GCB Bioenergy (2015), doi: 10.1111/gcbb.12268
Nitrous oxide emissions during establishment of eight
alternative cellulosic bioenergy cropping systems in the
North Central United States
LAWRENCE G. OATES1, DAVID S. DUNCAN1, ILYA GELFAND2,3, NEVILLE MILLAR2,3,
G . P H I L I P R O B E R T S O N 2 , 3 and R A N D A L L D . J A C K S O N 1
1
DOE-Great Lakes Bioenergy Research Center & Department of Agronomy, University of Wisconsin–Madison, Madison, WI
53706, USA, 2DOE-Great Lakes Bioenergy Research Center & W.K. Kellogg Biological Station, Michigan State University,
Hickory Corners, MI 49060, USA, 3Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing,
MI 48824, USA
Abstract
Greenhouse gas (GHG) emissions from soils are a key sustainability metric of cropping systems. During crop
establishment, disruptive land-use change is known to be a critical, but under reported period, for determining
GHG emissions. We measured soil N2O emissions and potential environmental drivers of these fluxes from a
three-year establishment-phase bioenergy cropping systems experiment replicated in southcentral Wisconsin
(ARL) and southwestern Michigan (KBS). Cropping systems treatments were annual monocultures (continuous
corn, corn–soybean–canola rotation), perennial monocultures (switchgrass, miscanthus, and poplar), and perennial polycultures (native grass mixture, early successional community, and restored prairie) all grown using best
management practices specific to the system. Cumulative three-year N2O emissions from annuals were 142%
higher than from perennials, with fertilized perennials 190% higher than unfertilized perennials. Emissions ranged from 3.1 to 19.1 kg N2O-N ha 1 yr 1 for the annuals with continuous corn > corn–soybean–canola rotation
and 1.1 to 6.3 kg N2O-N ha 1 yr 1 for perennials. Nitrous oxide peak fluxes typically were associated with precipitation events that closely followed fertilization. Bayesian modeling of N2O fluxes based on measured environmental factors explained 33% of variability across all systems. Models trained on single systems performed
well in most monocultures (e.g., R2 = 0.52 for poplar) but notably worse in polycultures (e.g., R2 = 0.17 for early
successional, R2 = 0.06 for restored prairie), indicating that simulation models that include N2O emissions
should be parameterized specific to particular plant communities. Our results indicate that perennial bioenergy
crops in their establishment phase emit less N2O than annual crops, especially when not fertilized. These
findings should be considered further alongside yield and other metrics contributing to important ecosystem
services.
Keywords: Bayesian model averaging, cellulosic biofuels, corn, greenhouse gas, miscanthus, poplar, restored prairie,
switchgrass
Received 16 February 2015 and accepted 30 March 2015
Introduction
Nitrous oxide (N2O) is a potent greenhouse gas (GHG)
and the main contribution to radiative forcing in the
atmosphere by agriculture (Robertson et al., 2000). For
first generation biofuels derived from edible oils and
starches, N2O emissions from feedstock production
comprise a substantial proportion of their total carbon
footprint (Gelfand et al., 2011). The development of second-generation biofuels derived from cellulosic materials offers the potential to substantially reduce N2O
Correspondence: Lawrence G. Oates, tel. 608 265 4022,
fax 608 262 5217, e-mail: goates@glbrc.wisc.edu
emissions associated with feedstock production (Sanderson & Adler, 2008; Smith et al., 2013). As bioenergy
cropping system viability is considered, the greenhouse
gas emissions of these systems will be a key component
of sustainability evaluation (Reay et al., 2012).
Nitrous oxide emitted from soils is primarily the product of microbially driven nitrification and denitrification.
These processes are influenced by a broad range of environmental factors including temperature, oxygen availability, rates of microbial activity, and the availability of
nitrogen substrates (Robertson & Groffman, 2015). The
effect of these factors can depend on soil-specific properties (Henault et al., 2005) including the composition of the
microbial community (Cavigelli & Robertson, 2001),
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License,
which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
1
2 L . G . O A T E S et al.
while their relative importance may differ among cropping systems (Dechow & Freibauer, 2011). Numerous
process-based models have been developed in an attempt
to account for these contextual effects within a generalized framework, but they are rarely calibrated to the
actively managed and harvested perennial cropping systems that have emerged as leading candidates for secondgeneration biofuel feedstocks (Chen et al., 2008). More
broadly, the dynamics governing N2O emissions in
perennial cropping systems managed for biofuel feedstock production are poorly represented in the literature
(but see Nikiema et al., 2011, 2012; Palmer et al., 2014).
While systems based on polycultures and perennial
species are anticipated to emit less N2O than conventional agricultural cropping systems, this reduction is
likely to be contingent on previous land-use and conversion methodology, phase and length of establishment,
soil type, and management of inputs and production
processes. Land conversion to cropping systems either
through extensification or intensification of open land
systems such as pasture is known to significantly
increase soil organic carbon (SOC) loss (Adler et al.,
2007; Zenone et al., 2011; Sanford et al., 2012). Vegetation
removal and cultivation may also affect the N cycle,
especially during conversion and establishment (Bouwman et al., 2010; Gelfand et al., 2011; Nikiema et al., 2012;
Ruan & Robertson, 2013) and lead to significant nitrogen
loss through leaching and gaseous emissions (Robertson
et al., 2012; Smith et al., 2013). We must improve our
understanding of N2O emissions of likely biofuel feedstock systems to help ensure that expansion of bioenergy
production generates expected societal benefits (Robertson et al., 2008; Dale et al., 2011, 2014).
We compared the establishment-phase N2O emissions
of annual monocultures of continuous corn and corn–soybean–canola rotations; perennial monocultures of switchgrass, miscanthus, and hybrid poplar; and perennial
polycultures of early successional species, native grasses,
and native prairie species. Our results cover the 2- to 4year period following planting over which many perennial crops attain ‘full capacity’ biomass production
(McLaughlin & Adams Kszos, 2005; Anderson-Teixeira
et al., 2013). Our aims were to (i) provide a direct comparison of the aggregate N2O emissions from a broad range of
feedstock production systems; (ii) characterize the effects
of location and year on N2O emissions from these cropping systems; and (iii) evaluate cropping system impacts
on relationships among N2O fluxes and environmental
factors.
Materials and methods
One study site was located in southwestern Michigan at Michigan State University’s W.K. Kellogg Biological Station (KBS) in
Hickory Corners, MI (42°230 47″ N, 85°220 26″ W and 288 m asl),
and another site was located in southcentral Wisconsin, USA,
at the University of Wisconsin’s Arlington Agricultural
Research Station (ARL) in Arlington, WI (43°170 45″ N, 89°220 48″
W and 315 m asl). Mean annual air temperature at KBS is
9.9 °C, and annual precipitation is 1027 mm (MSCO, 2013).
Soils are well-drained Kalamazoo loam (fine-loamy, mixed,
semi-active, mesic Typic Hapludalfs with soil C < 15 g kg 1,
N ≤ 0.13 g kg 1) developed over glacial outwash (Crum & Collins, 1995). At ARL, mean annual temperature and precipitation
are 6.9 °C and 869 mm, respectively (NWS, 2013). Soils at the
site are classified as Plano silt loam (fine-silty, mixed, superactive, mesic Typic Argiudolls with soil C > 20 g kg 1,
N ≥ 0.19 g kg 1) developed over glacial till (Jokela et al., 2011).
Experiments at both sites were established in spring 2008 in a
randomized complete block design. Ten treatments (eight cropping systems including each phase of the three-phase corn–soybean–canola rotation) were represented in five blocks of
30 9 40 m plots at each location, for a total of 100 plots. The
systems under study were (i) continuous no-till corn (Zea mays
L.); (ii) corn–soybean (Glycine max [L.] Merr.)–canola (Brassica
napus L.) rotation with all 3 phases represented; (iii) monoculture switchgrass (Panicum virgatum L.); (iv) monoculture
miscanthus (Miscanthus x giganteus); (v) hybrid poplar (Populus
nigra x P. maximowiczii ‘NM6’) on a 6-year coppicing rotation;
(vi) a mixture of five native grass species; (vii) a mixture of 18
native prairie species; and (viii) an early successional community defined by the pre-existing seed bank and novel recruitment with no management other than fertilizer application and
harvest. Of note, miscanthus at ARL suffered >95% mortality
over the 2008–2009 winter and was subsequently replanted in
May 2010. With the exception of miscanthus at KBS and the
early successional community at both sites, perennial systems
did not receive N fertilizer in 2009, while poplar at both sites
received N fertilizer in 2010 only (full crop and management
details are given in Table S1).
Estimating nitrous oxide emissions
When soils were consistently >0 °C, N2O fluxes were measured
biweekly with additional sampling to characterize episodic
events (i.e., fertilizer application and precipitation events) using
vented static chambers. All measurements were made between
1000 and 1600 h local time. Cylindrical chamber bases of
28.5 cm diameter were inserted ~5 cm below the soil surface.
With the chamber lid installed, the chamber had an effective
headspace volume of ~10 l (~17 cm height). Lids had a septum
for gas extraction and a 2-mm diameter vent and vent tube to
allow for chamber pressure equilibration. Headspace gas from
within the chambers was extracted immediately following lid
placement with a 30-ml nylon syringe and a 23-gauge needle.
Three subsequent extractions were made at 20-min intervals
over a 60-min period. Glass 5.9-ml Exetainer vials (Labco Limited, Buckinghamshire, UK) were flushed with 20 ml of
extracted sample and then overpressurized with 10 ml of sample to avoid contamination and facilitate analysis. At each sampling event, field standards (1 ppm N2O, 1 ppm CH4, and
400 ppm CO2) and ambient air were loaded into vials to assess
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
N2O FROM CELLULOSIC BIOENERGY SYSTEMS 3
potential sample loss prior to analysis. Sample CO2, N2O, and
CH4 concentrations were determined by gas chromatography
using an infrared gas analyzer (IRGA, LiCor 820, Lincoln, NE,
USA) for CO2, an electron capture detector (micro-ECD, Agilent 7890A GC System, Santa Clara, CA, USA) for N2O, and a
flame ionization detector (FID, Agilent 7890A) for CH4.
Visual inspection of CO2 accumulation curves identified
samples with lost pressure or other measurement problems, for
which fluxes were discarded (~2% of total measured fluxes).
Remaining fluxes were analyzed with the HMR package (v0.3.1,
Pedersen, 2011) in the R statistical environment (v3.0.3, R Core
Team, 2014) to fit gas concentrations against time with a nonlinear model (Hutchinson & Mosier, 1981), a linear regression,
and a null flux based on root mean squared error minimization. Of the 4139 flux estimates, a nonlinear model was used
for the 691 (16.7%) fluxes where the 95% confidence interval
for the nonlinear estimate excluded the corresponding linear
estimate. In all other cases, including the case of a null flux, the
linear flux estimate was used. This estimate was used to calculate the aggregate flux for the day it was sampled (daily flux)
by assuming the estimate was the average flux during that day.
Annual fluxes were calculated by integrating the linear interpolation of daily fluxes over one calendar year (Smith & Dobbie,
2001). Cumulative three-year emissions were then calculated
for each experimental plot by summing the aggregated annual
emissions from the three study years within a plot.
Assessing soil environmental variables
Concurrent with trace gas sampling, soil volumetric water content (VWC) (m3 m 3) and soil temperature (°C) were measured
within 1 m of the chamber with a time domain reflectometer
using 20-cm rods (FieldScout 300, Spectrum Technologies, Inc.,
Plainfield, IL, USA) and a 15-cm soil temperature probe
(Checktemp 1C, Hanna Instruments, Smithfield, RI, USA),
respectively. For analysis, VWC was converted to proportion of
water-filled pore space (WFPS) using the equation:
WFPS ¼
h
TP
ð1Þ
where h is equal to VWC, and TP is total porosity (m3 m 3) calculated using the equation:
Bd
100%
ð2Þ
TP ¼ 1
Pd
where Bd is the bulk density at each site (g cm 3) and Pd is
particle density, assumed to be 2.65 g cm 3 for both sites. Castellano et al. (2010) found WFPS was of limited utility as a predictor of N2O flux across soils with differing textures.
Accordingly, we scaled and centered WFPS at each site to create a new variable (WFPSC) for use in analyses.
In 2009, inorganic soil nitrogen was estimated using ion
resin strips (General Electric, Watertown, MA, USA) placed in
the field for 1-month periods from March to November.
Matched pairs of anion and cation strips were placed at each of
three persistent sampling stations per plot. Each month, all six
strips in each plot were collected from the field, cleaned with
deionized water to remove visible soil and then extracted in
2 M KCl. Extracted strips were regenerated with 0.5 M HCl and
0.5 M NaHCO3 prior to next use. For the years 2010 and 2011,
soil cores to 15 cm depth were taken concurrently with N2O
measurements to estimate soil inorganic nitrogen (N) pools. A
10 g wet-weight subsample was weighed out for immediate
inorganic N extraction in 2 M KCl following Robertson et al.
(1999). Potassium chloride extracts were stored in 20-mL polyethylene scintillation vials frozen at 20 °C prior to analysis.
Colorimetric determination of extracts for ammonium (NH4⁺)
(USEPA method-Pub# 27200110) and nitrate (NO3 ) (USEPA
method-Pub# 27190110) was performed on a Flow Solution
3100 segmented flow injection analyzer (OI Analytical, College
Station, TX, USA).
Data analysis
Emissions were analyzed with linear mixed-effect models using
the NLME package (v3.1, Pinheiro et al., 2013) in the R statistical
environment (v3.1.1, R Core Team, 2014). Nitrous oxide emissions were summed over three calendar years; sums were
log-transformed prior to analysis to approximate normally distributed data. Models were constructed to analyze response
variables as a function of the fixed effects of treatment (cropping
system) and site, accounting for the random effect of block
nested within site (site/block). Models were improved by allowing for distinct variances among cropping systems, sites, or both,
and evaluated with likelihood ratio tests. With the variance
structure in place, significant fixed effects and interactions were
determined by sequentially collapsing treatment levels and
comparing subsequent models with likelihood ratio tests. This
process continued iteratively until none of the remaining
groups could be collapsed. Annual emissions considered year
as an additional potential component of the variance structure
and used crop rather than cropping system as a factor and each
combination of year and site was analyzed separately. Variance
structure optimization and assessment of treatment differences
were conducted as described above.
Bayesian model averaging (BMA) was used to evaluate relationships between measured environmental variables and daily
N2O fluxes for cropping system level (Hoeting et al., 1999; Maroja et al., 2009). We conducted the model averaging process
using the bic.glm function of the R package BMA (v 3.16.2.2,
Raftery et al., 2013). Because N2O fluxes are seldom normally
distributed, and negative fluxes are biologically relevant (Schlesinger, 2013), we used a hyperbolic arcsine transformation
(Burbidge et al., 2013) on daily flux data prior to analysis. The
maximal model was defined as soil temperature, WFPSC,
NO3 NH4+, year, and site, as well as all second-order interactions among these terms. Because we used a different method
for estimating inorganic N in 2009, we only used 2010 and 2011
data. Of the 2657 data points in these two years, 2176 (82%)
included all environmental measures. Annual emissions recalculated from this subset were highly correlated to those
obtained from the full dataset (R2 = 0.90), although the range
of values was slightly greater. We trained the model on the full
dataset and also subsets of the data by cropping system to generate system-specific models. We then used environmental data
from the full dataset to evaluate the capacity of models trained
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
4 L . G . O A T E S et al.
from a given system to predict emissions from other systems
or from the full dataset.
Statistical analyses are discussed in greater detail in
Appendix S1.
Annual nitrous oxide emissions by year and site
We analyzed annual N2O emissions separately by year
and site (Fig. 2) due to a significant site 9 year 9 cropping
system interaction (P < 0.001). For 2009 treatment comparisons, model selection indicated that continuous corn and
miscanthus at KBS were not significantly different and
had the highest emissions, followed by the rotational
phase of corn and poplar; all other systems had emissions
that were not significantly different from each other
(Fig. 2a). The treatments responded differently at ARL
where the corn phase of the rotation had more than twice
the emissions of continuous corn. After continuous corn
and the corn phase of the rotation, there were no differences among other systems with the exception of restored
prairie, which had the lowest emissions (Fig. 2b).
The general patterns of emissions from treatments in
2010 were similar at both sites. Continuous corn and the
corn phase of the rotation had the highest emissions at
both KBS and ARL (Fig. 2c,d). Most systems at KBS had
relatively low emissions (Fig. 2c), similar to emissions
from native grass mix and restored prairie at ARL
(Fig. 2d). With the exception of continuous corn, which
was 21% higher at KBS, average emissions at ARL were
12% higher from annual systems, and 68% higher from
perennial systems than the respective treatments at
KBS.
Results
Cumulative nitrous oxide emissions over the 3-year
establishment phase
Cropping systems at the Wisconsin research station
(ARL) emitted 23% more N2O than their Michigan
(KBS) counterparts. The only systems for which this pattern did not hold were continuous corn and miscanthus,
where emissions were not significantly different
between the sites, and the native grasses, where KBS
had higher cumulative emissions (Fig. 1). Note that
while we did not have miscanthus data from ARL in
2009 because of winter kill, cumulative 2010–2011 emissions were similar at both sites. Across both sites, N2O
emissions relative to aboveground yield of continuous
corn were slightly greater than the rotation (0.88),
switchgrass (0.67), and early successional community
(0.79). The emissions relative to yield in native grasses
were just under half that of continuous corn (0.43),
while the restored prairie (0.18), miscanthus (0.15), and
poplar (0.14) were the lowest.
25
Site
ARL
20
KBS
a
N2O-N (kg ha–1)
Rotation phase
Corn
a
15
Canola
a
Soybean
a
10
b
b
c
5
c
d
d
c
c
c
d d
c
d
d
e
0
Co
nti
n
u
uo
sc
orn
Ph
as
r
e1
ota
tio
Ph
n
as
r
e2
ota
tio
Ph
n
as
r
e3
ota
tio
n
S
c
wit
hg
ras
s
n
sca
Mi
thu
s
s
se
ras
g
ive
Po
t
Na
Ea
pla
rly
e
r
su
ion
ss
e
cc
Re
al
st
d
ore
pra
f
irie
Fig. 1 Cumulative 2009–2011 N2O fluxes from 8 bioenergy cropping systems grown at Arlington Agricultural Research Station, WI
(ARL) and Kellogg Biological Station, MI (KBS). Values presented are geometric means with n = 5 at ARL and n = 4 at KBS. Phases
of a corn–soybean–canola rotation are separated by the contribution of each specific rotation phase in ascending chronological order;
analysis was conducted on the summed fluxes. Bars sharing a letter can be grouped during stepwise factor level collapse without significantly reducing the explanatory power of the model (P > 0.05). Miscanthus bars and letters correspond to 2010–2011 fluxes; for
KBS, the dotted line and corresponding letter show the contribution of 2009 flux.
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
N2O FROM CELLULOSIC BIOENERGY SYSTEMS 5
strips, were similar at both sites although the range of
values for both species observed at ARL was greater.
Extractable values of NH4+ were very similar at KBS
and ARL for both 2010 and 2011, but the range of values
was again greater at ARL
for extractable NO3
(Table 1).
The timings of precipitation events, fertilizer applications, and N2O flux measurements varied among sites
and years, with sharp increases in daily N2O fluxes
tending to occur when these events synchronized
(Fig. 3). In 2011 at KBS, for instance, fertilization of most
perennial crops was followed in rapid succession by a
10-mm precipitation event and a very large N2O flux
(Fig. 3f); aggregate annual emissions from perennial
systems were very high at KBS that year (Fig. 2e). During the late spring and early summer, systems receiving
no N fertilization tended to show limited changes in
their emissions. By contrast, fluxes from corn systems
varied over several orders of magnitude. N fertilization
in corn (blue arrows, Fig. 3) preceded a sharp increase
in N2O emission; the sole exception occurred in 2011 at
ARL, when there were no precipitation events between
fertilization and the next flux measurement (Fig. 3e).
The canola rotational phase and most perennial systems
Emissions in 2011 differed from the patterns observed
in previous years. At KBS, the highest emissions were
from the native grasses, followed by continuous corn,
which grouped with the switchgrass, miscanthus, and
early successional community systems. Poplar, restored
prairie, and the soybean phase of the rotation
responded with the lowest emissions (Fig. 2e). At ARL,
the switchgrass monoculture deviated from prior patterns and together with continuous corn averaged 34%
higher emissions than the average across all phases of
the rotation, 54% higher than miscanthus, poplar, and
early successional community, and 82% higher than the
average of the native grass mix and restored prairie systems (Fig. 2f).
Environmental predictors of daily nitrous oxide flux
15
a
a
3
b
b
a
c
c
c
c
b
c
c
2010
(c)
12
c
a
c
c
b
b
c
c
c
d
(d)
a
a
9
b
6
3
c
15
c
d
d
d
d
d
c
d
2011
(e)
12
d
d
c
d
e
(f)
9
6
a
b
3
c
b
a
b
a
b
d
e
e
b
b
c
d
c
c
d
ot
co
rn
.c
ot
or
R . ca n
ot
. s nol
o a
Sw ybe
itc an
h
M gra
N isca ss
at
iv nth
e
gr us
as
Ea
se
rly
s
su Po
pl
c
ce ar
R
es
ss
t
i
C ore ona
on d
l
p
tin
r
uo air
us ie
c
R orn
o
R t. c
ot
or
R . ca n
ot
n
. s ol
o a
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itc an
h
M gra
N isca ss
at
iv nth
e
gr us
as
Ea
se
rly
s
su Po
pl
c
ce ar
R
es
s
to sio
re
na
d
pr l
ai
rie
c
us
uo
b
R
N2O-N (kg ha –1 yr –1)
N2O-N (kg ha–1 yr –1)
ARL
(b)
6
tin
on
2009
9
15
C
KBS
(a)
12
R
N2O-N (kg ha–1 yr –1)
ARL and KBS differed substantially in their soil moisture; water-filled pore space (WFPS) values at ARL were
almost universally higher than at KBS (Table 1). Nevertheless, seasonal patterns within sites were largely similar, with reduced summer WFPS in 2009 and 2011, but
sustained WFPS during the wet summer in 2010 (Fig.
S1). Median NO3 and NH4+, as measured by resin
Fig. 2 Geometric mean of annual nitrous oxide emissions from the Biofuels Cropping System Experiment for the periods of 2009,
2010, and 2011. Left panels (a, c, and e) show results from KBS, n = 4, and right panels (b, d and f) show results from ARL, n = 5. See
Fig. 1 legend for further information.
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
6 L . G . O A T E S et al.
Table 1 Environmental factors observed at the GLBRC Bioenergy Cropping System Experiment colocated in Michigan (KBS) and
Wisconsin (ARL)
NH4+
Year
Site
Soil temp (°C)
WFPS (%)
2009
KBS
ARL
KBS
ARL
KBS
ARL
12.5 (3.0–22.0)
13.7 (1.1–23.3)
14.0 (5.4–21.0)
15.5 (2.9–22.8)
15.0 (4.0–23.5)
15.0 ( 0.4–25.0)
20 (7–29)
71 (40–89)
20 (13–27)
72 (53–93)
22 (9–31)
67 (37–83)
2010
2011
NO3
Resin strip
(lg N cm 2 day 1)
Soil pool
(lg N g 1 soil)
0.010 (0.002–0.071)
0.008 (0.001–0.149)
Resin strip
(lg N cm 2 day 1)
Soil pool
(lg N g 1 soil)
0.38 (0.03–3.68)
0.39 (0.04–5.59)
2.9 (0.7–12.6)
3.7 (1.9–17.4)
1.7 (0.7–7.7)
1.7 (0.9–7.7)
2.6 (1.5–5.3)
5.3 (0.9–56.3)
2.3 (1.3–4.4)
2.7 (0.4–25.2)
Median values are presented, with 5th and 95th percentile values in parentheses. Sites were Arlington Agricultural Research Station,
WI (ARL) and Kellogg Biological Research Station, MI (KBS). WFPS is water-filled pore space.
(a)
(b)
100
50
Group A
Group B
Unfertilized
80
60
10
5
40
1
20
Precipitation (mm day–1)
N2O flux (g N ha–1 day–1)
500
80
60
10
5
40
1
20
500
N2O flux (g N ha–1 day–1)
(d)
(e)
(f)
100
50
80
60
10
5
40
1
20
May-1 May-15
Jun-1 Jun-15
Jul-1
May-15
Jun-1 Jun-15
Jul-1
Precipitation (mm day–1)
(c)
100
50
Precipitatioaan (mm day–1)
N2O flux (g N ha–1 day–1)
500
Jul-15
Fig. 3 Average daily precipitation and nitrous oxide flux during late spring/early summer. Data presented separately by site and
year: (a) 2009 ARL, (b) 2009 KBS, (c) 2010 ARL, (d) 2010 KBS, (e) 2011 ARL, and (f) 2011 KBS. All treatments within a group were fertilized at the same time, denoted by arrows. Group A (gray arrow) consisted of rotational canola, early successional community,
switchgrass (post-2009), native grasses (post-2009), poplar (2010 only), and miscanthus (2010 at KBS, 2011 both sites). Group B (black
arrow) consisted of continuous and rotational corn and miscanthus (2009 at KBS). Unfertilized treatments included rotational soybeans, restored prairie, poplar (except 2010), switchgrass (2009 only), native grasses (2009 only), and miscanthus (2010 at ARL). Data
are presented with inverse hyperbolic sine scaling, with error bars 1 SE.
were fertilized earlier in the year and at a lower rate
than corn (Table S1; gray arrows, Fig. 3). The range of
emissions from these systems tended to be lower
than for corn, as were the emission increases following fertilization events. Our data are too limited to
broadly infer a generalized relationship between precipitation amounts and subsequent N2O fluxes, but
we note that in all cases where N2O flux increased
sharply after fertilization there was at least one
precipitation event of 10 mm or more between
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
N2O FROM CELLULOSIC BIOENERGY SYSTEMS 7
fertilization and flux measurement, whereas among
the cases where fertilization was not followed by a
large flux increase, there was only one instance
where a precipitation event of more than 10 mm
was observed prior to flux measurement (Fig. 3a,
Group A).
Bayesian model averaging of environmental predictors
Models trained on specific systems varied greatly in
their capacity to explain variation in both their training data and the full data (Table 2). Specifically, poplar, miscanthus, and systems containing corn were
relatively well modeled while the polycultures (native
grasses, early successional community, and restored
prairie) were not. There were no environmental factors that substantially contributed to model fits across
all systems (Table S2). There were also substantial
differences in how well models trained on a given
system predicted fluxes from other systems (Table
S3). This relationship was not reciprocal. For example,
the model based on data from native grass system
predicted fluxes from poplar system better than the
model based on poplar data predicted fluxes from
native grasses.
We tested how effectively models trained on specific
systems captured the temporal dynamics of fluxes from
their own and other systems (Fig. 4). Flux dynamics at
ARL were better modeled than those from KBS, where
the models failed to predict major emission events
(Fig. 4d–f). Despite the high correlation between predictions from the corn and native grass-based models
(R2 = 0.58), actual values from the native grass model
were systematically lower than those from the corn
model. The switchgrass-based model was inconsistent
in its relationship to the other two models, sometimes
tracking the corn model (Fig. 4b,d) and at other times
the native grass model (Fig. 4f). Overall, each model’s
performance was poor on systems other than the one on
which it had been trained.
Table 2 Bayesian model averaged posterior probabilities of inclusion for environmental factors used to predict N2O fluxes
Training dataset
Factor
Full data
Corn
Miscanthus
Native
grasses
Early successional
community
Poplar
Restored
Prairie
Switchgrass
Site
Year
NH4+
NO3
Soil temperature
(ST)
WFPSC
Site 9 Year
Site 9 NH4+
Site 9 NO3
Site 9 ST
Site 9 WFPSC
Year 9 NH4+
Year 9 NO3
Year 9 ST
Year 9 WFPSC
NH4+ 9 NO3
NH4+ 9 ST
NH4+ 9 WFPSC
NO3 9 ST
NO3 9 WFPSC
ST 9 WFPSC
Training R2
Full data R2
1.00
0.61
0.81
0.95
0.00
1.00
0.23
0.06
0.05
0.27
0.53
0.01
0.13
0.12
0.23
0.60
0.05
0.01
0.54
0.24
0.25
0.03
0.04
0.53
0.04
1.00
0.02
0.09
0.04
0.04
0.03
0.19
0.05
0.12
0.01
0.17
0.12
0.90
0.04
0.14
0.03
0.00
1.00
1.00
1.00
0.00
0.68
1.00
0.00
1.00
0.00
1.00
0.06
0.08
1.00
1.00
0.33
0.33
0.02
0.02
0.29
0.02
1.00
0.57
0.01
0.03
0.19
0.01
0.09
0.03
1.00
0.76
0.01
0.03
0.46
0.22
0.04
0.05
0.05
0.04
0.55
0.02
0.01
0.02
0.02
0.50
0.08
0.07
0.01
0.86
0.75
0.99
0.39
0.27
0.12
0.82
0.01
0.32
0.61
0.01
0.08
0.01
0.17
0.05
0.02
0.01
0.01
0.42
0.06
0.95
0.21
0.24
0.07
0.03
0.02
0.78
1.00
0.38
0.03
0.75
0.03
0.09
0.16
0.12
0.02
0.43
0.04
0.09
0.17
0.07
0.02
0.01
0.04
0.01
0.01
0.21
0.05
0.10
0.02
0.69
0.02
0.23
0.03
0.87
0.01
1.00
0.52
0.22
0.03
0.04
0.18
0.06
0.03
0.07
0.02
0.04
0.02
0.02
0.04
0.03
0.02
0.03
0.04
0.03
0.06
0.02
0.07
0.02
1.00
0.96
0.20
0.05
0.02
0.89
0.07
0.02
0.02
0.90
0.02
0.02
0.02
0.96
0.27
0.20
Models were trained on N2O flux data obtained from individual cropping systems. NH4+ and NO3 concentrations were log-transformed for analysis. The two sites were Arlington Agricultural Research Station (ARL) and Kellogg Biological Research Station (KBS).
WFPSC is water-filled pore space scaled and centered separately for each site. See Table S2 for factor coefficients and probabilities for
all cropping systems.
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
8 L . G . O A T E S et al.
N2O-N
(g ha–1 day–1)
N2O-N
(g ha–1 day–1)
N2O-N
(g ha–1 day–1)
1000
Corn
Switchgrass
Native grasses
Observed
Modeled
(a)
100
(d)
10
1
0
–1
1000
(b)
(e)
(c)
(f)
100
10
1
0
–1
1000
100
10
1
0
–1
05
07 09
2010
11
05
07 09
2011
Sampling date
11
05
07 09
2010
11
05
07 09
2011
11
Sampling date
Fig. 4 Observed and modeled daily N2O fluxes at ARL and KBS. Solid lines indicate mean observed N2O fluxes of: (a) continuous
corn ARL, (b) switchgrass ARL, (c) native grasses ARL, (d) continuous corn KBS, (e) switchgrass KBS, and (f) native grasses KBS. Dotted lines indicate mean daily predictions from models trained on data collected from the three cropping systems. The vertical axis has
inverse hyperbolic sine scaling, which asymptotically approaches logarithmic scaling. Error bars indicate 1 SE. A 90-day period during which no fluxes were measured was removed from the horizontal axis to conserve space.
Discussion
Cropping systems that included corn were consistently
higher in N2O emissions for any given site year, notwithstanding a high degree of interannual variability.
With few exceptions, N2O emissions from fertilized
perennial systems were much lower than annual systems, while those from unfertilized restored prairie
were lowest overall.
Previous work has demonstrated that rate of N fertilizer application has a strong influence on N2O
emissions from both annual and perennial systems
(Dobbie et al., 1999; Millar et al., 2010). The percentage of applied fertilizer emitted as N2O varies with
site conditions and management, but, in general, N2O
emissions are highest where inorganic N is readily
available. This is reflected in emissions from the continuous corn system which received ~ 3x the rate of
N (160 kg ha 1) as the fertilized perennial systems
(56 kg ha 1). Other factors such as crop rotation (i.e.,
crop diversity) can influence N2O emission rates.
Crop rotation is often associated with reduced N
demand which results in reduced N inputs and N2O
emissions (Drury et al., 2008; Osterholz et al., 2014).
The effect of crop rotation in our study was less clear
with cumulative emissions being highly dependent on
climatic conditions during the corn phase, indicating
a strong interaction between crop phase and year.
Cumulative N2O emissions can be driven by shortduration, high-intensity flux events (Molodovskaya
et al., 2012). These events typically occur when coincidence of fertilization and precipitation results in limited soil oxygen and readily available reactive N
(Dobbie & Smith, 2003; Castellano et al., 2010; Matthews et al., 2010). In general, we observed this pattern, where nearly concurrent fertilization and
precipitation events resulted in substantial emissions
spikes; of all the fertilized systems, this effect was
most clearly observed in corn while emissions for
crops grown in the absence of fertilizer were almost
invariant throughout this period at ARL and were
only slightly variable at KBS.
The difference in cumulative N2O emissions between
ARL and KBS is likely driven by soil properties. Soils at
the two sites differed in both their order and texture,
with ARL soils consisting of fine textured mollisols and
KBS a coarser-textured alfisol. Of the two soil orders,
mollisols are typically associated with higher carbon
and nutrient contents, consistent with the higher carbon
and inorganic N values recorded at ARL. Given that
both sites received comparable precipitation, greater
WFPS at ARL appears to stem from greater water-holding capacity emerging from high soil organic matter
and finer soil texture. We thus attribute the consistently
higher emissions at ARL to soil moisture-driven
increases in anaerobic microsite abundance and longevity (Bouwman, 1996) coupled with greater N and
C availability. Our findings are consistent with studies
in both agricultural and wildland ecosystems that
have linked finer soil texture and greater soil carbon
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
N2O FROM CELLULOSIC BIOENERGY SYSTEMS 9
availability to increased N2O emissions (Bouwman
et al., 2002; Stehfest & Bouwman, 2006). While the
restored prairie system was not fertilized at either site,
greater soil C at ARL suggests potential for rapidly mineralizing N, and this coupled with higher WFPS, may
have facilitated the slightly higher fluxes observed at
ARL. In contrast, between-site differences in the poplar
system were at least partially attributable to an infestation of the ARL plots with the fungal leaf pathogen
marssonina (Marssonina populi (Lib.) Magnus). The
infestation peaked in mid-August 2010 leading to complete defoliation by 15 September; this likely reduced
plant N uptake in the only year that N fertilizer was
applied to poplar, leaving more N available for microbial conversion and loss as N2O.
Cropping systems based on perennial species require
multiple years to become fully established (Parrish &
Fike, 2005). This development is most evident in the
delay in attaining maximum yields, but N-cycling processes may also change during this period (Smith et al.,
2013; Lesur et al., 2014). During the period of this study,
N2O emissions per unit aboveground yield were much
lower in the perennial systems. It is likely this ratio will
improve as the systems come into full production phase
and as farmers become more efficient, both in harvest
timing and mechanical efficiency, at harvesting perennial biomass. Perennial systems also produce greater
biomass belowground which over time will likely
improve soil organic matter and site fertility. While our
study was not structured to explicitly explore the effects
of the establishment period, it is still a potentially relevant contextual element for interpreting results from the
perennial cropping systems. Our results were largely
comparable to studies of perennial systems similarly
conducted over the establishment phase (HernandezRamirez et al., 2009; Smith et al., 2013). However, given
the high influence of interannual variability on our
results, it is clear that long-term studies will be required
to bound this variability and determine whether establishment-phase N2O emissions are representative of
established perennial cropping systems.
A consideration for our results is that our study
lacked measurements during winter (December–February), which could have resulted in underestimation of
N2O emissions. Substantial N loss can occur during this
fallow period, especially in conventionally managed
annual row crops when bare soil is subjected to freeze–
thaw events (Johnson et al., 2010). Soil N is susceptible
to denitrification during this period, especially for systems where manure has been applied after the primary
crop has been harvested (Parkin et al., 2006), or where
vegetative cover is not present during winter (McSwiney et al., 2010). However, only our annual cropping
systems had significant bare soil during winter, and as
discussed above, N2O emissions in these systems were
dominated by relatively brief spikes following fertilizer
events. Nitrogen was not applied in the fall to any of
our cropping systems so we expect that winter measurements would have negligible influence on the magnitude and comparison of N2O emissions.
The Bayesian averaged models we analyzed in detail
suggested alternative cropping systems would produce
different N2O fluxes under a given set of environmental
conditions. Our statistical models based on emissions
measured from our various cropping systems had very
distinct parameterizations, even when the systems were
as similar as continuous and rotational corn. Previous
studies have similarly found that key environmental
predictors of N2O emissions vary among systems (Dechow & Freibauer, 2011; Imer et al., 2013). The BMA
based on switchgrass, for instance, frequently predicted
substantially higher emissions than the model based on
native grasses, implying that at a given soil moisture,
soil temperature, and inorganic N concentration, switchgrass and native grasses would have different N2O
emissions. Cropping systems may differ not only in
their effect on environmental parameters (e.g., through
crop species differences in N uptake and water use), but
also in their response to these parameters. The role of
plant community composition and diversity in determining trace gas fluxes from soil has received little
attention (but see Hoeft et al., 2012).
Given our results, there may be a significant gap in
our ability to account for how plant community composition influences the response of N2O fluxes to environmental drivers. Single-species monocultures have
typically been used to model the broader category of
herbaceous biomass crops (Surendran Nair et al., 2012),
with switchgrass used as a model for exploring the
properties and environmental responses of bioenergy
crops (Lewandowski et al., 2003; Tulbure et al., 2012).
The cropping system specificity we observed in the
response of N2O fluxes to environmental parameters
suggests it may be risky to rely on model systems to
predict the behaviors of perennial and polycultural biomass cropping systems, particularly with the potential
for high variability during the establishment phase.
In summary, across years with highly variable climate, N2O emissions were consistently higher from
annual than perennial cropping systems. Under particular conditions, namely rainfall following fertilizer, emissions from corn dwarfed all other systems. N2O
emissions were consistently low for unfertilized
restored prairie harvested for biomass. Perennial cropping systems on highly productive mollisols had higher
N2O emissions than the same systems growing on moderately productive alfisols. Finally, N2O flux responses
to environmental conditions during establishment were
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
10 L . G . O A T E S et al.
not generalizable across cropping systems, indicating
that use of model systems, especially for perennials and
polycultures, should be performed cautiously.
Acknowledgements
We thank S. Hamilton, A. Dean, J. Tesmer, J. Sustachek, N.
Tautges, A. Miller, Z. Andersen, B. Faust, K. Kahmark, S. VanderWulp, and many others for assistance in the field and laboratory. We also thank S. Bohm for providing the FluxQC
framework, S. Sippel for database management, and anonymous reviewers for comments that improved the manuscript.
Funding was provided by the DOE Great Lakes Bioenergy
Research Center (DOE BER Office of Science DE-FC0207ER64494) and the DOE OBP Office of Energy Efficiency and
Renewable Energy (DE-AC05-76RL01830), and at KBS the NSF
Long-term Ecological Research Program and Michigan State
University AgBioResearch.
References
Adler PR, Del Grosso SJ, Parton WJ (2007) Life-cycle assessment of net greenhousegas flux for bioenergy cropping systems. Ecological Applications, 17, 675–691.
Anderson-Teixeira KJ, Masters MD, Black CK, Zeri M, Hussain MZ, Bernacchi CJ,
DeLucia EH (2013) Altered belowground carbon cycling following land-use
change to perennial bioenergy crops. Ecosystems, 16, 508–520.
Bouwman AF (1996) Direct emission of nitrous oxide from agricultural soils. Nutrient Cycling in Agroecosystems, 46, 53–70.
Bouwman AF, Boumans LJM, Batjes NH (2002) Emissions of N2O and NO from fertilized fields: summary of available measurement data. Global Biogeochemical
Cycles, 16, 6-1–6-13.
Bouwman AF, Van Grinsven JJM, Eickhout B (2010) Consequences of the cultivation
of energy crops for the global nitrogen cycle. Ecological Applications, 20, 101–109.
Burbidge JB, Magee L, Robb AL (2013) Alternative transformations to handle
extreme values of the dependent variable. Journal of the American Statistical Association, 83, 123–127.
Castellano MJ, Schmidt JP, Kaye JP, Walker C, Graham CB, Lin H, Dell CJ (2010)
Hydrological and biogeochemical controls on the timing and magnitude of
nitrous oxide flux across an agricultural landscape. Global Change Biology, 16,
2711–2720.
Cavigelli MA, Robertson GP (2001) Role of denitrifier diversity in rates of nitrous
oxide consumption in a terrestrial ecosystem. Soil Biology and Biochemistry, 33,
297–310.
Chen D, Li Y, Grace P, Mosier AR (2008) N2O emissions from agricultural lands: a
synthesis of simulation approaches. Plant and Soil, 309, 169–189.
Crum JR, Collins HP (1995) KBS Soils. Kellogg Biological Station Long-Term Ecological Research, Michigan State University, Hickory Corners, MI. Available at:
http://lter.kbs.msu.edu/research/site-description-and-maps/soil-description
(accessed 19 September 2014).
Dale VH, Kline KL, Wright LL, Perlack RD, Downing M, Graham RL (2011) Interactions among bioenergy feedstock choices, landscape dynamics, and land use.
Ecological Applications, 21, 1039–1054.
Dale BE, Anderson JE, Brown RC et al. (2014) Take a closer look: biofuels can support environmental, economic and social goals. Environmental science and Technology, 48, 7200–7203.
Dechow R, Freibauer A (2011) Assessment of German nitrous oxide emissions using
empirical modelling approaches. Nutrient Cycling in Agroecosystems, 91, 235–254.
Dobbie K, Smith K (2003) Nitrous oxide emission factors for agricultural soils in
Great Britain: the impact of soil water-filled pore space and other controlling variables. Global Change Biology, 9, 204–218.
Dobbie KE, McTaggart IP, Smith KA (1999) Nitrous oxide emissions from intensive
agricultural systems: variations between crops and seasons, key driving variables,
and mean emission factors. Journal of Geophysical Research, 104, 26891.
Drury CF, Yang XM, Reynolds WD, McLaughlin NB (2008) Nitrous oxide and
carbon dioxide emissions from monoculture and rotational cropping of corn,
soybean and winter wheat. Canadian Journal of Soil Science, 88, 163–174.
Gelfand I, Zenone T, Jasrotia P, Chen J, Hamilton SK (2011) Carbon debt of Conservation Reserve Program (CRP) grasslands converted to bioenergy production.
Proceedings of the National Academy of Sciences, 108, 13864–13869.
Henault C, Bizouard F, Laville P, Gabrielle B, Nicoullaud B, Germon JC, Cellier P
(2005) Predicting in situ soil N2O emission using NOE algorithm and soil database. Global Change Biology, 11, 115–127.
Hernandez-Ramirez G, Brouder SM, Smith DR, Van Scoyoc GE (2009) Greenhouse
gas fluxes in an eastern Corn Belt soil: weather, nitrogen source, and rotation.
Journal of Environmental Quality, 38, 841–854.
Hoeft I, Steude K, Wrage N, Veldkamp E (2012) Response of nitrogen oxide emissions to grazer species and plant species composition in temperate agricultural
grassland. Agriculture, Ecosystems and Environment, 151, 34–43.
Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging:
a tutorial. Statistical Science, 14, 382–417.
Hutchinson GL, Mosier AR (1981) Improved soil cover method for field measurement of nitrous oxide fluxes. Soil Science Society of America Journal, 45, 311.
Imer D, Merbold L, Eugster W, Buchmann N (2013) Temporal and spatial variations
of soil CO2, CH4 and N2O fluxes at three differently managed grasslands. Biogeosciences, 10, 5931–5945.
Johnson JMF, Archer D, Barbour N (2010) Greenhouse gas emission from contrasting
management scenarios in the northern Corn Belt. Soil Science Society of America
Journal, 74, 396–406.
Jokela W, Posner J, Hedtcke J, Balser T, Read H (2011) Midwest cropping system effects
on soil properties and on a soil quality index. Agronomy Journal, 103, 1552–1562.
Lesur C, Bazot M, Bio-Beri F, Mary B, Jeuffroy M-H, Loyce C (2014) Assessing
nitrate leaching during the three-first years of Miscanthus 9 giganteus from onfarm measurements and modeling. GCB Bioenergy, 6, 439–449.
Lewandowski I, Scurlock JMO, Lindvall E, Christou M (2003) The development and
current status of perennial rhizomatous grasses as energy crops in the US and
Europe. Biomass and Bioenergy, 25, 335–361.
Maroja LS, Andres JA, Walters JR, Harrison RG (2009) Multiple barriers to gene exchange in
a field cricket hybrid zone. Biological Journal of the Linnean Society, 97, 390–402.
Matthews RA, Chadwick DR, Retter AL, Blackwell MSA, Yamulki S (2010) Nitrous
oxide emissions from small-scale farmland features of UK livestock farming systems. Agriculture, Ecosystems and Environment, 136, 192–198.
McLaughlin SB, Adams Kszos L (2005) Development of switchgrass (Panicum
virgatum) as a bioenergy feedstock in the United States. Biomass and Bioenergy,
28, 515–535.
McSwiney CP, Snapp SS, Gentry LE (2010) Use of N immobilization to tighten the N
cycle in conventional agroecosystems. Ecological Applications, 20, 648–662.
Millar N, Robertson GP, Grace PR, Gehl RJ, Hoben JP (2010) Nitrogen fertilizer management for nitrous oxide (N2O) mitigation in intensive corn (Maize) production:
an emissions reduction protocol for US Midwest agriculture. Mitigation and Adaptation Strategies for Global Change, 15, 185–204.
Molodovskaya M, Singurindy O, Richards BK, Warland J, Johnson MS, Steenhuis TS
(2012) Temporal variability of nitrous oxide from fertilized croplands: hot
moment analysis. Soil Science Society of America Journal, 76, 1728.
MSCO (2013) Michigan State Climatologist’s Office: 27 year summary of annual values for Gull Lake (3504) 1981–2010, Available at: http://climate.geo.msu.edu/climate_mi/stations/3504/1981-2010%20annual%20summary.pdf (accessed 2 April
2014).
Nikiema P, Rothstein DE, Min D-H, Kapp CJ (2011) Nitrogen fertilization of switchgrass increases biomass yield and improves net greenhouse gas balance in northern Michigan, USA. Biomass and Bioenergy, 35, 4356–4367.
Nikiema P, Rothstein DE, Miller RO (2012) Initial greenhouse gas emissions and
nitrogen leaching losses associated with converting pastureland to short-rotation
woody bioenergy crops in northern Michigan, USA. Biomass and Bioenergy, 39,
413–426.
NWS (2013) National Weather Service: Wisconsin 30 year avereage temperature and
precipitation 1981–2010, Available at: www.crh.noaa.gov/images/mkx/climate/
avg_30_year_precip.png and www.crh.noaa.gov/images/mkx/climate/avg_30_
year_temp.png (accessed 2 April 2014).
Osterholz WR, Kucharik CJ, Hedtcke JL, Posner JL (2014) Seasonal nitrous oxide and
methane fluxes from grain- and forage-based production systems in Wisconsin,
USA. Journal of Environment Quality, 43, 1833–1843.
Palmer MM, Forrester JA, Rothstein DE, Mladenoff DJ (2014) Conversion of open lands
to short-rotation woody biomass crops: site variability affects nitrogen cycling and
N2O fluxes in the US Northern Lake States. GCB Bioenergy, 6, 450–464.
Parkin TB, Kaspar TC, Singer JW (2006) Cover crop effects on the fate of N following
soil application of swine manure. Plant and Soil, 289, 141–152.
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
N 2 O F R O M C E L L U L O S I C B I O E N E R G Y S Y S T E M S 11
Parrish DJ, Fike JH (2005) The biology and agronomy of switchgrass for biofuels.
Critical Reviews in Plant Sciences, 24, 423–459.
Smith KA, Dobbie KE (2001) The impact of sampling frequency and sampling times
on chamber-based measurements of N2O emissions from fertilized soils. Global
Pedersen AR (2011) ‘HMR’: Flux estimation with static chamber data. R package version 0.3.1.
Pinheiro J, Bates D, DebRoy S, Sarkar D, The R Core Team (2013) nlme: Linear and
Nonlinear Mixed Effects Models. R package version 3.1.
R Core Team (2014) R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-pro
ject.org/.
Change Biology, 7, 933–945.
Smith CM, David MB, Mitchell CA, Masters MD, Anderson-Teixeira KJ, Bernacchi CJ, DeLucia EH (2013) Reduced nitrogen losses after conversion of row
crop agriculture to perennial biofuel crops. Journal of Environment Quality, 42,
219.
Stehfest E, Bouwman L (2006) N2O and NO emission from agricultural fields and
soils under natural vegetation: summarizing available measurement data and
Raftery AE, Hoeting JA, Volinsky CT, Painter I, Yeung KY, (2013) BMA: Bayesian
Model Averaging. R package version 3.16.2.2. Available: http://cran.r-project.org/
package=BMA (accessed 25 September 2013).
Reay DS, Davidson EA, Smith KA, Smith P, Melillo JM, Dentener F, Crutzen PJ (2012)
Global agriculture and nitrous oxide emissions. Nature Climate Change, 2, 410–416.
Robertson GP, Groffman PM (2015) Nitrogen transformations. In: Soil Soil Microbiol-
modeling of global annual emissions. Nutrient Cycling in Agroecosystems, 74, 207–
228.
Surendran Nair S, Kang S, Zhang X et al. (2012) Bioenergy crop models: descriptions, data requirements, and future challenges. GCB Bioenergy, 4, 620–633.
Tulbure MG, Wimberly MC, Boe A, Owens VN (2012) Climatic and genetic controls
of yields of switchgrass, a model bioenergy species. Agriculture, Ecosystems and
ogy, Ecology and Biochemistry (ed. Paul EA), pp. 421–446. Academic Press, Burlington, MA.
Robertson GP, Coleman DC, Bledsoe CS, Sollins P (eds.) (1999) Standard Soil Methods
for Long-Term Ecological Research, pp. 1–462. Oxford University Press, New York.
Robertson GP, Paul EA, Harwood RR, (2000) Greenhouse gases in intensive agriculture: contributions of individual gases to the radiative forcing of the atmosphere.
Science, 289, 1922–1925.
Environment, 146, 121–129.
Zenone T, Chen J, Deal MW et al. (2011) CO2 fluxes of transitional bioenergy crops:
effect of land conversion during the first year of cultivation. GCB Bioenergy, 3,
401–412.
Robertson GP, Dale VH, Doering OC et al. (2008) Sustainable biofuels redux. Science,
322, 49–50.
Robertson GP, Bruulsema TW, Gehl RJ, Kanter D, Mauzerall DL, Rotz CA, Williams
CO (2012) Nitrogen–climate interactions in US agriculture. Biogeochemistry, 114,
41–70.
Ruan L, Robertson GP (2013) Initial nitrous oxide, carbon dioxide, and methane
costs of converting conservation reserve program grassland to row crops under
no-till vs. conventional tillage. Global Change Biology, 19, 2478–2489.
Sanderson MA, Adler PR (2008) Perennial forages as second generation bioenergy
crops. International Journal of Molecular Sciences, 9, 768–788.
Sanford GR, Posner JL, Jackson RD, Kucharik CJ, Hedtcke JL, Lin T-L (2012) Soil carbon lost from Mollisols of the North Central USA with 20 years of agricultural
best management practices. Agriculture, Ecosystems and Environment, 162, 68–76.
Schlesinger WH (2013) An estimate of the global sink for nitrous oxide in soils. Global Change Biology, 19, 2929–2931.
Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Figure S1. Average soil temperature and water-filled pore
space at ARL and KBS over the study period.
Tables S1–S3. Tables providing agronomic management
details, BMA analysis results for all cropping systems, and
correlations between predicted and observed N2O fluxes
using different cropping systems as training data.
Appendix S1. Detailed description of statistical analyses.
© 2015 The Authors Global Change Biology Bioenergy Published by John Wiley & Sons Ltd., doi: 10.1111/gcbb.12268
Lawrence Oates