Nitrous Oxide from Heterogeneous Agricultural

Soil & Water Management & Conservation

Nitrous Oxide from Heterogeneous Agricultural Landscapes: Source Contribution Analysis by Eddy Covariance and Chambers Marina Molodovskaya* Inst. for Resource, Environment and Sustainability Univ. of British Columbia Vancouver, BC, V6T 1Z4 Canada

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

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

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he 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 chromatography–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 Soil Sci. Soc. Am. J. 75:1829–1838 Posted online 18 July 2011 doi:10.2136/sssaj2010.0415 Received 10 Nov. 2010. *Corresponding author ([email protected]). © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

SSSAJ: Volume 75: Number 5 • September–October 2011

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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 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 1830

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 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 ane-


Fig. 1. Map of the Harford Teaching and Research Center (source: USGS) 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.

mometer (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 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)

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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 min from the moment of closing. The duration of deployment and frequency of sampling were determined from the chamber design and dimensions based on the closed static chamber recommendations of Rochette and EriksenHamel (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 285 and