The eddy covariance (EC) system located above the water-filled ditch. The red squares indicate the location of four fans which were used to maintain a constant trailer temperature for the LGR spectrometer to work properly.   Figure B. 2: Photos of the sonic anemometer (a), air sampling inlet tubes of the LGR and LI-7200 instruments (c), and trailer with LGR spectrometer inside (d).  93   Figure B. 3: Photos of the LGR spectrometer (a), pump for the spectrometer (b), site computer (c), and communication system for daily transmission of data to the UBC Biometeorology and Soil Physics Group (d).  B.2 Soil and climate sensors  Figure B. 4: Photo of the location of the soil sensors. Instead of being installed in the cropped area, soil sensors were installed near the grass in the edge area. 94   Figure B. 5: Photos of a) the soil sensors measuring soil temperatures and soil moistures at the 5-cm, 20-cm and 60-cm depths and b) the net radiometer installed in the edge area above the grass.  B.3 Photos of the potato field  Figure B. 6: Photos of the potato field on a) Jun 19, 2018, b) Jul 17, 2018, c) Jul 27, 2018, d) Aug 15, 2018, e) Sep 11, 2018 (one week before potato harvest) and f) Sep 26, 2018 (one week after potato harvest).    95  B.4 Photos of the field during the non-growing season  Figure B. 7: Photos of field conditions on a) Feb 5, 2019 (snow cover), b) Feb 19 , 2019 (poor drainage during winter), c) Mar 22, 2019 (soil cracks in spring) and d) Jun 10, 2019 (after tillage). 96  B.5 Phtotos of the pea field  Figure B. 8: Photos of the pea field on a) Jun 28, 2019, b) Jul 4, 2019, c) Jul 16, 2019, d) Aug 13, 2019, e) Aug 23, 2019 (pea harvest date) and f) Sep 29, 2019. 97  B.6 Photos of the edge area  Figure B. 9: Photos of the edge area during the study period. a and b show the edge areas which consisted of water-filled ditch with grass and farm road both in the west and east during the potato growing season in 2018, respectively. c and d show the dead grass in the edge area during winter months until the end of March. e, f and g show the regrowth of grass in April, May and June. The significant difference between crop and edge areas can be seen. h shows peas were planted adjacent to the grass without leaving a machinery turn around strip that was there before planting the peas (see photo b)).  98  Appendix C  Management operations on the farm Table C. 1: Farm operations for potato and pea crops during the study period. Crop Date Management  Potatoes (2018)     May 13 Fertilizer application (Broadcast/disked in) Rate: 50.4 kg N ha-1 May 15 Fertilizer application (Side dressing) Rate: 61.6 kg N ha-1 May 15 Potato planting  Sep 18 Harvest Yield: 34.6 t ha-1 (wet mass of tubers) Peas (2019) May 3 Tillage   Fertilizer application (Broadcast)  Jun 21 Pea planting Rate: 33.6 kg N ha-1 Aug 23 Harvest Yield: 10.4 t ha-1 (wet mass of peas)             99  Appendix D  Friction velocity (u*) threshold determination u* threshold was selected based on nighttime ecosystem respiration (Re) being relatively constant as u* increases. It can be seen from Fig. D. 1 that nighttime Re remained relatively constant after u* reached 0.1 m s-1. Therefore, 0.1 m s-1 was selected as the u* threshold.  Figure D. 1: Relationship between nighttime ecosystem respiration (Re) and friction velocity (u*) during the study period. u* was binned in 20 classes from 0 to 0.5 m s-1.                        100  Appendix E  Energy balance closure experiment in summer 2018   Figure E. 1: Photos of a) a net radiometer mounted 1 m above the potatoes (down-facing view included three potato rows and exposed soil surface) and b) soil heat flux plate inserted in the soil adjacent to a potato row.   Figure E. 2: a) Net radiation (Rn) and b) soil heat flux (G) measured in the edge area (before correction; black solid line) and corrected (red solid line) using linear relationships between measurements in the potato field and in the edge area from DOY 249 to 254 in 2018.      101  Table E. 1: Slope, intercept and coefficient of determination of linear relationships between (Rn -G) and (H + λE) measured in the edge area and corrected using linear relationships between measurements in the potato field and in the edge area from DOY 249 to 254 in 2018.                102  Appendix F  Manual chamber measurements of GHG fluxes  Figure F. 1: Chamber measurements of N2O fluxes in the edge area from May 27, 2018 to Aug 23, 2019. The data points are the average value of N2O fluxes in the west and east edge areas and error bars are also shown. Missing error bars are due to missing measurements or outliers being removed.  Figure F. 2: Chamber measurements of CH4 fluxes in the edge area from May 27, 2018 to Aug 23, 2019. The data points are the average value of CH4 fluxes in the west and east edge areas and error bars are also shown. Missing error bars are due to missing measurements or outliers being removed.   103   Figure F. 3:Chamber measurements of CO2 fluxes in the edge area from May 27, 2018 to Aug 23, 2019. The data points are the average value of CO2 fluxes in the west and east edge areas and error bars are also shown. Missing error bars are due to missing measurements or outliers being removed.                        104  Appendix G  Tarpaulin experiment Due to the fact that CO2 uptake was observed during the non-growing season with the crop field being completely bare soil and the grass in the edge area growing actively (Fig. B. 9), the significant effect of the grass photosynthesis during the non-growing season was confirmed by covering the grass in the edge area near the EC system. In order to provide further insight into the sink strength of the grass, a 5-day experiment was conducted from May 21 to May 25, 2019 (Fig. G. 1). The grass immediately to the northwest of the EC system was completely covered by tarps in order to eliminate the effect of grass photosynthesis on EC-measured NEE. Therefore, the sink strength of the grass could be roughly determined by comparing the NEE before and after the tarps were in place using Eq (2.14). The GPP value of grass was estimated to be between 25 and 40 μmol m-2 s-1.  Figure G. 1: Photos of grass immediately northwest of the EC tower covered by tarps.      105  Appendix H  Leaf area index (LAI) and canopy height (hc) measurements of potatoes H.1 Leaf area index (LAI) of potatoes  Figure H. 1: Effective leaf area index (LAI) of potatoes. Data points are shown as the average value of 3 measurements and error bars are also shown.  H.2 Canopy height (hc) of potatoes  Figure H. 2: Canopy height (hc) of potatoes. Data points are shown as the average value of 3 measurements and error bars are also shown.        106  Appendix I  Comparison of LGR and LI-7200 gas analyzer measurements of latent heat fluxes  Figure I. 1: Comparison of latent heat flux (𝜆𝐸) measurements between the Los Gatos Research (𝜆𝐸𝐿GR) and LI-COR (𝜆𝐸LI7200) gas analyzers. The regression line is the black solid line and the dashed line is the 1:1 line.                        107  Appendix J  Flux footprint climatology   Figure J. 1: Daytime flux footprint climatology (before filtering for wind directions determined for the west field) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar.  Figure J. 2: Daytime flux footprint climatology (after filtering for wind directions determined for the west field, 210˚ to 330˚) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. 108   Figure J. 3: Nighttime flux footprint climatology (before filtering for wind directions determined for the west field) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar.   Figure J. 4: Nighttime flux footprint climatology (after filtering for wind directions determined for the west field, 210˚ to 330˚) on a half-hourly basis during the potato growing season for the west potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. 109   Figure J. 5: Daytime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar.   Figure J. 6: Daytime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. 110   Figure J. 7: Nighttime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar.   Figure J. 8: Nighttime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the potato growing season for the east potato field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar.  111   Figure J. 9: Daytime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar.  Figure J. 10: Daytime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. 112   Figure J. 11: Nighttime flux footprint climatology (before filtering for wind directions determined for the east field) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar.  Figure J. 12: Nighttime flux footprint climatology (after filtering for wind directions determined for the east field, 50˚ to 130˚) on a half-hourly basis during the pea growing season for the east pea field. The yellow star indicates the location of the EC tower. The x- and y-axis indicate the distance from the EC tower along the east-west and north-south directions, respectively. The red contour lines indicate from 10 to 90% (with 10% interval) contribution of EC-measured fluxes at the tower. The white lines indicate the location of crop (c) and edge (e) areas. The flux footprint contributions (f) in each pixel (1m by 1m) are indicated by a range of colours shown in the colourbar. 

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