Introduction
Future energy needs require dedicated biomass crop production for bioethanol
and combustion-based electricity generation. Corn (Zea mays L.)–soybean
(Glycine max L.) rotations currently dominate the landscape across Ontario and the northern
US Corn Belt (Gaudin et al., 2015), and corn grain is
currently the primary feedstock for bioethanol production in Canada
(Jayasundara et al., 2014). The C4 perennial grasses (PGs)
switchgrass (Panicum virgatum L.) and miscanthus (Miscanthus spp.) have been proposed as alternate
feedstock crops to corn for biomass-based bioenergy production due to their
large biomass yields, reduced nitrogen (N) and water requirements, decreased
nutrient leaching, and potential for increased soil carbon (C) storage
(Blanco-Canqui and Lal, 2009; Foster et al., 2013).
Large-scale production of C4 PGs in Ontario and the northern Corn Belt would
require land use change (LUC) from existing corn–soybean rotations to PG
biomass cropping systems (Deen et al., 2011; Kludze et al., 2013; Liang et al.,
2012; Sanscartier et al., 2014).
Few studies have assessed how this LUC may influence soil microbial
community functioning. In particular, soil denitrifying communities
represent an ideal subset of the soil microbial community to target to
assess changes in ecosystem functioning due to agricultural management and
LUC. Denitrifying bacteria represent approximately 5 % of the total soil
microbial biomass (Braker and
Conrad, 2011) and have been identified in over 60 genera
(Philippot, 2006), encompassing a wide range of
phylogenetic and functional diversity. Multiple studies have linked changes
in denitrifier communities with plant types or development stage (Bremer
et al., 2007; Hai et al., 2009; Petersen et al., 2012), N fertilization
(Hallin et al., 2009; Yin et al., 2014),
organic or conventional crop management (Reeve et al.,
2010), perennial vs. annual crop land use (Bissett et
al., 2011), and C and N inputs (Bastian et al., 2009).
These studies suggest that LUC from corn–soybean rotations to PG species may
influence the soil bacterial communities which drive soil N2O
production and consumption.
N2O is a potent greenhouse gas with a global warming potential
296 × that of CO2 (IPCC, 2007). However, measuring N2O directly in the
field is often difficult with chamber methods in cropping systems that
produce large aboveground biomass. Additionally, including multiple field
treatments (e.g. as in a randomized complete block design) makes micrometeorological methods of
N2O flux impossible to obtain. Instead, relative abundances of
denitrifier genes can be used to assess a soil's potential to produce
(e.g. nirS or nirK) and consume (e.g. nosZ) N2O via denitrification, representing a
qualitative proxy of relative N2O emission potential of a soil
(Butterbach-Bahl et al., 2013; Hallin et al., 2009; Morales et al., 2010;
Petersen et al., 2012; Philippot, 2002). Denitrifier community size has been
correlated with denitrification process rates (Hallin et al., 2009; Wu et
al., 2012) and denitrification potential (Attard et al., 2011; Cuhel et
al., 2010; Enwall et al., 2010). Potential denitrifying activity and
denitrifying community size have also been shown to be correlated with each
other in some studies (Hallin et al., 2009; Morales et al., 2010; Szukics et
al., 2010; Throbäck et al., 2007), suggesting community size may indicate
potential differences in soil N processes after LUC. Particularly, the
nosZ-bearing community may act as a N2O sink and counter high N2O
production rates (Braker and Conrad, 2011; Philippot et al., 2011),
therefore influencing N2O emissions (Cuhel et al., 2010; Morales et
al., 2010; Philippot et al., 2011).
Denitrification nirS and nosZ gene targets represent the two most important steps in
the denitrification pathway that produce gaseous by-products, and account
for a large proportion of functional N genes in some studies
(Stone et al., 2015). The first step in denitrification
that produces a gaseous N product is the reduction of nitrite (NO2-) to
nitric oxide (NO), catalyzed by nitrite reductases either encoded by the
cytochrome cd1 (nirS) or copper-containing (nirK) genes, which are equivalent but
have not been detected within the same species
(Zumft, 1997). We chose to quantify nirS because
three-quarters of cultured denitrifiers possess the nirS gene
(Zumft, 1997) and some molecular reports indicate
nirS may dominate in abundance over nirK in some natural environments
(Deslippe et al., 2014; Nogales et al., 2002),
indicating it may be a better-suited target for relative characterization of
potential nitrite-reducing communities than nirK. Additionally, nirK has been
recently identified in autotrophic ammonia-oxidizing species (Cantera
and Stein, 2007; Casciotti and Ward, 2001), calling into question its
utility in specifically targeting denitrifying communities. The nosZ target codes
for nitrous oxide reductase, which catalyzes the reduction of N2O to
N2 in the denitrification pathway, indicating nosZ-bearing communities help
to complete the N cycle and determine the N2O : N2 balance. 16S rRNA
was chosen as a molecular target for the total bacterial community size;
although 16S rRNA gene copies vary from 1 to 15 copies per genome, its use has
continued to be the “gold standard” for microbial ecology
(Case et al., 2007; Vos et al., 2012).
Although an alternate target, such as rpoB, which is a single copy gene
would be valuable if assessing phylogenetic diversity, there are no
universal primers for it (Adékambi et al.,
2009) as rpoB is not conserved enough to be of use as a universal marker and
only a subset of the microbial community can be targeted
(Vos et al., 2012). Many studies have used 16S rRNA gene
copy numbers as a proxy for the total bacterial community size, and some
have found that the total estimated numbers of proteobacteria species was
not significantly different if using rpoB or 16S rRNA markers
(Vos et al., 2012). As this study has not assessed phylogenetic
relationships of the microbial communities, 16S rRNA is an appropriate
target for the relative comparison of the overall bacterial community size
between environmental treatments/variables.
LUC resulting from displacement of corn–soybean rotations by PG production
may alter soil microhabitats and therefore soil microbial N-cycling due both
to extensive root and rhizome biomass and to large leaf litter inputs to
soils in perennial vs. annual systems (Dohleman et
al., 2012). Within studies targeting soil microbial communities in biomass
cropping systems to date (Hedenec
et al., 2014; Liang et al., 2012; Mao et al., 2013, 2011; Orr et al.,
2015), the effects of various management practices (e.g. N fertilization and
harvest) on soil microbial community functioning have not been an area of
focus. The effect this type of LUC may have on soil microbial communities
may depend on PG management practices in these systems.
Currently, there is no consensus regarding optimal N fertilization practices
for increased yields in PG production as yield responses can be highly
variable depending on environmental conditions and crop species
(Deen et al., 2011). Depending on downstream use, miscanthus
can be harvested in the fall pre-frost, harvested post-frost kill, or
left to overwinter as standing biomass for harvest in the spring.
Switchgrass is commonly harvested in the fall, and is often windrowed (cut,
swathed, and left on soil) over winter due to producers' limitations in
collecting and storing harvested biomass in winter
(REAP, 2008; Sokhansanj et al., 2009). Differences in N fertilizer
requirements and harvest regimes may alter C and N inputs
(Attard et al., 2011) and may influence LUC impacts on soil denitrifier community sizes.
Our objective was to compare the effects of LUC from corn–soybean to PG biomass
production on the relative abundances of total (16S rRNA gene
target) and denitrifier (nirS and nosZ gene targets) soil bacterial communities
3–4 years after PG planting. Soil was collected on four dates from 2011 to 2012
from a field trial established in Ontario in 2008. This study is unique in
that it consists of two PG biomass crops produced in parallel with the
existing common land use of corn–soybean rotation within the same field
trial site. It also includes unfertilized and fertilized plots in both
annual and perennial systems, and varied harvest practices within PG plots.
We hypothesized that soils from PG plots would support larger total
bacterial and denitrifier communities than soils from corn–soybean plots due
to increased shoot residue return and root inputs to soils in PG systems,
as well as that soils from PG plots with biomass harvested in the spring would
support larger total bacterial and denitrifier communities than supported by
soils from PGs harvested in the fall due to increased root inputs and leaf
loss to soil over winter.
Mean soil properties measured at the Elora Research Station.
Cropping system/
N rate
Bulk
Yield
harvest
(kg ha-1)
density1
% organic carbon
% total carbon
(dry t ha-1)
(g cm-3)
0–15 cm
15–30 cm
0–15 cm
15–30 cm
2011
2012
Mean2
Corn–soybean
Fall
0
1.21 AB
1.88
1.06
2.22
1.86
5.341
2.912
E
Corn–soybean
Fall
160
1.27 A
1.79
1.47
2.25
2.11
9.92
7.882
BC
Miscanthus
Fall
0
1.10 B
2.06
1.44
2.27
1.72
17.62
12.77
A
Miscanthus
Fall
160
1.10 B
2.13
1.63
2.36
1.84
17.43
18.32
A
Miscanthus
Spring
0
1.13 AB
2.09
1.53
2.31
1.69
12.66
13.38
AB
Miscanthus
Spring
160
1.13 AB
2.24
1.42
2.47
1.89
14.33
14.56
A
Switchgrass
Fall
0
1.11 B
2.12
1.43
2.33
1.61
7.648
6.458
CD
Switchgrass
Fall
160
1.09 B
2.12
1.34
2.32
1.73
11.1
10.45
AB
Switchgrass
Spring
0
1.11 B
2.09
1.23
2.32
1.55
6.33
4.146
DE
Switchgrass
Spring
160
1.21 AB
1.92
1.33
2.23
1.7
6.905
6.441
CD
1 Means of bulk density (n = 6) followed by the same letter within one column
are not significantly different according to a post hoc Tukey's means
comparison (p < 0.05); carbon measurements (n = 3) were not
significantly different between treatments. 2 Mean yields (n = 3)
followed by the same letter are not significantly different according to a
post hoc Tukey's means comparison (p < 0.05).
Mean daily air temperature (∘C) and daily precipitation (mm)
at the Elora Research Station from January 2011 to November 2012. Soil
gravimetric H2O was measured on a per-sample basis and is shown as crop
means (± SE) for each sampling date (9 May 2011, 30 October 2011,
2 May 2012,
and 20 October 2012) (n = 12 in perennial grasses, n = 6 in
corn–soybean rotation).
Materials and methods
Site description and experimental design
A field trial was established in 2008 at the University of Guelph Research
Station in Elora, ON (43∘38′46.73′′ N, 80∘24′6.66′′ W). The field site was cultivated on 16 May and 6 June 2008.
Switchgrass (Panicum virgatum L. “Shelter”) was planted on 6 June 2008.
Miscanthus (M. sinensis × M. sacchariflorus `Nagara', M116) was planted on 12 June 2008,
and soybean (Glycine max L.) was planted on 24 June 2008 and annually rotated
with corn (Zea mays L.). Corn was planted on 5 May 2010, soy was planted on
3 June 2011, and corn was re-planted on 18 May 2012, with
annual light cultivation to prepare seedbeds for planting. In 2007, prior to
trial establishment, the experimental area was planted to barley (Hordeum vulgare L.). The
soil type is a London silt loam (Gray Brown Luvisol).
The field trial was a split–split–strip plot design with three replicates.
The main plot factor was PG crop or annual rotation (miscanthus,
switchgrass, and corn–soybean). Main treatment plots measured 6.2 m × 26.0 m.
Nitrogen fertilizer (0 or 160 kg N ha-1) was applied in strips
randomly within replicates. 160 kg N ha-1 subplots received
hand-broadcast urea fertilizer (46-0-0; N-P-K) in May 2011 or hand-broadcast
ammonium nitrate fertilizer (34-0-0; N-P-K) in May 2012, after soil sampling
procedures described below. Main treatments were split into two harvest
timings (fall or spring) within the PG fertilizer strips only. Miscanthus
plots were either harvested in the late fall season after post-frost kill
or left standing to overwinter until spring harvest. Switchgrass plots were
harvested in the fall or cut and assembled into windrows in the field for
biomass removal in the spring. Spring harvest of PGs occurred before N
fertilizer was applied. Harvest methods of PG yields (dry harvested biomass
(tonnes) ha-1) are described in Deen et al. (2011).
Figure 1 illustrates the seasonal and annual variation in daily average air
temperature (∘C) and daily precipitation (mm) measured at the
Elora Research Station.
Soil sampling and analysis
Baseline bulk density and carbon measurements were measured for each main
plot on 23 October 2010. For bulk density, two soil cores per plot
were collected at 0–5 cm depth using 2.5 cm diameter cylindrical aluminum
cores. Cores were weighed before and after drying for 24 h at 105 ∘C
(Maynard and Curran, 2007). For soil carbon analysis, 10 soil cores per
plot were collected from both 0–15 and 15–30 cm depths using a
5 cm diameter soil corer on a Z-shaped transect, and then composited per
treatment plot for each depth. Total soil carbon and inorganic carbon were
analyzed with a Leco® Carbon Determinator CR-12
(model no. 781-700, Leco Instruments Ltd.) following the dry combustion
technique (Périé and Ouimet, 2008) on approximately 0.300 g of dried,
ground, and homogenized soil (Table 1).
For molecular analyses, soil was sampled on four dates (9 May 2011,
30 October 2011, 2 May 2012, and 20 October 2012).
October sampling dates occurred before fall harvest of PG crops, while May
sampling dates occurred before N fertilizer application and after spring
PG biomass removal (Fig. 1). Ten soil cores per plot were sampled aseptically
to 15 cm depth using a 5 cm diameter soil corer on a Z-shaped transect,
composited and kept on ice until transport back to the laboratory. The
transect shape was chosen to encompass plot heterogeneity; at a pre-trial
study date, initial analysis indicated gene abundances were not significantly
different between bulk or rhizosphere soils in corn–soybean or PG plots,
possibly due to the large root biomass/leaf loss to soils in perennial plots
and residual soy/corn residue cover on soil in corn–soybean plots. Soil
samples were divided for storage at 4 and -20 ∘C.
Mean values of gravimetric soil moisture (g g-1) are shown in Fig. 1.
Soil exchangeable NO3--N and NH4+-N were determined for
each of the soil samples by KCl extraction. Soil samples (10.0 g) were
placed into 125 mL flasks and 100 mL of 2.0 M KCl was added to each flask.
Flasks were stoppered and shaken for 1 h at 160 strokes per minute; solutions
were allowed to settle and were then filtered through Whatman no. 42 filter
paper (Whatman plc, ME, USA). Extractable NO3--N and
NH4+-N were determined colourmetrically with segmented flow
analyses (AA3, SEAL Analytical, Wisconsin, USA) via a cadmium reduction
(US Environmental Protection Agency, 1974) and a Berthelot reaction, respectively (Fig. 2).
Soil DNA extraction
Total DNA was extracted from field-moist soil sampled from each plot
(three field replicates, n = 3; total plots n = 30). DNA was extracted in duplicate
(∼ 0.250 g) within 48 h of sampling as per manufacturer's protocol using
a PowerSoil DNA isolation kit (Mobio, Carlsbad, USA). Duplicate extracts were then
pooled, separated into aliquots, and stored at -80 ∘C until use in
downstream analyses.
Mean soil NH4-N and NO3-N (mg g-1 dry soil
± SE) in annual and perennial biomass cropping systems under varied harvest
and N management at the Elora Research Station. CS: corn–soybean;
SF: fall-harvested switchgrass; SS: spring-harvested switchgrass;
MF: fall-harvested miscanthus; and MS: spring-harvested miscanthus. Different
letters within panels indicate significant differences according to a
post hoc Tukey's test (p < 0.05).
Quantification of total bacteria and functional genes
Quantitative PCR (qPCR) assays were used to enumerate the total bacterial
communities (16S rRNA gene) and communities of denitrifiers by targeting
nitrite reductase (nirS) and nitrous oxide reductase (nosZ) genes, using primer
pairs 338f/518r (16S rRNA; Fierer et al., 2005), Cd3af/R3Cd (nirS;
Throbäck et al., 2004), and 1F/1R (nosZ; Henry et al., 2006).
For each gene target analyzed, duplicate replicates were run in parallel on
an IQ5 thermocycler (Bio-Rad Laboratories, Hercules, CA, USA). qPCR reaction
mixtures contained 12.5 µL of 1 × SYBR Green Supermix, with each forward and
reverse primer at a final concentration of 400 nM; 1 µL of DNA
template; and RNase/DNase-free water to a final volume of 25 µL. The SYBR Green
Supermix contained 100 nM KCl, 40 mM Tris-HCl, 0.4 mM dNTPs, 50 units mL-1
iTaq DNA polymerase, 6 mM MgCl2, SYBR Green 20 nM
fluorescein, and stabilizer (Bio-Rad Laboratories, Hercules, CA, USA).
Conditions for qPCR were an initiation step at 94 ∘C for 2 min, followed by 35 cycles of denaturing at 94 ∘C for 30 s, annealing at 57 ∘C for 30 s (16S rRNA) or at
55 ∘C for 60 s (nirS), followed by elongation at
72 ∘C for 30 (16S rRNA) or sixty (nirS) seconds. For nosZ, a touchdown
protocol adapted from Henry et al. (2006)
was used. Amplicon specificity was screened by running qPCR products on an
ethidium bromide-stained gel (1 % agarose, 80 V for 20 min) with a
100 bp ladder, which resulted in clean bands for all gene targets. The
16S rRNA primers used are degenerate and have been cited as having 89–91 %
matching efficiency to all bacteria (Bergmark et al., 2012). The primers
amplify one of two conserved regions in V3 of the SSU rRNA gene, resulting
in a ∼ 200 bp amplicon that is within small enough to amplify via qPCR
methodology and amplifies for most bacterial taxa (Bakke et al., 2011).
Known template standards were made from cloned PCR products from pure
culture genomic DNA (Clostridium thermocellum (16S), Pseudomonas aeruginosa (nirS), and Pseudomonas fluorescens (nosZ)) and
transformed into Escherichia coli plasmids (TOPO TA
cloning kit); plasmids were sequenced to confirm successful cloning and
transformation of the target genes. Amplicon specificity was screened by
running PCR products on an ethidium bromide-stained gel (1 % agarose,
80 V for 20 min) with a 100 bp ladder. PCR amplicons of cloned gene
targets were sequenced by the Laboratory Services Department at the
University of Guelph using an ABI Prism 3720 (Applied Biosystems, Foster
City, CA, USA) to confirm target identity.
In all qPCR assays, all unknown samples were amplified in parallel with a
triplicate serial dilution (101–108 gene copies per reaction) of
control plasmids. PCR assays were optimized to ensure efficiencies ranging
from 93.0 to 106.4 %, with R2s ranging from 0.990 to 0.999 and standard
curve slopes of -3.177 to -3.408 by testing serial dilutions of DNA extracts
in order to minimize inhibition of amplification due to humic and fulvic
contaminants. Duplicate no-template controls were run for each qPCR assay,
which gave null or negligible values. Melt curve analysis was used to
confirm amplicon specificity. Normalization of DNA concentrations to grams of
dry soil was used to give results on a biologically significant scale, which
assumes similar DNA isolation efficiency across samples.
Statistical analysis
Analysis of variance was conducted in SAS 9.3 (Carlsbad, NC, USA) using a
generalized linear mixed model (PROC GLIMMIX). The Shapiro–Wilks test was
used to test for normality of data; studentized test for residuals confirmed
the absence of outliers. The probability distributions of gene abundance
data sets were log-normal or highly skewed and were analyzed using an
overdispersed Poisson distribution for count data
(Ver Hoef and Boveng, 2007). Bulk density, organic
carbon, total carbon, nitrate, and ammonium data were log-transformed when
required and fitted to the normal distribution.
Within each data set, sampling time was a repeated measure; independent and
interactive fixed effects were associated with crop/crop rotation, nitrogen
application rate, and harvest timing within perennial grasses, while field
replicate and its associated interactions were random effects. The residual
maximum likelihood method was employed to fit the model for all data sets.
Several covariance structures were entertained before the variance
components structure was chosen based on convergence and model fitting
criteria. Individual treatment means within data sets were compared using a
post hoc Tukey's test for all pairwise comparisons, with significance
denoted at p < 0.05.
Correlation analysis was used to assess nonparametric measures of
statistical dependence between gene abundances and H2O,
NO3--N and NH4+-N measured over time (Table S1 in the Supplement).
Correlation analysis resulted in multiple significant correlations
between variables; as such a principal component analysis was conducted in
SAS (PROC FACTOR) on 120 samples using a VARIMAX rotation.
Results
Environmental and soil conditions
Environmental conditions varied during the periods prior to the four soil
sampling dates (Fig. 1). Average air temperatures over the growing seasons
(May–October) were 16.9 and 17.3 ∘C in 2011 and 2012,
respectively (Roy et al., 2014); average air temperatures in spring 2012 were
warmer than normal and resulted in earlier emergence of PG crops compared to 2011.
Cumulative monthly precipitation was above average prior to the May 2011
sampling date (101 mm vs. 72 mm 30-year average in April 2011 and
113 mm vs. 82 mm 30-year average in May 2011) (Roy et al.,
2014). In comparison, southern Ontario received very low cumulative
precipitation in April 2012 (30 mm vs. 72 mm 30-year average) and May 2012
(28 mm vs. 82 mm 30-year average) (Roy et al., 2014). Cumulative
precipitation levels were lower in 2012 compared to 2011 from May to August
(391 mm in 2011 vs. 186 mm in 2012); however, higher than normal
precipitation levels occurred in October of 2011 (129 mm vs. 77 mm 30-year
average) and both September (106 mm vs. 77 mm 30-year average) and October
(127 mm vs. 77 mm 30-year average) of 2012 (Roy et al., 2014). Environmental
conditions prior to soil sampling directly impact soil gravimetric content
measured at the time of sampling (Fig. 1) and could also impact soil N and soil
bacterial communities.
Soil physical and chemical properties were assessed in October 2010, after
only 2 years of contrasting management since crop establishment in 2008.
The corn–soybean rotation had higher soil bulk density than soils from both
miscanthus and switchgrass plots harvested in the fall. No differences in
total or organic soil carbon were detected between the corn–soybean rotation
and the PG treatments at either the 0–15 or 15–30 cm depth (Table 1). Soil
NH4-N levels did not differ significantly between the corn–soybean
rotation and the PG soils; however, N fertilization significantly increased
NH4-N levels in soils from fall-harvested miscanthus plots (p < 0.05)
(Fig. 2a). N fertilization also significantly increased NO3-N
levels in spring-harvested switchgrass (p < 0.05) (Fig. 2b). From May
to October 2011, soil NH4-N levels increased significantly and soil
NO3-N levels decreased significantly in PG soils (data not shown); a
similar trend was not observed in 2012 or for soils from the corn–soybean
rotation in either year.
Biomass yields
Despite significant differences in precipitation between 2011 and 2012,
biomass yields of miscanthus and switchgrass did not differ between years.
In comparison, corn grain yields were higher in 2011 vs. 2012 (Table 1).
Miscanthus produced higher yields (12.7–18.3 dry t ha-1) than
either switchgrass or corn grain, regardless of N fertilization rate or
harvest timing (Table 1). When harvested in the fall and N-fertilized,
switchgrass yields were not significantly lower (10.5–11.1 dry t ha-1)
than miscanthus yields. Switchgrass yields from unfertilized
plots were not significantly different if harvested in the fall or spring;
however, switchgrass yields from fertilized plots were significantly higher
(∼ 3–4 dry t ha-1) when harvested in the fall compared to yields
obtained when switchgrass was windrowed over winter.
Bacterial responses to annual and perennial crops and their management
There was no statistically significant effect of N fertilization or any
significant interactions between cropping system and sampling time on any of
the targeted gene abundances. Therefore, we analyzed the impact of each
biomass crop under specific harvest management on soil bacterial gene
abundances (Table 2). Denitrifying (nosZ) gene copy abundances were affected by
LUC; regardless of harvest or N management, mean nosZ gene copies were higher in
miscanthus plots than in the corn–soybean rotation, and nirS : nosZ ratios were higher
in the corn–soybean soils than in miscanthus or switchgrass soils
(p < 0.05) (Table 2). Under fall harvesting management, biomass crop
had no impact on total bacterial 16S rRNA gene copies or nirS gene copies.
However, leaving miscanthus biomass standing over winter until spring
resulted in significantly higher 16S rRNA gene copies than observed in soils
from fall-harvested biomass crops and significantly higher nirS gene copies than
in fall-harvested switchgrass or the corn–soybean rotation (Table 2).
Mean gene abundance responses to crop and harvest management, averaged
over nitrogen application rate and time at the Elora Research Station.
Cropping
Management
Total soil
Soil denitrifying bacteria (gene
nirS : nosZ
system
bacteria (gene
copy g-1 soil)*
(× 10-2)
copy g-1 soil)*
nirS
nosZ
16S
Corn–soybean
Fall harvest
1.35 × 109b
1.95 × 106b
2.63 × 105b
7.42
Miscanthus
Fall harvest
1.38 × 109b
2.30 × 106ab
4.47 × 105a
5.15
Miscanthus
Spring harvest
1.91 × 109a
3.02 × 106a
5.25 × 105a
5.75
Switchgrass
Fall harvest
1.41 × 109b
2.19 × 106b
3.55 × 105ab
6.17
Switchgrass
Spring windrow
1.48 × 109ab
2.46 × 106ab
3.98 × 105ab
6.18
* Means followed by the same letter within one column are not
significantly different according to post hoc Tukey's means comparison at p < 0.05
(n = 24).
Mean log gene copies (g-1 dry soil ± SE) in annual and
perennial biomass cropping systems under varied harvest management at the
Elora Research Station (n = 6) over time. Different letters within panels
indicate significant differences according to a post hoc Tukey's test (p < 0.05).
Temporal changes in bacterial gene abundances
Sampling date had a significant impact on gene abundances for all genes
quantified (Fig. 3). Over both sampling years, 16S rRNA gene copies were
significantly higher (5.2–5.4 × 109 gene copies g-1 dry soil) at
fall (October) sampling dates compared to the ∼ 5.5–6.4 × 108 gene
copies g-1 dry soil quantified at spring (May) sampling dates (Fig. 3).
Populations of nirS and nosZ denitrifiers represented ∼ 1.58 and 0.26 % on a
gene-to-gene basis (nirS or nosZ to 16S) of the total bacterial community (data not shown)
and did not follow similar trends with time of sampling (Fig. 3). The
abundance of nirS gene copies was significantly higher in 2012
(4.0 × 106–1.6 × 107 gene copies g-1 dry soil) compared to 2011
(2.5–6.3 × 105 gene copies g-1 dry soil), with no significant differences
between May and October sampling dates within each year (Fig. 3). The
abundance of nosZ gene copies was approximately 1.3–3.2 × 105 gene
copies g-1 dry soil, but this increased significantly in May 2012 to approximately
3.2 × 106 gene copies g-1 dry soil and dropped back to previous
levels by October 2012 (Fig. 3). Higher relative proportions of denitrifiers
(nirS or nosZ to 16S) were observed at spring sampling dates, when total bacterial
16S rRNA gene abundances decreased in comparison to fall sampling dates (Fig. 3).
(a) Principal component analysis; factor 1 accounted for 43.89 %
variance and factor 2 accounted for 23.84 % variance. (b) Loading plot for
principal components of response variables (nirS, nosZ, and
16S rRNA gene copies, as well as soil NO3-N, soil NH4-N, and gravimetric soil H2O).
Two factors were selected in the principal components analysis, which
accounted for 67.73 % cumulative variance. A scree plot was examined for
breaks, and factors were retained for eigenvalues ≥ 1. Soil
NH4-N+, soil NO3--N, nirS, and nosZ loaded on factor 1, which
accounted for 43.89 % variance, while soil gravimetric H2O and 16S
rRNA loaded on factor 2, which accounted for 23.84 % variance (Fig. 4a and b).
Differences in soil NO3--N and NH4+-N were strongly
related to differences in nirS and nosZ gene abundances observed between May 2011 and
May 2012 sampling dates (Figs. 3 and 4), while the size of the total
bacterial community (16S rRNA) was related to soil gravimetric moisture levels (Fig. 4).
Discussion
Denitrification is an important process contributing to the production and
consumption of N2O in soils, and mitigation of greenhouse gases such as N2O is
required to create sustainable biomass cropping systems (Miller et al., 2008; Schlesinger,
2013). Changes in the potential functional abilities of the soil microbial
community may reflect changes in LUC or agricultural management and should
be considered to assess the ecological impact of biomass crop production
(Hedenec et al., 2014). Currently, few studies have assessed soil microbial community responses to
PG biomass production systems (Hedenec
et al., 2014; Liang et al., 2012; Mao et al., 2013, 2011; Orr et al.,
2015). The highest potential to reduce greenhouse gas emissions from biomass cropping
systems is to produce crops with high yields, such as PGs
(Sanscartier et al., 2014), which offset the amount of land
required for crop production (Kludze et al., 2013). However,
if PG biomass production negatively affects soil health as indicated by
changes in the potential functioning of microbial communities, large-scale
LUC from annual to perennial biomass production may not be as sustainable as
originally proposed. As such it is necessary to identify biomass cropping
systems that not only result in large biomass yields but also ensure
agroecosystem sustainability by maintaining or improving ecosystem services
(Orr et al., 2015), such as soil N cycling.
Biomass yields of annual and perennial crops
Miscanthus and switchgrass biomass yields were within the typical range of
values reported previously in Ontario (Kludze et
al., 2013; REAP, 2008) and Europe (Christian et al., 2008; Himken et
al., 1997), despite differences in temperature and precipitation between the
two study years. Corn grain yields were within the lower range for reported
Ontario yields (Munkholm et al., 2013), potentially
due to wetter (2011) and drier (2012) field conditions than normal over the
two growing seasons (Roy et al., 2014). Deen et al. (2011)
showed increases in PG biomass yields between the second and third years
after PG planting at our site, whereas we measured similar yields in 2011
and 2012, indicating the PGs may have reached maximum yield potential.
Nitrogen fertilization significantly increased corn grain yields and
fall-harvested switchgrass biomass yields; however, no significant increases
due to N fertilization were observed in miscanthus or spring-harvested
switchgrass biomass yields. Potential yield increases from N fertilization
in spring-harvested switchgrass may have been offset due to leaf loss over
the winter season, as increases in switchgrass yields to N fertilization
have been previously observed (Nikièma et al., 2011; Vogel et al.,
2002). Similar to the present study, European and US field trials have also
found no response of miscanthus yields to N (Lewandowski et al., 2000, 2003;
Behnke et al., 2012; Christian et al., 2008), and PG yields were minimally
impacted by differences in growing season conditions compared to corn grain
yields (Table 1).
Despite significant differences in biomass yields between miscanthus and
corn–soybean systems, there were no significant differences in either total
or organic soil carbon between any of cropping systems assessed (Table 1).
Sampling of soil carbon occurred only two years after PG planting; PGs are
expected to be productive for 20+ years, indicating future changes in soil
carbon levels may occur. Additionally, Ontario-based land conversion
modelling scenarios have estimated a soil carbon decrease of 2.5 % upon
miscanthus establishment (Sanscartier et al., 2014), which
may have negated potential increases in soil organic carbon. However, high
miscanthus yields most likely resulted in increases in above- and
below-ground plant residue return to soils (Mutegi
et al., 2010; Soil Quality National, 2006); therefore, our carbon measures
may not have reflected short-term changes in labile carbon sources that had
occurred. Regardless of management or climatic conditions, miscanthus
consistently produced large yields, emphasizing its potential as a bioenergy
crop suitable for production in variable Ontario conditions.
Bacterial responses to annual and perennial crops and their management
Some studies in biomass cropping systems have not observed differences in
soil microbial responses between perennial and annual crop types (Mao et
al., 2011), while others have measured significant differences in microbial
abundance, diversity and community structure between these cropping types (Liang et
al., 2012; Morales et al., 2010; Watrud et al., 2013). Currently, we
observed significantly higher nosZ gene copies in miscanthus soils compared to
corn–soybean soils, illustrating a distinct effect of LUC from corn–soybean
to miscanthus production on soil N cycling (Table 2).
Due to the large biomass produced by miscanthus compared to corn, a large
amount of plant residues are returned to the soil; these residues provide
surface cover, decrease soil bulk density, increase water retention, and
regulate temperatures (Blanco-Canqui and Lal, 2009). Previous
work at the Elora Research Station found an inverse correlation between
field-scale N2O fluxes and nosZ transcript abundance in
conventionally tilled corn plots with residues returned to soils
(Németh et al., 2014), and increased nosZ activity after
residue amendment has also been observed in lab studies (Henderson
et al., 2010). High C : N plant residues have been negatively correlated with
cumulative N2O emissions (Huang, 2004) and may
encourage complete reduction of N2O to N2 as soil available
NO3-N is limiting, so bacterial populations with the ability to reduce
N2O to N2 are favoured (Miller et
al., 2008). Presently, the primers used for nosZ gene target amplification
provided good coverage of γ-Proteobacteria (Henry et al., 2006),
which are stimulated by surface-applied residues (Pascault et al., 2010).
Increased residue return in miscanthus plots may have selected for bacterial
populations harbouring enhanced catabolic capabilities, such as N2O
reduction (Pascault et al., 2010). This
implies that producing biomass crops with large yields may indirectly alter
soil N cycling and potentially mitigate soil N2O emissions due to
increased residue return influencing the soil microbial community. It is
likely that differences in environmental conditions (e.g. temperature,
H2O and O2 availability) and resource quality and availability
between corn–soybean and miscanthus soils related to differences in
microbial community structure (Cusack et al., 2011) and
selected for different dominant taxa that filled different ecological niches
(Stone et al., 2015).
N fertilization did not affect targeted gene abundances; however, studies in
other cropping systems have found that N fertilization affected the size of
denitrifying communities (Hallin et al., 2009),
nitrifying communities (He et al., 2007),
and proportions of nirS to nirK communities (ratio of nirS : nirK genes)
(Hai et al., 2009). Elevated 16S rRNA and
nirS gene copies were observed in soils from spring-harvested miscanthus and
windrowed switchgrass (Table 2). Increased N return via senescent leaf loss
in PG plots over winter contributes to the soil organic matter pool (Heaton
et al., 2009) and may have contributed to elevated total (16S rRNA) bacterial
populations in these soils, concomitantly increasing nirS abundances
(Huang et al., 2011).
Temporal changes in bacterial gene abundances
Total soil bacterial communities (16S rRNA) followed a seasonal trend, with
elevated 16S rRNA gene copies at fall (October) compared to spring (May)
sampling dates, possibly due to an increase in the availability and
diversity of resources for microbial metabolism and growth over the growing
season (Habekost et al., 2008). Denitrifying abundances
changed differently than the total bacterial community, suggesting
denitrifiers were influenced by different proximal regulators than the total
bacterial community (Figs. 3 and 4). Seasonal dynamics of N-cycling microbial
communities have been previously characterized (Boyer et al.,
2006; Németh et al., 2014; Wolsing and Priemé, 2004; Dandie
et al., 2008; Bremer et al., 2007)
and are tightly coupled with seasonal changes in labile C and N pools,
temperature, and soil H2O (Butterbach-Bahl
et al., 2013; Rasche et al., 2011), indicating that local edaphic drivers may
often take precedence over crop-specific drivers (Mao et al., 2013).