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Nitrous Oxide Flux from a Heterogeneous Agricultural Landscape: Analysis of Source Contribution by Eddy Covariance and Static Chambers

Marina Molodovskaya* Dep. of Biological and Environmental Engineering

Cornell Univ.

Ithaca, NY 14853

Jon Warland Dep. of Land Resource Science

Univ. of Guelph

Guelph, ON, N1G 2W1 Canada

Brian K. Richards Dep. of Biological and Environmental Engineering

Cornell Univ.

Ithaca, NY 14853

Gunilla Öberg Inst. for Resource, Environment and Sustainability

Univ. of British Columbia

Vancouver, BC, V6T 1Z4 Canada

Tammo S. Steenhuis Dep. of Biological and Environmental Engineering

Cornell Univ.

Ithaca, NY 14853

ABSTRACT

Eddy covariance and static chambers are different-scale methods for monitoring agricultural N2O that, when used together on heterogeneous agricultural landscapes, can help identify flux sources and sinks and evaluate the effect of management interventions on landscape-scale N2O emissions. This study compared the N2O flux data obtained by eddy covariance and static chambers during a short-term N2O measurement campaign from two adjacent agricultural treatments: alfalfa (Medicago sativa L.) and corn (Zea mays L.)

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fields. Wind direction data from micrometeorological observations were used to downscale the integrated eddy covariance N2O flux and estimate the treatment contributions. The N2O data from static chambers installed on each treatment were used to verify the partitioned eddy covariance fluxes. Both methods consistently showed greater emissions for the alfalfa field, which received more N fertilizer earlier in the growing season. Two methods were also compared with respect to the landscape-integrated N2O flux measured at the eddy covariance mast location. Upscaling the chamber N2O fluxes was performed by totaling the contributions from individual chambers weighted toward the source area share associated with their field locations using a simple footprint model. The comparison of the chambers’ total to the measured eddy covariance emissions showed a difference of 7 to 33% between the methods. The best agreement was observed when the integrated eddy covariance flux was associated with uniform wind direction and a homogeneous source area. The results suggest that localization of the flux source using wind directions and footprint information can help in comparing different-scale N2O emissions.

Received 10 Nov. 2010. *Corresponding author (m.molodovskaya@ires.ubc.ca).

Abbreviations: EC, eddy covariance; TDLAS, tunable diode laser absorption spectrometer.

The steady increase of atmospheric N2O is of concern given its persistence, its role in stratospheric ozone depletion, and its high global warming potential as a greenhouse gas (310 CO2 equivalent). Agricultural N fertilization is believed to be the greatest anthropogenic contributor of N2O to the atmosphere (Kroeze et al., 1999). Substantial recent efforts have gone into the development of reliable and robust tools for agricultural N2O measurements. Two major groups of methods—each with its own methodological niche, advantages, and limitations—are generally considered for N2O flux measurements from agricultural soils: (i) small-scale (up to 1 m2) ground-based conventional chambers, and (ii) landscape-scale (up to 1 km2) mast-based micrometeorological observations (Denmead, 2008).

For many years, there has been no alternative to chambers for process-level studies of gaseous emissions from soils, and chamber-based studies have consequently formed the basis for the current understanding of agricultural N2O. Regular chamber N2O measurements coupled with gas chromatograpy–electron capture detection analysis began in the 1970s and 1980s (Delwiche and Rolston, 1976; Rolston et al., 1978; Hutchinson and Mosier, 1981) and continue to date (e.g., Yates et al., 2006; Hernandez-Ramirez et al., 2009; Halvorson et al., 2010; Johnson et al., 2010). The methodological principles remain the same, but chamber design, deployment protocols, and flux calculation precision have significantly improved with time (Hutchinson and Livingston, 2001; Rochette and Eriksen-Hamel, 2008). Modern chambers provide affordable and reliable soil N2O estimates; there are, however, serious methodological limitations related to the small area coverage and the disturbance of the soil environment caused by the chamber collars and closures (Denmead, 2008). In addition, low sampling frequency and chamber density result in poor temporal and spatial resolution of the data.

During the last decade, micrometeorological greenhouse gas measurements have become more common as an alternative to the traditional chamber technique (Fowler et al., 2001; Edwards et al., 2003; Pattey et al., 2007). In contrast to chambers, micrometeorological instrumentation does not disturb the soil ecosystem and allows field-scale, continuous, real-time flux monitoring. One of the micrometeorological methods widely used for N2O measurements is eddy covariance (EC), which is based on direct flux calculations from instantaneous changes in the vertical wind speed and trace gas concentration in the air above the soil surface (Stull, 1988, p. 427–428; Baldocchi, 2003). Current EC equipment for N2O flux monitoring includes a tunable diode laser absorption spectrometer (TDLAS) coupled with a three-dimensional sonic

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anemometer for N2O concentration and wind speed measurements, respectively. Continuously operated EC systems provide high-frequency, fast-response data suitable for estimates of N2O flux temporal variability, and they are routinely used for both short- and long-term monitoring of agricultural N2O (Laville et al., 1999; Scanlon and Kiely, 2003; Di Marco et al., 2004; Neftel et al., 2007, 2010).

The EC method is typically used for integrated flux monitoring over large and homogenous source areas but is not particularly helpful in estimating localized soil N2O variability because it represents single-point measurements. When EC fluxes are monitored over a homogeneous agricultural landscape (the same land treatment, crop type, fertilization, etc.), the results are in line with corresponding chamber measurements (Smith et al., 1994; Christensen et al., 1996; Laville et al., 1999). If, however, the underlying terrain includes heterogeneities of crops, soil conditions, or other treatments, flux contributions from a specific source can strongly influence the overall N2O emissions and thus skew the emission estimates (Smith et al., 1994; Christensen et al., 1996; Pattey et al., 2007). Therefore, localizing emission sources is important for understanding how various management practices affect agricultural N2O emissions. Recent studies suggest that wind direction data can be used to identify emission sources and that simultaneous chamber monitoring of each contributing landscape component can be used to verify integrated EC fluxes (Famulari et al., 2010; Schrier-Uijl et al., 2010). A significant improvement (from 55 to 13% difference between the chambers and EC methods) in the CO2 and CH4 flux estimates was found when including all landscape components that had a greenhouse gas generating capacity (Schrier-Uijl et al., 2010).

The aim of this study was to examine N2O flux contributions from two adjacent agricultural treatments, alfalfa and corn, to the landscape-integrated flux. The N2O flux was measured by EC and static chambers during the short-term intense campaign. The EC mast was installed between the fields, and the EC measurements thus represented the two-treatment integrated N2O flux. Two sets of static chambers were installed on each individual treatment. Two approaches were used for the method comparison to synchronize the measurements’ scale. First, the EC N2O flux was downscaled to the treatment scale using the wind direction data from micrometeorological observations as an indicator of the flux origin. The partitioned contributions from each treatment were estimated and verified by the data from static chambers. Second, the two methods were also compared with respect to the landscape-integrated N2O flux measured at the EC mast location. The upscaling of the chamber N2O fluxes was performed by totaling the flux contributions from the individual chambers weighted toward the source area share associated with their field locations. The cumulative chamber N2O emissions for both treatments were then compared with the measured EC emissions.

MATERIALS AND METHODS

Site Description

The experimental site was located at the Cornell University Animal Science Teaching and Research Center, Harford, NY (42°26′ N, 76°15′ W, elevation 384 m). The Teaching and Research Center is a large dairy farm with >500 ha of cropland under silage corn and alfalfa, which are typical dairy farm crops grown in the state of New York. The landscape consists of uplands cut by valleys from north to south, with elevations ranging from 360 m on the valley

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floor to 520 m in the uplands. The groundwater table intersects the ground surface near the 370-m contour line; the watershed is characterized by low moisture storage in the upland soils, with intermittent streams that have maximum flow during spring snowmelt and often dry out in summer. The annual 30-yr average temperature and rainfall for the area are 7.8°C and 932 mm, respectively. The soils on the research sites are a well-drained Howard gravelly loam (a loamy-skeletal, mixed, active, mesic Glossic Hapludalf) with 12% clay, 45% sand, 43% silt, and 4% organic matter; the slope across the monitored fields is ?1.2%.

The EC and chamber measurement campaign was conducted in June and July 2008 on adjacent corn and alfalfa fields. Corn was planted on the entire EC footprint area in 2006 and 2007, but in 2008 approximately half of the area was rotated to alfalfa while the balance of the field remained in corn. The EC setup was located between the alfalfa (24.8 ha, 620 by 400 m) and corn (29.7 ha, 350 by 850 m) fields (Fig. 1). The fields were fertilized with dairy manure broadcast without immediate incorporation. In 2008, manure applications took place daily in January to April for the alfalfa field and in May for the corn field, with total loadings of 750 kg N ha−1 for alfalfa and 125 kg N ha−1 for corn. The fields were tilled (moldboard plowed for corn, chisel plowed for alfalfa) in early spring before planting. All field treatments, including manure applications and harvesting, were actual dairy farm operations not controlled by the research group.

Eddy Covariance Instrumentation and Analysis

The EC flux was calculated as the mean product of the instantaneous vertical wind speed and the gas concentration (Fowler, 1999; Laville et al., 1999; Pattey et al., 2006). A three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Logan, UT) and TDLAS analyzer (TGA100A, Campbell Scientific) were used to measure the wind speed and N2O air concentration, respectively.

The air sampling inlets and three-dimensional sonic sensors were installed at a permanent height of 3.5 m (corn height was 2.0–2.2 m maximum). The sonic axis of the sonic anemometer was oriented along the prevailing wind direction in the area and coincided with the alfalfa and corn field dividing path (285° azimuth angle) (Fig. 1). The TGA100A analyzer was located on the ground at the base of the measurement mast to minimize the length of sample tubing (3.2-mm i.d., ?4-m length). The analyzer was inside an insulated box where the constant temperature was maintained by a heater and two fans. Sample air was drawn through the TGA’s sample cell under 5 to 5.5 kPa pressure with a rotary vane vacuum pump (Model RB0021, Busch USA, Virginia Beach, VA) installed ?70 m downwind from the mast location. Sample air moisture was removed via purge flow through a diffusive dryer (PD1000, Perma Pure, Toms River, NJ) installed between the sample intake and the TGA, and particulates were removed with a disposable 10.0-µm polypropylene filter on the dryer’s inlet (changed weekly). The total air flow rate was 18 L min−1 and the purge flow rate was 3 L min−1, leaving a flow of 15 L min−1 through the analyzer. The lag time of 0.7 s for air traveling between the sample intake and N2O detection was calculated to synchronize the N2O concentration and wind speed time series. The N2O signal was measured at a 2205 cm−1 laser absorption line and 723 mA laser direct current. A certified standard reference gas (2000 mL L−1 N2O in N2, Airgas East, Salem, NH) with a flow rate of 10 cm3 min−1 simultaneously passed through the reference cell for continuous calibration. The laser was cooled by liquid N2 (refilled every 6 d) to an operating temperature of 88.6 K. The

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measurement frequency was 10 Hz, with half-hourly fluxes calculated from the high-frequency data. All data were collected using a Model CR5000 datalogger (Campbell Scientific) and stored and transferred with a standard compact flash card. The electricity was provided for the system by a power line with outlets located in the machinery warehouse downwind from the mast.

The detection limit of the EC measurements was calculated from the standard deviation of the vertical speed and the noise level of the TDLAS system (Pihlatie et al., 2005). For a 30-min averaging period with 10-Hz measurement frequency, the method’s lowest detection limit was 0.05 µg N2O-N m−2 min−1. Quality control was performed on the half-hour data before the covariance calculations. The data with friction wind velocity ≤0.1 m s−1 and horizontal wind speed ≤1.5 m s−1 were discarded to exclude measurements occurring when turbulent mixing was not sufficient (Laville et al., 1999). Covariances were rotated to a natural coordinate system. The fluxes were averaged over half-hour and daily periods. The minimum threshold for averaging half-hour to daily fluxes was 25% or 12 half-hour data points per day. All eddy covariance flux calculations and data processing were performed using Matlab, version 7.1 (The MathWorks, Natick, MA).

Static Chamber Instrumentation and Analysis

Easily constructed and inexpensive chambers were composed of two parts: an opaque cylindrical collar (30-cm diameter) made from the upper 17 cm of standard 5-gallon (19-L) plastic buckets, which was designed to be installed (wide end down) in the soil, and a removable cover that fit over the collar and which consisted of a standard opaque 3.5-gallon (13.2-L) plastic bucket (Paragon Manufacturing, Melrose Park, IL) fitted with sampling and vent ports. A large rubber band (size 12G, 30.5 cm long by 5 cm wide flat dimensions, Dykema Co., McKees Rocks, PA) was stretched around the outside of the upper portion of the collar to provide a gas-tight seal between the collar and cover. Each cover top was equipped with a rubber serum bottle septum (to allow insertion of a gas sampling syringe) inserted in the center of the cover. A pressure equilibration vent tube (dimensions calculated from Hutchinson and Mosier [1981] equations) was inserted through a second septum 10 cm from the gas sampling septum. The vent tube consisted of an aluminum pipe (1.1-cm o.d. by 5.0-cm length) fitted (on the inside of the cover) with an 18.5-cm length of 0.45-cm i.d. flexible plastic tubing. Leak tests were performed for each collar–cover pair before field installation using a gas leak detector (Model 21-250, Gow Mac Instrument Co., Bethlehem, PA). Covers were installed on the collars only during deployment to minimize potential effects on the soil. The coverage area and enclosed volume of the closed chamber were 0.07 m2 and 0.017 m3, respectively.

The chamber N2O measurements included three sampling deployments each day at 1100, 1230, and 1400 h. Twenty-eight static closed chambers were installed along a transect linking the adjacent alfalfa and corn fields, with two rows of seven chambers in each crop field (Fig. 1). The distance was 10 m between chambers along the transect and 0.5 m between the rows. The major focus of the experimental design was on time synchronizing the half-hour EC fluxes and chamber data, and this chamber setup facilitated simultaneous sample withdrawal by fewer operators (each operator sampled four chambers within 2 min). The N2O fluxes were calculated for each deployment (at 1100, 1230, and 1400 h) and each treatment (14 chamber replicates).

Collars were inserted 5 cm into the ground 1 wk before the deployment. Field deployment time for each chamber cover was 30 min, with four air samples taken at times 0, 10, 20, and 30

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min from the moment of closing. The duration of deployment and frequency of sampling were determined from the chamber design and dimensions based the closed static chamber recommendations of Rochette and Eriksen-Hamel (2008). For alfalfa measurements, plants were present under the enclosure, whereas, due to crop height, chambers in the corn field were installed between the crop rows. Considering the narrow row spacing (50–60 cm), well-established root coverage by this point in the growing season, and overabundance of manure applied, the chambers covered a lot of treatment-affected soils even with the interrow setup.

Air samples (10 mL) were withdrawn through the septum with gas-tight glass chromatographic syringes (Hamilton Co., Reno, NV) and placed in previously evacuated (residual pressure

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resulting in 12 time points of simultaneous parallel measurements. With the major focus on differences in spatial resolution between the two methods, the data were sufficient for the immediate research goal of the study. This assumption was based on a previous study, which suggested that peak events were unlikely to occur during the study period (Molodovskaya et al., in prep).

Data Comparison: Downscaling Eddy Covariance Fluxes and Upscaling Chamber Fluxes

To compare the two methods, the N2O flux data needed to be scale synchronized. Two approaches were used for the synchronization. First, the EC N2O fluxes were downscaled (partitioned) to the field or treatment scale. Wind directions were used to identify the geographic origin of the flux and associate it with a particular treatment. The wind direction was calculated as the angle of a resulting vector of the three-dimensional sonic wind speed (Ux, Uy, Uz), and therefore was relatively independent of the EC flux, which was calculated using only the vertical wind speed component. The data control removed fluxes associated with wind directions ≥120° and ≤−120° from the pointing direction of the sonic anemometer (azimuth of 45and 165°) to eliminate potential disturbances from the equipment shed (Fig. 1) and the mast itself. The remaining half-hour N2O fluxes were divided into two groups by associated wind directions: corn (>285 and

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emissions. Chamber data therefore were related to the EC flux at the mast location through the wind data, which served as a connecting link between the two sets of flux measurements, originally fully independent of each other.

Soil and Weather Parameters

The micrometeorological setup included volumetric soil moisture content and soil temperature measurements. Two soil moisture sensors (CS616 water content reflectometer, Campbell Scientific) were installed at 10 cm below the surface on the undisturbed (i.e., unplowed) part of the ground at ?5 m from the mast. The measurements therefore represented background soil water storage. The reflectometers were field calibrated by standard soil-core gravimetry, with a calibration coefficient determined from a curve fit of known water content and sensor output. Soil temperature was monitored by four replicated thermocouple probes also installed at 10 cm below the ground surface. Weather parameters included air temperature and humidity (HMP45A/D, Vaisala, Helsinki, Finland) and precipitation (tipping bucket rain gauge). All data were collected at a 10-Hz frequency and averaged for half-hour periods (except for precipitation). Precipitation data were summed for the same time periods. Manual soil thermometers were inserted into the ground inside and outside the chambers to monitor the temperature change during the deployments.

Soil samples for mineral N content analysis were taken from five near-chamber locations in each field using an aluminum soil coring tool (10-cm length, 5-cm diameter) 24 h before the chamber campaign. Soil NO2–N and NO3–N were determined by the sulfanilamide method with a continuous-flow spectrophotometer (Astoria Pacific, Clackamas, OR). The soil NH4–N content was analyzed fluorometrically using the o-phthalaldehyde method. Gravimetric moisture content and bulk density were determined by drying soil core samples for 24 h at 105°C.

Statistical Analysis

Both EC and chamber fluxes were tested for normality with the Kolmogorov–Smirnov test (P = 0.05), and the statistical differences between treatments and methods were tested by a nonparametric Mann–Whitney rank sum test (SigmaStat 3.1, Systat Software, Chicago). Descriptive statistical parameters (mean, standard deviation, maximum and minimum values, and coefficients of variation) for two sets of N2O flux measurements were calculated.

RESULTS AND DISCUSSION

Soil and Weather Parameters The campaign period was characterized by dry, warm weather. No precipitation events

occurred and the soil moisture content was steadily decreasing from 61 to 56% water-filled pore space. Daily air temperatures varied between 17 and 19°C, close to the 30-yr monthly normal for the area. The soils were generally 2 to 5°C warmer than the air, with similar variations in daily temperatures. The soil temperature variations due to chamber closings did not exceed 0.5°C and were within the measurement variability range (3%). Rapid soil moisture and temperature changes, for example during spring soil thaw or intense rainfall, can trigger an immediate N2O flux response (Wagner-Riddle et al., 2007; Scanlon and Kiely, 2003). In our case, the changes in soil temperature and moisture were not dramatic enough to cause a strong peak event, so the

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emissions measured during the campaign probably represent background levels. The concentration of soil available N was substantially greater for the alfalfa field (76.6 kg total available N ha−1) than for the corn field (22.5 kg total available N ha−1), possibly resulting from the greater manure loading that the alfalfa field received earlier that season.

Wind Directions The wind directions for each half hour of simultaneous chamber and EC measurements are

shown in Fig. 2. The angles varied widely between 168 and 44° (+360°) azimuth on 30 June, covering both fields (71% from alfalfa, 29% from corn), with the shift from alfalfa to corn occurring in the middle of the 3.5-h chamber observation period. The daily resulting vector was 275° azimuth. On 1 July, 95% of the flux came from the alfalfa part of the footprint, and that wind direction (244° resulting vector) remained stable during the whole day and thus no wind data were available for identifying the corn EC flux for that day. On 2 July, the wind directions were again scattered across a larger span of angles (274–13° azimuth), the majority coming from the corn part (81%). On 3 July, the winds were mostly associated with the corn field for the chamber measurement period; however, during the day, they were equally distributed between the two treatments (52% from corn, 48% from alfalfa).

Nitrous Oxide Fluxes Measured by the Eddy Covariance Method

The half-hour EC fluxes varied within the wide range of −10 and 40 µg N2O-N m−2 min−1 (Fig. 3), possibly reflecting uncertainty from single-point measurements and short averaging periods, as previously mentioned (Pihlatie et al., 2005; Kroon et al., 2010). The data eliminated during quality screening were mostly attributed to the nocturnal low-turbulence conditions and winds coming from behind the mast. The number of remaining points varied from 14 to 29 out of 48 possible half-hour fluxes per day. The analysis of normality showed that the EC fluxes were highly skewed and not normally distributed (P < 0.001), with the probability distribution being reverse J-shaped, as often observed for N2O soil fluxes (Yates et al., 2006; Wagner-Riddle et al., 2007). The largest variability in half-hour EC fluxes was observed on the days with the most varied wind directions, in particular on 30 June and 2 July. More stable winds generated less variable half-hour EC fluxes, as observed on 1 and 3 July.

Nitrous Oxide Fluxes Measured by the Chambers The fluxes from the chambers varied within relatively narrow range, from −0.1 to 1.9 and

from −0.6 to 1.6 µg N2O-N m−2 min−1 for alfalfa and corn, respectively (Table 1). The chamber flux variability was considerably lower than that of the half-hour EC data (Table 1), which is in line with some previous studies (Pihlatie et al., 2005; Reth et al., 2005; Neftel et al., 2007; Mammarella et al., 2010). Chamber measurements, which are independent of atmospheric conditions, often show smoother emission trends and do not follow the highly scattered peaks of the EC measurements.

The chamber emission data for both alfalfa and corn were normally distributed (P = 0.113 and 0.090, respectively). For 3 out of 4 d of observations, alfalfa chamber fluxes were significantly greater than those for corn (P = 0.002), with the total alfalfa mean N2O emission rate more than twice that of corn. The chamber fluxes from the corn field were generally low and, in some cases, negative (Fig. 4). Negative fluxes can be attributed to random uncertainties

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or instrumental noise because they are observed at levels very close to method detection limits (Skiba et al., 1996; Merino et al., 2004); however, it can also indicate net N2O uptake, the mechanisms of which are not well understood. Glatzel and Stahr (2001) reported net N2O consumption up to −41.2 µg N2O-N m−2 h−1 for fertilized grasslands, and Li et al. (2008) observed mean negative N2O fluxes of −0.75 mg N2O m−2 h−1 for 4 d in a fertilized corn field. The most common theory explaining net N2O uptake is that, in the absence of other forms of oxidized N in the soil, the microbial denitrifiers consume soil-formed N2O as an electron acceptor, thus reducing N2O to N2 gas and completing the final step of denitrification (Neftel et al., 2007; Chapuis-Lardy et al., 2007).

Downscaling (Partitioning) Nitrous Oxide Eddy Covariance Data to the Treatment Scale To estimate each treatment’s input to the cumulative N2O EC-measured emissions,

partitioning the EC data into the corn and alfalfa inputs was performed using the wind directions as an indicator of flux origin. Table 2 shows the time share and cumulative N2O emissions from each treatment during 24-h averaging periods. Corn-associated N2O EC fluxes were only available for 3 out of 4 d because on 1 July the winds were entirely from the alfalfa field.

Daily N2O rates from the corn field were low, which explains its small contribution to the cumulative integrated flux (Table 2). In contrast, the cumulative alfalfa N2O input was high and exceeded its wind direction time share. Much higher daily N2O emission rates were observed for alfalfa than for corn, possibly due to the higher manure N loads that the alfalfa field received earlier in the growing season.

Comparison of the EC and chamber N2O data from alfalfa and corn is shown in Fig. 4. The largest difference between the daily partitioned EC and chamber N2O fluxes was observed on the days when the wind direction was highly variable and both treatments contributed to the integrated flux at the EC mast location (30 June and 3 July). The best agreement between the methods was documented for the alfalfa field on 1 July, when the wind direction was the least variable and the entire daily EC N2O flux was contributed by that single treatment. The partitioned daily EC means for each treatment were mostly within the chamber’s standard error range, providing evidence that wind direction can be a useful tool for identifying the source area of N2O origin.

Upscaling Chamber Nitrous Oxide Data to the Landscape Scale The Schuepp et al. (1990) model estimates the flux footprint for a given observation height as

a function of atmospheric stability and the distance in the upwind direction between the source and the observation point. The model simplifies the wind processes, but it has been widely used in micrometeorological studies and is a sufficiently reliable and simple tool to predict localized spatial contributions to the integrated flux. Schmid (1997) and Laville et al. (1999), in their comparative study of chamber and micrometeorological N2O fluxes from agricultural fields, suggested that the model can be practically applied to weight the flux of each chamber by the footprint function, therefore linking the emission source strength with its location.

Our study tested the assumption that this approach could be used for upscaling the chamber measurements to the landscape integrated flux at the EC mast location. The measurements were verified by comparisons with the EC emission data. The diagrams showing source area strength

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and related chamber locations for each day of measurements were constructed using the micrometeorological data on wind direction and the percentage of flux density contribution (Fig. 5). The model showed that approximately 80% of the flux originated from within 100 m upwind of the mast, the fetch well covered by the chambers.

The treatment contributions to the integrated N2O emissions were estimated as the total of the individual chamber fluxes multiplied by the footprint percentage associated with the chamber field locations. The flux input of each treatment was therefore weighted toward its footprint share in the cumulative emissions from the entire source area. The calculated chamber total was then compared with the cumulative integrated N2O emissions measured daily by EC. The comparison between the two methods showed trends similar to the first approach (downscaling the EC flux) described above. The maximum difference was observed on 2 July, when the chambers measured 33% greater cumulative N2O emissions than the EC method, and the differences for the remaining days of observation stayed within a 7 to 16% range (Fig. 6). The chamber and EC data were agreed best (7% difference) on 1 July, when winds were coming from a narrow span of angles and the flux footprint included only one contributing treatment (the alfalfa field). These findings are in line with Laville et al. (1999), who reported high sensitivity of the EC integrated flux to changing wind directions. The uncertainty of the EC fluxes strongly increased when the flux footprint included source areas with higher variability. Therefore, the analysis of the wind direction pattern and associated underlying surface could provide important information for flux and footprint interpretation.

The choice of the appropriate N2O flux measurement method in each individual case depends on many factors, including researcher goals, research conditions, and affordability of the equipment, and no method can be ultimately declared as best. Static chambers provide straightforward N2O flux estimates; however, any extrapolations to the larger spatial and temporal scale are generally biased by the small area coverage and low sampling frequency resulting from the labor-intensive nature of the method. In contrast, micrometeorological methods such as eddy covariance are characterized by high data resolution and continuity and a relatively low need for labor, and they are more common for routine N2O flux observations. The fact that these methods integrate the flux from the entire footprint, however, calls their applicability for spatial analysis into question, especially for nonhomogeneous agricultural landscapes. The experiment described here was part of a long-term EC N2O emission monitoring study of an agricultural landscape with rotating crops, focusing mostly on the temporal resolution of the interseasonal N2O emission pattern (Molodovskaya et al., in prep). We suggest an approach that would allow the application of this combination technique in ongoing research: chambers to capture differences due to spatial factors and continuous field-scale monitoring to place those chamber snapshots in the context of the overall temporal emission pattern.

The limitations of this study were related to the short time frame of the parallel chamber and EC campaign and mostly observed background N2O fluxes in the absence of flux-triggering weather events. The comparison at the background level, however, helped to reveal possible source of discrepancies between the two methods, such as scattered wind directions, showing that the suggested approach of synchronizing different-scale measurements can be a promising tool for data cross-validation and reducing the uncertainty in flux estimates. More comparative studies are needed to verify this methodology, including (i) using more complete chamber

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coverage of the flux source areas in treatments, and (ii) capturing flux-triggering weather events to compare the flux response by the two methods.

CONCLUSIONS Two different-scale methods (EC and chambers) commonly used for N2O emission

measurements were compared using two scaling approaches: downscaling the EC flux to the treatment scale and upscaling the chamber flux to the landscape scale. Wind direction and a source-area-weighted model were used to identify the location and strength of the N2O area inputs. Both approaches demonstrated good agreement between the methods. The smallest difference was observed on the day with the least varied wind directions and the lowest variations in half-hour EC fluxes, possibly because the integrated EC flux associated with the uniform wind direction and homogeneous source area was less affected by the overall spatial field variability. Our study shows that wind direction can be a useful tool when analyzing source variability and is crucial for stratification of the flux components. These results, however, need further verification by conducting more extensive field studies with specific attention to the quality of the EC data and higher temporal and spatial resolution of the chamber measurements.

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Fig. 1. Map of the Harford Teaching and Research Center (source: USGS Maps, 1949) and experimental setup of the static chambers (black dots, distance between chambers is shown in meters) and micrometeorological equipment (center of the diagram) in the field. The black square shows the location of the equipment shed in the field. The dashed arrow indicates the prevalent wind direction.

Fig. 2. Half-hour wind directions observed during parallel chamber and eddy covariance N2O flux measurements. The x axis shows wind direction (η) as the angle from the sonic axis of a three-dimensional sonic anemometer (zero) (first row) and azimuth angle (second row). Zero sonic direction (285° azimuth) coincided with the dividing path between the alfalfa and corn fields.

Fig. 3. Half-hour N2O-N fluxes from alfalfa and corn fields measured by the eddy covariance method. Gray bars indicate time periods of parallel chamber deployments. Lines indicate the daily averages for the eddy covariance landscape-integrated flux.

Fig. 4. Nitrous oxide N flux measurements by chambers (open circles) and eddy covariance (closed triangles) from (a) corn and (b) alfalfa as part of the footprint at the Harford Teaching and Research Center. The solid line depicts the eddy covariance daily average and the dashed black line depicts the chamber daily average. Standard errors were calculated for 14 chambers for each treatment.

Fig. 5. Source area strength (% of the flux density) as a function of distance and azimuth angle from the eddy covariance mast (0, 0) for the simultaneous chamber and eddy covariance N2O measurement campaign: (A) 30 June; (B) 1 July; (C) 2 July; (D) 3 July. Chambers (corn, open circles; alfalfa, open triangles) are shown relative to the mast location. The dashed line depicts a dividing path between the alfalfa and corn fields (105 and 285° azimuth angles).

Fig. 6. Comparison between integrated cumulative daily N2O-N emissions calculated from measured eddy covariance (open bars) and calculated chamber fluxes (gray bars). The latter represents the total of the source-area-weighted individual chamber contributions from each treatment.

Table 1. Statistics of N2O flux measurements for the eddy covariance and chamber measurements.

Measurement N2O-N flux

Mean CV Mean SD Min. Max. ———— µg m−2 min−1 ———— % Eddy covariance fluxes, n = 75 1.3 7.0 −10.0 40.0 538 Chamber fluxes Alfalfa, n = 12 1.1 0.7 −0.1 1.9 59 Corn, n = 12 0.5 0.8 −0.6 1.6 160

Table 2. Treatment contributions to the integrated eddy covariance flux based on the wind direction data from the alfalfa and corn fields during the chamber and eddy covariance campaign in 2008.

Crop Daily N2O-N flux Cumulative N2O-N† Area input

30 June 1 July 2 July 3 July 30 June 1 July 2 July 3 July 30 June 1 July 2 July 3 July ——— µg m−2 min−1 ——— ———— mg m−2 ———— ————— % —————

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Alfalfa 3.29 1.37 1.98 1.54 1.86 (96%) 1.34 (100%) 0.50 (67%) 0.46 (156%) 71 95 19 48 Corn 0.14 none 0.96 −0.95 0.07 (4%) none 0.24 (33%) −0.16 29 5 81 52

† Contribution to total in parentheses.

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Figure 1. The map of the Harford T&R Center (source: USGS Maps, 1949) and experimental

setup of the static chambers (black dots, distance between chambers is shown in meters) and

micrometeorological equipment (center of the diagram) in the field. The black square shows the

location of the equipment warehouse in the field. The dashed arrow indicates prevalent wind

direction

0 0.5 1.0 km

285o

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Figure 2. Half-hour wind directions observed during parallel chamber and eddy covariance N2O

flux measurements. The x-axis shows wind directions (η) as angles from the sonic axis of 3D-

sonic anemometer (zero) (first row) and azimuth angles (second row). Zero sonic direction (285o

azimuth) coincided with dividing path between alfalfa and corn fields.

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Figure 3. Half-hour N2O-N fluxes from alfalfa and corn fields measured by the eddy covariance method. Grey bars indicate time

periods of parallel chamber deployments.

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Figure 4. N2O-N fluxes by chambers (open circles) and eddy covariance (closed triangles)

measurements from corn (a) and alfalfa (b) part of the footprint at the Harford T&R Centre.

5

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67

Solid line depicts eddy covariance daily average, and dashed black line depicts chamber daily

average. Standard error is calculated for 14 chambers on each treatment.

6

Corn

Corn

Alfalfa

Corn

Corn

Alfalfa

Alfalfa

Corn

Corn

Alfalfa

Corn

Corn

Figure 5. The source area strength (% of the flux density) as a function of distance and azimuth angle (R,η) from the eddy covariance mast (0, 0) for simultaneous chamber/eddy covariance N2O measurement campaign (A – June 30th; B – July 1st; C- July 2nd; D – July 3rd). Chambers (corn - open circles, alfalfa – open triangles) are shown relative to the mast location. Dashed line depicts a dividing path between alfalfa and corn parts (105 and 285o azimuth angles)

68

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6/30/2008 7/1/2008 7/2/2008 7/3/2008

cum

N2O

-N e

mis

siso

ns, u

g m

-2

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Figure 6. The comparison between integrated cumulative daily N2O-N emissions calculated from

measured eddy covariance (open bars) and calculated chambers (grey bars) fluxes. The latter

represents the total of the “source area weighed” individual chamber contributions from each

treatment.

S10-0415.ce.pdfNitrous Oxide Flux from a Heterogeneous Agricultural Landscape: Analysis of Source Contribution by Eddy Covariance and Static ChambersAbstractMaterials and MethodsSite DescriptionEddy Covariance Instrumentation and AnalysisStatic Chamber Instrumentation and AnalysisData Comparison: Downscaling Eddy Covariance Fluxes and Upscaling Chamber FluxesSoil and Weather ParametersStatistical AnalysisResults and DiscussionSoil and Weather ParametersWind DirectionsNitrous Oxide Fluxes Measured by the Eddy Covariance MethodNitrous Oxide Fluxes Measured by the ChambersDownscaling (Partitioning) Nitrous Oxide Eddy Covariance Data to the Treatment ScaleUpscaling Chamber Nitrous Oxide Data to the Landscape Scale

ConclusionsReferences