Introduction
In the current regulation concerning energy and climate change policies,
the European Union (EU) established two targets for 2020: (i) a reduction of
20 % of greenhouse gas (GHG) emissions relative to the levels of 1990, and
(ii) a share of 20 % renewable energy use in gross final energy consumption
(European Commission, 2007, 2008). For Italy the latter
is set at 17 % (European Commission, 2009).
In the context of climate mitigation, bioenergy crops are expected to play a
key role in the renewable energy supply in the EU in the coming decades
(Djomo et al., 2013). Short-rotation coppices (SRC) of fast-growing trees,
and especially of poplar (Populus spp.), are a promising culture in
this context. SRC has the potential to reduce GHG emissions to the atmosphere
during both its production (by capturing CO2 from the atmosphere and
storing it in above-ground biomass and soil) and use (by avoiding CO2
emissions from fossil fuel burning). However, the management of SRC requires
energy inputs and the use of fossil fuels. Furthermore, the land use change
(LUC) to SRC may imply losses of soil organic carbon (SOC) at the point of
its installation (Don et al., 2012), especially in C-rich soils. For these
reasons, converting land for SRC production may alter the equilibrium of the
existing ecosystems, causing an impact that in some cases can counterbalance
the positive effects on climate mitigation of the supposedly carbon-neutral
SRC systems (Abbasi and Abbasi, 2009; Zona et al., 2013; see also Crutzen et
al., 2008; Fargione et al., 2008, on bioenergy crops in general). A recent study (Djomo
et al., 2011), however, showed that poplar and willow SRC biomass use can
save up to 80–90 % of GHG emissions compared to using coal for energy
production. Studies on the climate mitigation potential of poplar
cultivations constitute an important tool in supporting energy and
environmental policies on different scales. In recent years researchers have
approached poplar SRCs from different perspectives: ecological (Jaoudé et
al., 2011; Zhou et al., 2013), economic (Strauss and Grado, 1997; Mitchell et
al., 1999; El Kasmioui and Ceulemans, 2012, 2013), and related to energy
production and different environmental aspects (Jungmeier and Spitzer, 2001;
Cherubini et al., 2009; Davis et al., 2009; Nassi o Di Nasso et al., 2010;
Arevalo et al., 2011; Don et al., 2012; Dillen et al., 2013; Djomo et al.,
2013). However, these studies often used different approaches, making it
difficult to compare their results (Migliavacca et al., 2009; Djomo et al.,
2011). Furthermore, emphasis was mainly placed on emissions from fossil fuels
rather than on the biogenic emissions due to the LUC (Djomo et al., 2013).
Including the different contributions of the LUC in the assessments of
emission savings related to energy crops is crucial (Davis et al., 2009). A
full GHG budget based on long-term measurements of CO2 and non-CO2
GHGs via eddy covariance (EC) methodology (Aubinet et al., 2012) and soil
chamber measurements (Allard et al., 2007) can be used to assess the GHG
mitigation potential of land conversion to SRC (Byrne et al., 2007; Ceschia
et al., 2010). Several authors (e.g. Ceschia et al., 2010; Osborne et al.,
2010) highlighted the need for a more consistent number of studies on GHG
budgets, including different types of management practices, climate
conditions, and soil characteristics, in order to reduce the uncertainty in
GHG budgets on a large scale (Smith et al., 2010). A GHG budget approach was
used by Gelfand et al. (2011) in a conversion of unmanaged lands to
herbaceous biofuel crops in the US. In Europe, Zona et al. (2013) estimated
the GHG balance in the first year after the conversion from agricultural
lands to a poplar SRC in Belgium, focusing on biogenic contributions. The
present study considered the conversion of a cropland (hereafter referred to
as “REF site”) to a poplar SRC (hereafter referred to as “SRC site”) for
bioenergy production in the Mediterranean area (Viterbo, Central Italy). The
aim was to extend the GHG balance to emissions generated by field management
and to the offset of GHG from fossil fuel substitution. The number of studies
on SRC systems cultivated in Mediterranean areas, where water availability
can constitute a limiting factor for biomass yield and thus climate
mitigation (Cherubini et al., 2009), is limited. Given that the climate
change mitigation potential of energy crops is the main reason for subsidies
to arable land conversion, our study aimed to assess the suitability of the
LUC to SRC in terms of the mitigation of GHG emissions.
Materials and methods
GHG budgets assessment
The GHG budgets were calculated for the SRC and for the REF sites on a
temporal basis of 2 years (24 months), corresponding to the second
rotation cycle of the SRC site. They included several positive and negative
GHG contributions, with the following sign convention: a positive flux
indicates a release into the atmosphere, while a negative flux represents an
uptake from the atmosphere. In both cases the boundary of the system was set
to the farm level. For the SRC site, the net GHG budget (BSRC) was
calculated as the algebraic sum of all GHG contributions as indicated in Eq. (1):
BSRC=FCO2+FCH4+FN2O+FMAN+FSOC+FSAV+FEXP.
In Eq. (1), FCO2 represents the flux of CO2, i.e. the net
ecosystem exchange (NEE) of CO2, while FCH4 and FN2O represent
the biogenic methane and nitrous oxide soil–atmosphere exchanges.
FMAN includes the GHG emissions related to the management of the SRC
site, and FSOC is the loss of soil organic carbon content due to the
installation of the cuttings. FSAV represents the GHG offsets, i.e. GHG emissions avoided due to the substitution of natural gas by biomass in heat production, and FEXP represents the biomass exported from the site at the
end of the cycle and re-emitted as CO2 at burning.
Similarly, the net GHG budget of the REF site (BREF) was estimated with
the algebraic sum indicated in Eq. (2), where unlike in Eq. (1), there is
no FSOC and FSAV, and FEXP is the portion of the exported
biomass that returns to the atmosphere as CO2 or CH4:
BREF=FCO2+FCH4+FN2O+FMAN+FEXP.
All the contributions of BSRC and BREF were expressed as
CO2-equivalent (CO2eq) fluxes per unit of surface, as the
functional unit of the study was 1 m2 of land. Finally, the net
GHG cost or benefit of converting the cropland to an SRC plantation was
calculated by comparing BSRC and BREF. Displacement of food and feed
production related to SRC cultivation on cropland was beyond the scope of
this study.
Site description
Two sites close to each other located on a private farm (Gisella ed Elena
Ascenzi S.A.A.S.) in Castel d'Asso, Viterbo, Italy (coordinates:
42∘22′ N, 12∘01′ E), were selected during summer 2011.
Two EC towers were installed at the two sites to measure the exchanges of
CO2 and H2O between the ecosystem and the atmosphere following the
methodology reported in Aubinet et al. (2000). The climate of the area is
Mediterranean, with a yearly average rainfall of 766 mm, mean temperature of
13.76 ∘C, and weak summer aridity in July–August (Blasi, 1993). The
SRC site was a 2-year rotation-cycle-managed poplar plantation of 11 ha
planted in 2010 to produce biomass for energy (heat). Poplar cultivar was
Populus x canadensis – clone AF2, selected in Alasia
Franco Vivai's nurseries. According to the regional law (Rural Development
Programme of Latium 2007–2013, Latium Region, 2015), 12 years is the maximum
period during which to obtain subsidies for SRC, and this corresponded to the
time the farmer decided to cultivate the SRC site (A. Trani, personal
communication, 2012). For that reason the calculations of the present study
will be based on a 24-month period taking into account the 12-year lifespan
for the SRC site. The site was previously managed with a 2-year rotation
between a clover grassland (Trifolium incarnatum L.) mixed with
ryegrass (Lolium multiflorum Lam.) and winter wheat
(Triticum aestivum L. emend. Fiori et Paol.). The REF site was a
9 ha grassland–winter-wheat rotation located a short distance away
(300 m). As this site had identical land use and management to the SRC site
before the installation of the poplars, it was selected to assess the GHG
effects of the LUC. GHG balances were calculated over 24 months at both
sites. However, these 24-month periods did not completely overlap, as the two
cultivations had different starting times: for the SRC site the GHG budget
estimation went from 12 January 2012 (immediately after the first harvest of
the SRC site) to 11 January 2014, corresponding to the second cycle of
cultivation. The period of calculation of the GHG budget for the REF site
went instead from 1 September 2011 until 31 August 2013. However, manual
chamber measurements of CH4 and N2O at the REF site started at the
beginning of April 2012. The 24 months considered for the SRC site
corresponded to the second cycle of the short-rotation coppice and thus did
not include the period right after the conversion of agricultural land. This
rotation was supposed to terminate with the harvest. However, due to
unfavourable climate conditions (a strong drought during summer), the harvest
of the SRC site, planned for 2014, was postponed to 2015.
The SRC site had a planting density of around 5300 cuttings per hectare,
which were planted in rows 2.5 m apart, with a distance of 0.75 m between plants in the same row. The first harvest occurred in January 2012. The SRC site was
irrigated during the driest periods in summer using a system of tubes
installed 35 cm belowground on alternate inter-rows, totalling about 210 mm in 2012 and 80 mm in 2013 of equivalent precipitation added to the soil.
No fertilizer was provided to the SRC site in 2012, while 40 kg of urea per hectare were dissolved in the irrigation water in a single event in 2013.
Insecticide (DECIS) was used in May 2012 against Chrysomela populi L. At the REF site a
shallow tillage (15 cm) was performed in September 2011 with a rotary
harrow, and the mixture of clover and ryegrass was sown. At the end of April 2012 half of
the crop was converted to sorghum (Sorghum vulgare Pers.) after a period of aridity in spring
time. Both the clover and the sorghum were grazed during the growing season,
with grazing removing all the above-ground biomass from the sorghum, while
the clover was harvested at the end of the cycle. At the end of October 2012
the land was tilled at 40 cm depth, and winter wheat was sown in November.
In April 2013 herbicide was distributed over the wheat (Buctril, at a rate of
1 L ha-1), which was harvested at the beginning of July 2013 and no
other operation was performed until the end of August. Sorghum was irrigated
on several days in summer using a sprinkler with a total amount of 275 mm of
equivalent precipitation, while no irrigation was applied to the winter
wheat. Sorghum was also fertilized twice with 150 kg ha-1 of ammonium
nitrate, while 200 kg ha-1 of the same fertilizer were provided once to
the wheat. Apart from irrigation and fertigation, all the operations
described above were performed using two different types of tractors,
generating different diesel consumptions associated with each operation (Table 3).
An older SRC site (indicated hereafter as O_SRC site), located
alongside the other one and subjected to the same type of management but
planted in 2007, was used in the estimation of SOC content loss caused by
the LUC. This was necessary as the expected SOC loss following the
conversion (i.e. during the first rotation) was not measured.
In the 24 months considered for the GHG budget of the SRC site,
precipitations totalled 1078 mm, with an average temperature of 14.72 ∘C, while in the 24 months used for the REF site precipitations
were 1157 mm, with an average temperature of 15.31 ∘C. In both cases
yearly values of precipitation were lower than the long-term average of 766 mm (Blasi, 1993). An intense drought occurred in summer 2012, with no rain
from the beginning of June until the end of August, in contrast to the
long-term average of cumulate rainfall in these months (110 mm, Blasi,
1993). Soils were classified as Chromic Luvisol according to the World Reference Base
classification (IUSS, 2014), with a clay-loam texture. Values of pH ranged
between 5.88 at the REF site, 6.66 at the O_SRC site, and 6.69
at the SRC site. The stock of nitrogen (N) up to 70 cm was not significantly
different between sites, ranging from 3.16 ± 1.60 to
3.19 ± 1.47 and 3.25 ± 1.47 Mg N ha-1
respectively for SRC, O_SRC, and REF sites. See Fig. 1 for a
schematic representation of land cover and management events of the two
sites.
Scheme of the chronological land cover during the cultivation
cycle taken into account for GHG budget calculation in the two ecosystems.
The expected harvest of poplar at the beginning of 2014 was postponed of 1 year: for that reason data from the previous harvest (beginning 2012) were
taken into account for GHG budget calculation. Textures indicate different
land cover type, symbols mark the most important management practices,
straight lines indicate the periods in which sites were irrigated, dashed
line period of grazing. SRC: short-rotation coppice site; REF: reference site; in the x axis dates are reported as month-year (mm-yy).
FCO2: eddy covariance
measurements
The EC technique was used to determine the turbulent vertical fluxes of
momentum, CO2, and latent and sensible heat. A 3-D sonic anemometer was
installed at each site for high-frequency measurements of wind speed, wind
direction, and sonic temperature. CO2 and water vapour densities were
collected using a fast-response open-path infrared gas analyser (see Table 1
for models and manufacturers). These instruments were mounted on towers located approximately in the centre of the fields. At the REF site the mast was 3 m high,
while an extendible telescopic pole was used at the SRC site in order to
always measure turbulences above the roughness layer (Foken, 2008).
Measurement heights ranged between 5 and 8 m, and the distances of the
measuring system over the d+z0 plane ranged between 2 and 5 m (d: displacement height; z0: roughness length). Several meteorological
variables above and belowground were continuously measured on a 30 min basis
to properly calculate fluxes and characterize the two sites. In Table 1 the
complete instrument set-up is described, including both meteorological and
high-frequency variables.
Instrumental set-up of the two towers. SRC: short-rotation
coppice site; REF: reference site; Tair: air temperature; T: temperature; RH: relative
humidity; PAR: photosynthetically active radiation; MSOIL: soil
water content; G: soil heat flux; P: precipitation; EC: eddy covariance; prof: profile.
Four-component radiometers were used to measure short- and long-wave
radiations and to derive net radiation. SRC site soil profiles were located in
irrigated and non-irrigated inter-rows. Precipitation and PAR were assumed to be
consistent in the two ecosystems.
SRC
REF
Tair and RH
MP-100, Rotronic AG, Bassersdorf, CH
MP-100, Rotronic AG, Bassersdorf, CH
PAR
Li-190, LI-COR, Lincoln, NE, USA
–
Radiations
CNR-1, Kipp & Zonen, Delft, NL
NR01, Hukseflux, Delft, NL
MSOIL
CS616, Campbell Scientific, Logan, UT, USA (2 prof.)
CS616, Campbell Scientific, Logan, UT, USA (1 prof.)
Soil T
107, Campbell Scientific, Logan, UT, USA (2 prof.)
107, Campbell Scientific, Logan, UT, USA (1 prof.)
G
HFT3, REBS Inc., Seattle, WA, USA
HFP01, Hukseflux, Delft, NL
P
–
ARG100, EML, North Shields, UK
Logger
CR3000, Campbell Scient., Logan, UT, USA
CR1000 Campbell Scient. Logan, UT, USA
Anemometer
CSAT3, Campbell Scientific, Logan, UT, USA
USA-1, Metek GmbH, Elmshorn, DE
Gas analyser
LI-7500, LI-COR, Lincoln, NE, USA
LI-7500A, LI-COR, Lincoln, NE, USA
Half-hourly fluxes were calculated with EddyPro® software
(LI-COR, Lincoln, NE, USA). Several corrections to the time series (Aubinet
et al., 2012) were applied as reported in Table 2. Post-processing included
spike removal and friction velocity (u∗) filtering (Papale et al.,
2006), gap filling using the marginal distribution sampling (MDS) approach
and the partitioning of FCO2 into gross primary production (GPP) and
ecosystem respiration (Reco) components (Reichstein et al., 2005). The
gap-filled FCO2 and its components were then cumulated along the
24-month period considered.
Uncertainty in FCO2 was calculated on the basis of the uncertainty in
the u∗ filtering, assuming that the main potential systematic error
is due to advection and thus linked to the u∗ filtering. One
hundred thresholds were calculated using a bootstrapping technique and then
applied to filter the data. For each half-hour, the median of the
distribution of FCO2 obtained using the 100 thresholds was used for the
GHG budget (Gielen et al., 2013), and the range of uncertainty was derived
as half the range between the 16th and the 84th percentile.
Correction steps applied to the time series using LICOR EddyPro
software.
Correction
Reference
Despiking
Vickers and Mahrt (1997)
Density fluctuations
Webb et al. (1980)
Maximization of covariance
Aubinet et al. (2000)
for time lag compensation
Linear detrending for trend removal
Gash and Culf (1996)
Two-dimensional coordinate rotation
Wilczak et al. (2001)
High-pass filtering effect
Moncrieff et al. (1997)
Low-pass filtering effect
Ibrom et al. (2007)
Soil characteristics and SOC stock and changes
To better characterize the soil properties and to quantify the changes in SOC
stocks due to the installation of the poplar plantation, a number of soil
analyses were performed at the three sites in two different periods. In the
first phase, in February 2012, three soil trenches 150 cm wide were opened
randomly at each site and the soil sampled by depth (0–5, 5–15, 13–30,
30–50, 50–70, 70–100 cm) at opposite sides of the profiles, resulting in
six replicate samples per depth. The bottom layer (70–100 cm) was absent at
the REF site due to the presence of bedrock at 80 cm rather than at 100 cm
as at both the SRC sites. Samples were collected using a cylinder to also
determine the bulk density. Main goals of this first sampling campaign were
to describe the soil characteristics and to determine the number of
replicates necessary to detect, with statistical significance, a change in
SOC content of 0.5 g C kg-1 soil (Conen et al., 2003). In the SRC and
O_SRC sites 10 samples of the organic layer were also taken, removing all
the material present over the mineral surface within a squared frame with an
area of 361 cm2. At the REF site this sampling was not performed
because a permanent organic layer was not present. All samples were air-dried
at room temperature and then sieved at 2 mm to separate the coarse fraction,
and the analyses were performed on the fine earth. The pH was measured
potentiometrically in deionized water by using a sure-flow electrode and a
ratio of soil to solution of 1 : 2.5 (w : w). The texture, on the other
hand, was determined using the pipette method after the organic cements were removed by
using sodium hypochlorite adjusted at pH 9 (Mikutta et al. 2005). The sand
fraction was separated by wet sieving at 53 µm, while the silt and
the clay fractions were separated by time sedimentation according to the
Stokes law. Total carbon (C) and nitrogen concentrations were measured on
finely ground samples by dry combustion (ThermoFinnigan Flash EA112 CHN),
while SOC and N stocks were determined taking into account soil C and N
concentrations and a weighed mean of bulk density, depth of sampling, and
stoniness (Boone et al., 1999). During the second phase in March 2014, a new
sampling was performed at the REF, SRC, and O_SRC sites. The number of
samples necessary to detect an SOC change statistically was 50, as derived
from the first phase. Samples were taken from the first 15 cm of soil, as
most of the changes in a short period occur in the shallower layers. C
concentration was measured and SOC stocks recalculated. The normality of the
distributions was checked using a Chi-squared test (Pearson, 1900). An
analysis of variance (ANOVA) test (Fisher, 1919), combined with a Tukey
multiple comparison test, was used to check whether SOC stocks were different
between the sites. As data of FCO2 from the beginning of the
cultivation are missing, SOC changes due to the installation of the poplar
cuttings were calculated building a linear regression between SOC content of
the SRC site (4 years old) and the O_SRC site (7 years old) and then
estimating the SOC at the time of plantation (year “0”). Following the
“free-intercept model” described by Anderson-Teixeira et al. (2009), the
SOC content change due to the plantation of the SRC was then extrapolated
considering the difference between the SOC content at year 0 and the one
measured at the REF site, assuming the SOC content at the REF site to be in
equilibrium, as this type of land use was constant in the last 30 years.
Uncertainties in SOC concentration and stock were calculated as standard
deviations from the mean values of each repeated measure, while errors were
estimated using the law of error propagation as reported by Goodman (1960).
Soil CH4 and N2O fluxes
On-site measurements of CH4 and N2O soil fluxes were combined with
laboratory incubation analyses, where soil samples were tested at different
water contents and N addition levels. Field measurements of soil N2O and
CH4 fluxes were carried out at the two sites using nine manual, dark,
static PVC chambers (15 cm diameter, 20 cm height, and total volume
0.0039 m3) per site, placed over as many PVC collars (7 cm height,
15 cm diameter) permanently inserted into the soil at 5 cm depth for the
period of observation. At the SRC site, three collars were distributed along
one of the lines of trees (by
placing each of the collars between two trees), three along one of the
irrigated inter-rows, and three along one of the non-irrigated inter-rows;
all were placed at a distance of about 5 m from each other. At the REF site, collars were
placed in three different blocks of three collars each. Gas samples were
collected from each chamber at the closure time and 30 and 60 min after
closure. Samples were stored in glass vials provided with a butyl rubber
airtight septum (20 mL), and the concentration of N2O and CH4 was
measured using a TRACE Ultra gas chromatograph (GC; Thermo Scientific,
Rodano, IT). The flux detection limit due to the concentration measurement
was of the order of about 0.1 mg of CH4 or N2O m-2
day-1, and the analytical precision of the GC for standards at ambient
concentration was approximately 3–5 %, using 1 standard deviation as a
measure of mean error. Further details on GC are found in Castaldi et
al. (2013). Measurements started 2 weeks after collar insertion and samples
were collected every 2–4 weeks, depending on land management practices and
weather conditions, for a total of 30 dates at the SRC site and 24 at the REF
site. Similar frequencies were used in previous studies (e.g. Pihlatie et
al., 2007; Weslien et al., 2009) and were considered pertinent on the basis
of the low magnitude of the measured fluxes. To test if fertilization could
trigger a peak of N2O emission as found in previous studies (e.g. Gauder
et al., 2012), measurements at both sites were carried out more frequently
during fertilization events (on average every 2 days), starting from the day
before the application of fertilizer and for 1 week. Measurements also
covered different soil and meteorological conditions, including periods of
drought and rewetting. Measured average daily soil CH4 and N2O
fluxes were cumulated over the 24 months by linear interpolation as described
by Marble et al. (2013), and uncertainty calculated propagating the standard
deviations of the replicates. FN2O and
FCH4 were converted to CO2 equivalents by multiplication
by 298 and 25 respectively. These factors were based on the Intergovernmental
Panel on Climate Change (IPCC) 100-year global warming potential (GWP)
weighted estimates of GHGs (Forster et al., 2007).
Laboratory incubations
Due to the fact that we do not have continuous measurements of non-CO2
fluxes from soil, we performed a laboratory analysis to verify the accuracy
of field campaigns. Laboratory incubations were carried out to assess the GHG
emission rates under controlled laboratory conditions in soil treated with
both water and nitrogen addition and to quantify the rates of soil
mineralization and nitrification. The purpose of the incubation was to assess
whether the fluxes were driven by limiting conditions such as water and/or
nitrogen or a slow rate of organic N mineralization, as found in a
Mediterranean coppice site in the same region (Castaldi et al., 2009;
Gundersen et al., 2012). The addition of N allowed us to check whether
short-time peaks of emissions occurred that could escape due to the selected
frequency of sampling. Soil cores (7 cm diameter, 10 cm height) sampled in
the two ecosystems were incubated at 20 ∘C. Water was then added to
reach three different ranges of water-filled pore space (WFPS): 20 (i.e. the
value estimated at sampling), 50, and 90 %, each of them replicated five
times. The sample with the highest WFPS percentage was also replicated with
or without nitrogen supply (100 kg N ha-1 of NH4NO3). Cores
were placed in gas-tight 1 L jars, and 6 mL air samples were collected
immediately after closure and after 3 h of incubation for N2O
production determination. Gas concentration was determined by gas
chromatography on the day after the treatment and in the following 5 days,
leaving the jars open during this period and closing them only when N2O
production needed to be determined, in order to avoid the development of
liquid oxygen tension conditions. Net mineralization and nitrification and
the net potential nitrification rate were determined on sieved (2 mm mesh)
soil samples over 14 days of incubation, while for the determination of
potential nitrification, soil was amended with ammonium sulfate
(NH4)2SO4 (100 µg N g-1 dry soil). A modified
method (Kandeler, 1996; Castaldi and Aragosa, 2002) was used to extract
NH4+ and NO3- from the soil at T0 and T14 days for
further concentration determination with calibrated specific electrodes after
the addition of a pH and ionic buffer 0.4 mL of ISA (Ionic Strength
Adjustor; Orion cat. no. 951211 and Orion cat no. 930711). Mineralization
rates were calculated as the total soil mineral N (µg of N-NH4++ N-NO3- per gram of dry soil) measured after 14 days of incubation
(T14) minus total mineral N measured at the incubation start (T0)
divided by the number of days of incubation. Nitrification rates were
calculated similarly, considering only the amount of N-NO3- produced at
T14 minus the amount of N-NO3- present at T0.
In order to compare results obtained with soil cores to field conditions, in
situ WFPS percentage was calculated for the whole period of field monitoring:
WFPS%=MSOIL1-ρBULK/ρPART×100,
where MSOIL is the volumetric soil moisture (m3 m-3), ρBULK is the bulk density (Mg m-3), and ρPART is the
particle density (Mg m-3). For mineral soil, ρPART is
approximated to that of common silicate materials (2.65 Mg m-3; Chesworth, 2008).
Emissions due to management
Life cycle inventory (LCI) was used to estimate the anthropogenic GHG
emissions due to farming operations (Robertson et al., 2000) at both sites
(Table 3) and the GHG emissions due to grazing at the REF site (Table 4). The
present study is not a full LCA, but the LCA approach was used to estimate
emissions caused by field management as described in the following. In
particular, indirect land use change (iLUC) was not taken into account. iLUC
includes modifications in land use elsewhere in the world triggered by the
local substitution of arable land with an energy crop (Djomo et al., 2013).
iLUC occurred outside the boundary of the system we used for this analysis,
i.e. the farm. Fossil fuel emissions associated with the cultivation of the
SRC and REF sites included on-site emissions from tractors (used to carry out
all the main operations: ploughing, seeding, solid fertilization, harvesting)
and irrigation, as well as off-site emissions from the production and
transport of agricultural inputs (fertilizer, insecticide, herbicide).
Emissions due to the production of tractors were considered negligible, as in
Budsberg et al. (2012) and Caputo et al. (2014). On-site GHG emissions due to
diesel consumption were calculated as the product of the amount of fuel
diesel consumed to carry out a given farm activity (e.g. harvesting) and the
emissions factor of diesel, 90 g CO2eq MJ-1 (Table 3). This
factor includes emission costs due to the combustion of diesel (74 g
CO2eq MJ-1) and emissions due to its production and transportation
(16 g CO2eq MJ-1; Edwards et al., 2007). Considering the energy
density of diesel to be 38.6 MJ L-1 (Alternative Fuels Data Center,
2014), producing, transporting, and burning 1 L of diesel emitted 3474 g
CO2eq. An exception was made for harvesting at the SRC site, for which
emissions for diesel consumption relative to the previous harvest (2012) were
considered, as the harvest at the end of the cycle was postponed. Emissions
due to irrigation were calculated by multiplying the electricity consumed in
powering the pumps by an emissions factor of 750 g CO2 kWh-1,
calculated as the average of different emission factors for different sources
of electricity (Bechis and Marangon, 2011) weighted according to the Italian
electricity grid mix, derived from the Italian energetic balance 2012
(Italian Ministry of Interior, 2013). Off-site emission costs for fertilizers
and insecticides were estimated as the product of the amount of fertilizer or
insecticide applied and the emission factors for manufacturing 1 kg of
fertilizer or insecticide: 4018.9 g CO2 kg-1 N for urea (NPK
rating 40-0-0), 4812 g CO2 kg-1 N for diammonium phosphate (NPK
18-46-0), 7030.8 g CO2 kg-1 N for ammonium
nitrate (NPK 33-0-0), and 7481.9 g CO2 kg-1 N for calcium
ammonium nitrate (NPK 27-0-0; Wood and Cowie, 2004). Although emission
factors differ among insecticide types, in this analysis we assumed that the
difference is negligible as the use of insecticides was limited, and thus
considered the emission factor of insecticide (active ingredient:
deltamethrin) as the product of energy required to produce 1 kg of
insecticide (310 MJ kg-1) and the emission rate of insecticide (60 g
CO2 MJ-1; Barber, 2004; Liu et al., 2010). The emission factor of
herbicide was taken from the literature (Ceschia et al., 2010): 3.92 kg C
per kg of product. The fuel used for the application of chemical products
was included in the on-site calculations described above. All the
contributions listed above were converted on a surface basis (Table 3).
Farming activities. Three tractors of different power were normally
used to collect chips: two of type 1 and one of type 2. DAP: diammonium
phosphate; AN: ammonium nitrate; CAN: calcium ammonium nitrate. SRC and REF
as defined previously. Reported units are given per hectare and activity.
Occurrences of the same type of operation with different characteristics are
listed in the same cell using lowercase letters.
Operation
Fuel consumption
Input rates
Site
(unit ha-1)
(unit ha-1)
Harvesting – wood chipper
30 L diesel
–
SRC
Harvesting – tractor type1
20 L diesel
–
SRC
Harvesting – tractor type 2
10 L diesel
–
SRC
Shallow tillage
8 L diesel
–
SRC, REF
Application of insecticide
1.125 L diesel
1.25 kg DECIS®
SRC
Mechanical weeding
4 L diesel
–
SRC
Ploughing
8 L diesel
–
SRC, REF
Sowing
2 L diesel
–
REF
Seed covering
4 L diesel
–
REF
a. 150 kg DAP
a. REF
Application
2 L diesel
b. 150 kg AN
b. REF
of fertilizer
c. 200 kg CAN
c. REF
d. 40 kg Urea
d. SRC
Reaping
20 L diesel
–
REF
Chemical weeding
1.125 L diesel
1 L Buctril®
REF
Bale
7.5 L diesel
–
REF
Irrigation
a. 471 kWh electricity
a. 16 L H2O
a. SRC
b. 149 kWh electricity
b. 46 L H2O
b. REF
Grazing calendar and methane emissions at the REF site.
Graz_days: number of days with grazing; num: number of
sheep in the cropland.
Months
Graz_days
Num (per 9 ha)
December 2011
10
800
January 2012
7
400
June 2012
2
580
August 2012
1
580
September 2012
2
580
October 2012
5
400
Biomass use and GHG offset
During the first year of cultivation, the REF site was grazed by sheep, which
were brought to the field in defined periods (Table 4). Hence, at different
periods, the above-ground biomass (AGB) from the REF site was either grazed
by sheep, provided as hay to other livestock, destined for meat and milk
production, or in the case of wheat used in food (grains) and feed (foliage)
production. Due to the different species cultivated throughout the 2 years
and to the different uses of the biomass, FEXP of the REF site
(Eq. 2) includes the following:
FEXP=ECH4,on+ECO2,on+ECH4,off+ECO2,off,
where the first subscript indicates whether the exported C is re-emitted to
the atmosphere as CO2 or CH4 and the second subscript
distinguishes between emissions occurring on-site (on) and off-site (off). In
fact, the percentage of AGB ingested by herbivores on grassland varies with
the intensity of management (Soussana et al., 2010). In the present study,
however, what was left in the field by the sheep was then harvested and
provided to them off-site. We assumed then that, apart from the grains in wheat
ears, all the AGB was ingested by sheep or other livestock and that the
digestible portion of the organic C ingested was respired back to the
atmosphere as CO2 or emitted as CH4 via enteric fermentation (Eq. 4; Soussana et al., 2007). Biomass at the REF site was sampled every 2–3
weeks in five plots (0.5 m × 0.5 m) randomly selected within the field. At three dates, samples were collected immediately after grazing in a grazed
area and in an undisturbed area to quantify the intensity of mowing (68 %)
and identify the C ingested on-site and off-site. Biomass samples were
oven-dried at 70 ∘C to constant mass and weighed. Total AGB was
obtained by cumulating dry weights measured immediately before each grazing
event, each time subtracting the 32 % of the dry weight of the previous
sample to consider mowing intensity. The IPCC methodology (Dong et al., 2006)
was then used to estimate ECH4,on (Eq. 4), adjusting the methane
emission factor per animal considering the average weight (55 kg) of sheep
(19 g CH4 head-1 day-1) and multiplying it by the daily
number of sheep present on-site. The method in Soussana et al. (2007; their
Eq. 4) was then adapted to estimate the other three components in Eq. (4):
ECH4,off was estimated by applying to the C ingested off-site the
ratio between the C weight in ECH4,on and the C ingested on-site. The C
emitted as CH4 was subtracted from the digestible portion of the C
ingested, assumed to be 65 %, and the remaining converted in CO2 so as
to estimate ECO2,on and ECO2,off. The remaining, non-digestible C
(35 %) was assumed to be returned to the SOC of the grassland (for the
on-site part) or of other systems (for the off-site part) as faeces, thus
not contributing to the GHG balance. The portion that formed the C stock in the
body mass of animals was considered negligible (Soussana et al., 2007). For
the sake of simplicity, we assumed that the C content of wheat ears
will also be respired back to the atmosphere as CO2 quickly, and it was thus
included in ECO2,off (Eq. 4).
At the end of the cycle, poplar above-ground woody biomass (AGWB) of the SRC
site was supposed to be harvested and burnt, thus, on the one hand, releasing
C back to the atmosphere and, on the other, offsetting GHG emissions for
fossil fuel displacement. To estimate poplar AGWB, stem diameters were
measured at the end of the cycle after the leaves had fallen. Three rows of
trees were selected inside the plantation and the diameters of these trees
were measured (minimum threshold 0.5 cm) at 1 m height. A simple model
considering the regression between individual shoot dry weight (WD)
and 1 m diameter (D) was used:
WD=b×Dc,
where b and c are empirical parameters, WD is given in
kilograms of dry mass, and D is given in centimetres. Parameters were set
as b= 0.0847 and c= 2.112 following Mareschi (2008; see also Paris et
al., 2011) for the second rotation cycle of clone AF2 of the plantation
located in Bigarello (Mantua province). Among the plantations presented in
this publication, Bigarello is the one with climatic and soil characteristics
that are more similar and it also has the same root and shoot age. Dry
combustion (1108EA, Carlo Erba, Milan, IT) was used to determine the C
concentration for both sites. Regarding the GHG emissions offset, it was
assumed that heat produced from SRC biomass will substitute heat produced
from natural gas. The GHG offset (FSAV) was estimated based on the
yield of the SRC site, the energy density of poplar, the conversion
efficiency of a typical biomass boiler in Italy, and the emission rate of
heat production from natural gas in Italy:
FSAV=Y×HL×ηCONV×ING,
where Y is the biomass yield (kg m-2), HL is the low heating value
of poplar (13 MJ kg-1 at 30 % moisture content; Boundy et al., 2011), ηCONV is the efficiency of conversion of poplar chips to
heat, assumed in this study to be 84 % (Saidur et al., 2011), and
ING is the carbon emission rate (intensity) of heat produced from
natural gas (i.e. 55.862 g CO2eq MJ-1) for Italy (Romano et al.,
2014).
Boxplot of the 24-month cumulative fluxes of net ecosystem
exchange of CO2 (FCO2, a), gross primary production (GPP; b), and
ecosystem respiration (Reco; c) from eddy covariance (EC) data at the REF
and SRC sites. Each box represents the range 16th–84th percentile: the
central mark is the median, while the whiskers extend to the 5th and 95th
percentiles.
Results
Biogenic fluxes of CO2
The cumulative FCO2 at the REF site for the 2 years considered was
-1838 ± 107 g CO2 m-2, partitioned into 8032 ± 313 g CO2 m-2 absorbed through photosynthesis (GPP) and
6216 ± 338 g CO2 m-2 emitted by total Reco. At the SRC
site, cumulative FCO2 was -3512 ± 224 g CO2 m-2, with a GPP equal to
8717 ± 298 and Reco equal to 5205 ± 425 g CO2 m-2 (Fig. 2). Hence, the SRC site was a larger CO2
sink compared to the REF site over the measuring period, due to both the
higher GPP and the lower ecosystem respiration of the SRC site relative to
the REF site.
Seasonal differences between the sites in the net flux of CO2 were
observed (Fig. 3). The main difference was the timing of the peak of CO2
uptake, which occurred during spring at the REF site and in summer at the SRC
site. At both sites, peaks in CO2 uptake were higher in 2013 than in
2012. In the latter year, however, a minor peak of uptake was observed in
early fall in the SRC site. Periods with positive net fluxes of CO2
appeared longer and with higher values at the REF site (Fig. 3, top). Air
temperatures (Tair) registered at the two sites were similar but
higher in summer 2012, while the SWC (soil water content) at 30 cm depth was
higher at the REF than at the SRC site (Fig. 3, bottom).
Soil CH4 and N2O fluxes
Daily average of both FN2O and FCH4 were very low in almost every
measurement (Fig. 4), leading to low total cumulative soil FN2O and
FCH4 for both the sites: overall soil non-CO2 fluxes were
15.5 ± 4.7 g CO2eq m-2 in 2 years for the SRC site and
0.5 ± 1.6 g CO2eq m-2 in 2 years for the REF site. Both
sites were small sources of N2O and small sinks of CH4. The CH4
sink at the SRC site was not significantly different from the one at the REF
site, although on average slightly higher, and significantly higher N2O
emissions were observed at the SRC site, although they were still very low.
Measurements carried out during fertilization events showed no
significant increase in the emission rates of N2O compared to
non-fertilization periods: fluxes at the SRC site during the
single instance of fertilization that occurred in the 2 years of study remained low, and at
the REF site none of the four measurements taken during the
fertilization event of June 2012 exceeded the detection limit of the GC.
Monthly averages of FCO2 at the REF and SRC sites
(top panel). The bottom panel shows monthly averages of air temperature
(Tair) and soil water content (SWC) at 30 cm depth. In both subplots
dotted lines are used for the SRC site and continuous lines for the REF site,
while in the bottom panel SWC is in grey and the Tair in black.
Laboratory incubations
The N2O emissions determined in laboratory incubations confirmed that
for most of the analysed WFPS percentage values, both soils were producing little
N2O without N addition, even at a WFPS percentage normally considered to
trigger N2O emission (WFPS: 60–80 %; Fig. 5). Addition of N did
not seem sufficient to stimulate N2O production. In contrast, a very high
WFPS percentage, close to saturation, was able to trigger a strong increase in N2O production in the soil of the REF site. Comparing the data reported
in Fig. 5 with the field data of WFPS percentage for the REF site (Fig. 6), it can
be seen that, most of the time, WFPS percentage was significantly below 70 % in the
whole profile and that at 5 cm, where most of the interaction with added
fertilizer might have occurred, the WFPS never exceeded 50 %.
Mineralization and nitrification rates were quite low at both sites, with
slightly positive mineralization rates at the SRC site (0.28 ± 0.05 µg N g-1 d-1) and a very small net
immobilization in the REF samples (-0.2 ± 0.2 µg N g-1 d-1). Net
nitrification rates calculated in the control (no N addition) were also
quite low and varied between 0.5 ± 0.05 and -0.1 ± 0.2 µg N g-1 d-1 at the REF site, which might suggest either quite a slow ammonification phase as a limiting step of the nitrification or a
slow nitrification rate. However, when ammonium sulfate was added to soil
samples, the potential nitrification rates significantly increased, reaching
1.8 ± 0.1 µg N g-1 d-1 and 1.4 ± 0.3 µg N g-1 d-1 at the SRC and the REF sites
respectively and suggesting that mineralization might be the limiting step of subsequent
nitrification and denitrification processes in the field.
Fluxes of soil N2O (crosses) and CH4 (circles) at the
SRC (a–c) and the REF (b–d) sites. Each marker represents the average
of the nine chambers, with bars indicating their standard deviation. First
letter of month on the x axis.
Emissions due to management
The GHG emissions due to management practices were, in total,
100.9 ± 20 g CO2eq m-2 for the SRC site and
135.7 ± 27.1 g CO2eq m-2 for the REF site. Analysing the
individual contributions, differences arose between the two sites (Fig. 7):
among the field operations, fertilization was the main source of GHG
emissions at the REF site and one of the less important sources at the SRC
site. Irrigation constituted a big portion of the GHG emissions from
management operations at the SRC site, while at the REF site, despite similar
amounts of water provided, irrigation played a smaller role, similar to
harvesting and tillage. Emissions due to the latter were more relevant at the
REF site than at the SRC site.
SOC content changes
In the first 15 cm of soil, total C stocks were 1603 ± 376 g C m-2
at the REF site, 1169 ± 442 g C m-2 at the SRC site, and
1403 ± 279 g C m-2 at the O_SRC site. The
statistical analysis performed on the SOC stocks showed that there were
statistically significant differences between the SOC data of the three sites
(Table 5; p value = 2.05 × 10-7). The linear regression between the SOC
content of SRC and O_SRC sites led to the relation
SOC(t)=78×t+857,
where t is the years from plantation and SOC is the soil organic carbon content
expressed in grams of C per square metre. Estimated uncertainty was 25 g C m-2 for the
slope value, and 139 g C m-2 for the intercept (Fig. 8), meaning that
the yearly SOC accumulation after poplar plantation was 78 ± 25 g C m-2 and the initial value (t= 0) was 857 ± 139 g C m-2, 746 ± 858 g C m-2 lower than the REF value and corresponding to the SOC content loss due to the installation of the SRC. As
this loss was a positive flux occurring only once in a LUC at the
installation of the cuttings (Arevalo et al., 2011) and as the expected
lifespan of the SRC site was 12 years, the value considered for the 24-month
GHG budget was 1/6, corresponding to 124 ± 143 g C m-2
(455 ± 524 g CO2 m-2).
N2O fluxes from incubation experiment reported as a function of
the water-filled pore space estimated for each individual replicate. In (a) data
from samples taken at the SRC site are shown; in (b) data from REF site
samples are shown.
Soil characteristics of the layer 0–15 cm. SRC and REF as
previously defined; SOC: soil organic carbon; ρBULK: bulk
density.
Site
Variable
Value ± SD
REF
C (%)
1.46 ± 0.34
ρBULK (Mg m-3)
1.00 ± 0.11
SOC (Mg C ha-1)
16.03 ± 3.76a
SRC
C ( %)
1.05 ± 0.40
ρBULK (Mg m-3)
1.12 ± 0.15
SOC (Mg C ha-1)
11.69 ± 4.42b
O_SRC
C ( %)
1.38 ± 0.27
ρBULK (Mg m-3)
1.02 ± 0.11
SOC (Mg C ha-1)
14.03 ± 2.79c
a–c Indicate statistically significant differences
between the means of SOC.
Biomass use and GHG offset
The dry weight of AGB at the REF site totalled
0.72 ± 0.18 kg m-2 for the grassland, of which
0.35 ± 0.07 kg m-2 was due to the mix of clover and ryegrass and
0.37 ± 0.17 kg m-2 came from the sorghum; winter wheat totalled
0.63 ± 0.09 kg m-2, of which 0.36 ± 0.05 kg m-2 was
in the ears. The C content measured was 46 % for all species, leading to
a total of 621.0 ± 93.2 g C m-2 in AGB, of which
265.5 ± 79.2 g C m-2 was ingested by sheep on-site,
191.2 ± 49.8 g C m-2 was used by livestock off-site, and
163.9 ± 21.9 g C m-2 was converted to food. The estimated
emissions of CH4 due to enteric fermentation totalled 4.3 ± 1.3 g
CH4 m-2, equal to 3.3 ± 1.0 g C m-2 emitted as
CH4 and thus corresponding to 109 ± 33 g CO2eq m-2
(ECH4,on, Eq. 4). Hence, about 1.25 % of the
ingested C became CH4 in the digestive process. Using this ratio led us
to estimate another 2.4 ± 0.6 g C m-2 emitted as CH4
off-site, i.e. 3.2 ± 0.8 g CH4 m-2 or 80 ± 20 g
CO2eq m-2 (ECH4,off). Subtracting the C
emitted as CH4 on- and off-site from the respective digestible C
ingested by sheep and other livestock led to 621 ± 189 g
CO2eq m-2 emitted on-site (ECO2,on) and
447 ± 118 g CO2eq m-2 emitted off-site. The sum of this
latter emission value with the emissions expected from wheat ear use
(601 ± 80 g CO2eq m-2) gave a total off-site emission
(ECO2,off) of 1048 ± 143 g CO2eq m-2. In total, emissions were 1858 ± 240 g CO2eq m-2 in 2
years (FEXP, Eq. 4).
For the SRC site, applying Eq. (5) with the diameter distribution led us to
estimate AGWB (dry matter) as 0.62 ± 0.29 kg m-2, which, with a C
content of 49 %, corresponded to an FEXP of 1118 ± 521 g CO2eq m-2 in 2 years that is expected to be re-emitted
to the atmosphere at combustion. This value of AGWB then corresponded to
8.1 ± 3.7 MJ m-2 of gross energy from biomass chips, which
decreased to 6.8 ± 3.1 MJ m-2 of final heat obtainable from
burning biomass chips when the conversion efficiency is considered. This
could offset about 379.7 ± 175.1 g CO2eq m-2 from final heat
produced using natural gas.
WFPS percentage at the REF site at three different depths for the
24-month integration periods. Dashed line indicates the threshold (70 %)
above which N2O is released during lab incubation. First letter of month
on the x axis.
GHG budgets
All the contributions reported in the previous sections were summed to
calculate the GHG budgets of the two sites. The net GHG budget of the REF
site (BREF, Eq. 2) amounted to 156 ± 264 g
CO2eq m-2, indicating that the REF site was close to neutrality
from a GHG perspective, while for the SRC site, the BSRC (Eq. 1)
resulted in a cumulative sequestration of -2202 ± 792 g
CO2eq m-2. The different components of the GHG budget of the two
sites are summarized in Fig. 9. At the REF site, the FCO2,
weighing about 48 % in the GHG budget, was completely compensated for by
the emissions of CO2 and CH4 due to the biomass utilization (about
44 and 5 % respectively), while the other components had a minor role
(FMAN around 4 %, soil non-CO2 < 1 %).
FCO2 was the main contribution also at the SRC site, where it
represented 63 % of BSRC, while FEXP represented
20 %. The SOC loss and the GHG offset for the fossil fuel substitution
represented 8 % and 7 % respectively, while the other contributions
played a minor role. As BREF was almost neutral and the SRC site a
sink of GHGs, the difference between the two GHG budgets was favourable at
the SRC site (2358 ± 835 g CO2eq m-2 saved), highlighting
the advantages in terms of GHGs of the LUC from common agricultural land to
SRC of poplar in the study area.
GHG emissions of the different farming operations. Harv: harvesting; plow: ploughing;
sow: sowing; irr: irrigation; fert:
fertilization; other: minor contributions. SRC and REF as previously
defined.
Discussion
The two ecosystems behaved differently in the measuring period: they were
both characterized by a seasonal uptake of CO2 (Fig. 3), driven by the
timing and duration of the growing season (in spring at the REF
site and in summer at the SRC site). The peak of CO2 uptake was similar
at both sites in 2012, while it was higher at the REF site in 2013. Periods
with positive CO2 fluxes were longer at the REF site and often higher
in magnitude, likely as a consequence of the shorter growing season of
grasses and winter wheat compared to the poplar trees of the SRC site. The land cover of the two sites during the dormant periods and the shift in
time between them may also have played a role in this difference: some
herbaceous vegetation continued to grow at the SRC site in wintertime, while
harvesting and ploughing at the REF site in late summer and early fall may
have enhanced ecosystem respiration. Interannual differences were also
observed at both sites. Both the higher air temperature and the more
extended period of low SWC proved the strong aridity of summer 2012,
responsible for the autumnal increase in CO2 uptake at the SRC site,
which corresponded to the rewetting of the soil. At the REF site, autumn uptake
was higher in 2011, while the springtime uptake was much higher in 2013 than
in 2012 (Fig. 3). This different behaviour was mostly ascribable to the
different cultivations (grassland and winter wheat) and to some extent to
the different climate conditions in springtime. All these differences in
ecosystems responses resulted in a net sink of GHGs from the SRC site and in
a neutral GHG balance for the REF site.
Regression line of SOC content in time t (years). The gap between
SOC(0) and SOC content measured at the REF site represented the loss of SOC for the
land use change. Est: estimated values; meas: measured values; SRC and
REF as previously defined; O_SRC is the older short-rotation
coppice site used to build the regression.
A GHG balance not significantly different from 0 is in agreement with the
average results for a set of sites in Soussana et al. (2007), where, however, management costs were not considered and on-site CO2 emissions from
grazing animals were measured with EC. C sequestered by the SRC site in our
study was higher than that of the Belgian site in the study of Zona et al. (2013). In the latter study, the net budget was positive (for a time span of 1.5
years) with a net emission of 280 ± 80 g CO2eq m-2, due
to both the higher emission rates of CH4 and N2O fluxes from soil
(350 ± 50 g CO2eq m-2) and to the lower CO2 sink
(-80 ± 60 g CO2eq m-2) compared to the present
study. Jassal et al. (2013) also found lower FCO2 in a 3-year-old poplar
SRC in Canada (-293 g CO2 m-2 yr-1) compared to the FCO2 of the SRC site of the present study (root age: 4 years), likely
due to the lower stem density of their site. All these values lay in the
range found by Arevalo et al. (2011), i.e. -77 and -4756 g CO2 m-2 yr-1 relative to a
2-year-old and 9-year-old poplar SRC respectively. These results show that
even in a Mediterranean area, where plants are subjected to drought stress, there is the potential for a positive effect
on climate mitigation with a proper use of irrigation.
Several studies (Grigal and Berguson, 1998; Price et al., 2009) confirmed
that converting agricultural land to SRC resulted in an initial release of
SOC due to SRC establishment and then in a slow and continuous accumulation
of SOC due to vegetation activity and wood encroachment (Arevalo et al.,
2011). Despite the deep tillage during SRC establishment and despite the fact that
the REF site was ploughed every year at different depths, a gradient
decreasing with depth in the C distribution of the vertical profile was
evident at the three sites (not shown). This suggests that the changes in
SOC were attributable to the plantation of the SRC only because of the effects
of tillage (Anderson-Teixeira et al., 2009) and not to the mechanical
redistribution of SOC. This study indicates an SOC loss of 47 % compared to the value measured at the REF site, due to the installation of poplar
cuttings. This loss was not measured at the time it occurred, i.e. right
after the conversion of arable land to poplar short-rotation coppice, but
was estimated with data from the O_SRC site. The reported
value was close to the range maximum reported in the review by Post and
Kwon (2000; 20–50 %) but was higher than the results found by Arevalo
et al. (2011; 7 %). The absolute value, however, was close to the one of
this latter study (8 Mg C ha-1), where the initial SOC was 1 order of magnitude higher (114.7 Mg C ha-1). To correctly interpret
this rapid loss of SOC for a conversion of a cropland to an SRC the low
degree of disturbance that characterized the REF site must be taken into
account. Furthermore, the loss of SOC found in the present study has to be
considered along with its own uncertainty that was as large as the estimated
value: for the purposes of the GHG balance, where the uncertainty of the
single components are propagated to the net budget, this result is correctly
interpreted as a range. We highlight that a loss of SOC close to the minimum
of the abovementioned range by Post and Kwon (2000), e.g. 321 g C m-2,
would have changed BSRC (-2202 ± 792)
by only -259 g CO2eq m-2. Thus, even if a measured value would probably have been more accurate, the sensitivity of the total GHG budget to
this loss was shown to be relatively low. The estimated annual SOC
accumulation rate was in the range of that reported by Don et al. (2012) for SRCs
(0.44 ± 0.43 Mg C ha-1 y-1), which explained how the
frequent harvest of above-ground biomass was likely to facilitate the die-off of the roots that contributes to SOC accumulation. In our study, the low
biomass yield supports the hypothesis that a large fraction of C taken up via
photosynthesis was transferred to roots and soil. In our study the
break-even point, where the initial SOC content would be restored and a net
SOC accumulation would start, was 10 years, in agreement with findings from
other studies (e.g. Hansen, 1993, and Arevalo et al., 2011, found a value of 7
years, while Grigal and Berguson, 1998, calculated a break-even point of 15
years). This result, not directly involved in the 24-month GHG budget, is
relevant, considering that the SRC of the present study is expected to be
used for 12 years, thus enough to make the complete recovery of the SOC
loss that occurred at the plantation possible. Different previous land uses, soil types
(in particular clay content), climate conditions, fertilization rates may be
the main causes of differences between studies, as shown in a meta-analysis
by Laganière et al. (2010).
GHG balances of the SRC and the REF sites: components (left) and
net (right). FCH4 and FN2O from soil are negligible and not
included in the graph. FMAN: management; ECH4: exported biomass
re-emitted as CH4 by enteric fermentation; ECO2: exported biomass
re-emitted as CO2 by sheep respiration; FSOC: initial SOC change
at the installation of cuttings; FSAV: GHG savings for replacement of
fossil fuel use; FCO2 as previously defined.
Our results showed that CH4 and N2O soil fluxes were not relevant
in the GHG budgets due to the combination of soil characteristics and
climatic trends at both sites. Low values are reported in other studies for
SRCs: for example, Gauder et al. (2012) found that the soil of different energy crops
acted as a weak sink of CH4 even in the case of fertilization, while
emissions of N2O turned out to be higher for annual than for perennial
(willow) crops, which showed no significant effect of fertilization on
N2O fluxes. Agricultural sites usually have higher N2O effluxes
from soil, though their magnitude depends on cultivations and on management
practices, as shown by Ceschia et al. (2012). The SRC site as a perennial
woody crop was subjected to low soil disturbance during its lifespan, while
the REF site was ploughed once per year, which had an impact on the ecosystem
respiration. Zona et al. (2013) found high N2O emissions in the first
growing season of a poplar SRC in Belgium: 197 ± 49 g CO2eq m-2 in 6 months, which drastically decreased to
42 ± 17 g CO2eq m-2 for the whole following year. This
suggested an influence of soil disturbance during land conversion on the
stock of N in soil, which was almost 1/3 lower in our study sites than in
that of Zona (9.1 ± 2.1 Mg N ha-1). In the present experiment, however, N2O fluxes were low both at the SRC and REF sites, even during
periods of fertilization, with no clear patterns. The low N2O fluxes
were confirmed by laboratory analyses, as the presence of extra N did not
affect the emission rates of N2O, and only very high WFPS percentage could
trigger significant N2O fluxes. The conditions needed of soil humidity
were never reached at the REF site and persisted only for a few days at 35 cm depth at the SRC site (Fig. 6). At this depth fertilizer was added as
fertigation at the SRC site: we hypothesize that the very low porosity, the
compaction, and the strength of the soil might have favoured slow gas release and
further N2O reduction, thus leaving little N2O to escape to the
atmosphere from soil surface. At the REF site, winter fertilization was also
associated with low temperatures, a further constraint on microbial
activity. These results provide further evidence of how the simple
application of the IPCC N2O emission factor to the analysed systems
might have led to an overestimation of the field GHG contribution to the
overall GWP at both sites. Laboratory estimates of mineralization and
nitrification rates suggested that N mineralization might be the limiting
process of the chains of mineral N microbial transformations, which
contributed to keeping N2O emissions low even during events of intense
rainfall and soil saturation. The clay content and compaction of the
analysed soils might be an important factor in limiting oxygen and substrate
diffusion, which are both necessary to have optimal rates of soil organic
matter mineralization. From a methodological point of view, the low
emissions of both CH4 and N2O from soil also suggest that using
four samples of gas concentration per chamber instead of three would not have dramatically improved the accuracy of the calculated fluxes, as a slight
variation in the slope would have not induced significant changes in the
results. The relevance of this result lies in the fact that fertilizing a
poplar SRC in a Mediterranean area and in this kind of soil does not
necessarily lead to increased emissions of N2O, with the requirement
that the correct equilibrium is found between irrigation and WFPS percentage.
Thus, it is possible to maximize yield and GHG mitigation with the right
management practices (Nassi o Di Nasso et al., 2010). CH4 and N2O
fluxes might have been enhanced by the land conversion in the first period
of cultivation of the SRC site, as found for CO2. However, measurements
carried out at the REF site, ploughed every year, and the incubation
experiment showed very low fluxes, mostly related to soil characteristics
and not to management activities. Thus, a low sensitivity of the total GHG
budget to these components can be expected.
Other components of the GHG budget related to N compounds (e.g. aerosol
NH4NO3, N deposition and leaching) were considered negligible in
this study compared to the role of N2O emissions from soil and
related to fertilizer production.
Regarding the use of biomass, comparisons with other studies for the REF site
are complicated because half of the field was converted to sorghum in spring
to compensate for the low productivity experienced during the drought.
However, the productivity of a mixture of clover and ryegrass was found to be
highly variable by Martiniello (1999), and the results of the present study
are comparable with the lower values found by this author in non-irrigated
stands in a Mediterranean climate (0.39 kg m-2). Sorghum productivity
was lower than that reported by Nassi o Di Nasso et al. (2011; around
0.75 kg m-2) in a similar climate, likely due to the short period of
cultivation and to grazing. The productivity of winter wheat was similar to
that in Anthoni et al. (2004; 0.32 ± 0.03 kg m-2). The drought
in summer 2012 had an important influence on the AGWB of the SRC site, which
was lower compared to other studies (e.g. Scholz and Ellerbrock, 2002; 0.4 to
0.7 kg m-2 yr-1) and to the FCO2 values found
with EC. Our hypothesis is that the period of drought influenced the
above-ground/belowground ratio and that the herbaceous vegetation
contributed to the increase in FCO2. In terms of C, the
difference FCO2–FEXP represents, to a first
approximation, the C stocked by each ecosystem that does not return shortly
to the atmosphere after utilization minus heterotrophic respiration (Rh).
While at the SRC site that difference was negative (C sink of 650 g
C m-2), the REF site acted as a small source of C (120 ± 98 g
C m-2). Small sources were also found by Anthoni et al. (2004; between
50 g C m-2 and 100 g C m-2), while Aubinet et al. (2009)
reported a 4-year rotation crop as a source of 340 g C m-2. For
poplar, Deckmyn et al. (2004) found a similar behaviour in a poplar SRC in
Belgium. Concerning the fraction of the exports that is emitted as CH4
from enteric fermentation, our estimates were in agreement with those of
Dengel et al. (2011). Several studies (e.g. Gilmanov et al., 2007) used EC to
measure CO2 and CH4 fluxes from grazed systems. Some included only
FCO2, FCH4 and FN2O in
the GHG budget and created a C budget for lateral fluxes such as biomass
export (e.g. Allard et al., 2007). However, the EC method is not capable of
measuring point sources of trace gases moving inside and outside the
footprint (data discarded by QA–QC (quality-assurance–quality-control)
procedures: see also Baldocchi et al., 2012). Thus, we adapted the method
described in Soussana et al. (2007) for off-site emissions, extending it also
to on-site emissions, to include the effects of above-ground biomass use in
the GHG budget.
Different studies (e.g. Cherubini et al., 2009; Djomo et al., 2013)
confirmed the advantages of using biomass from SRC over fossil fuels in
mitigating the increase in atmospheric GHG concentrations, while Abbasi and
Abbasi (2010) found that the SRC management led to GHG emissions that
compensate for the gain due to the fossil substitution. The low yield of the SRC
site led to lower GHG savings compared to those found by Cherubini et al. (2009) for the production of heat from woody products (379.7 ± 175.1 g CO2eq m-2 in 2 years compared to 600 g CO2eq m-2
per year). Cherubini et al. (2009) found GHG mitigation to be directly proportional to
crop yield for dedicated bioenergy crops. From a GHG budget perspective,
however, the yield is also proportional to C emissions from combustion and
correlated with FCO2. The same study reported GHG savings of other
bioenergy systems, showing that the performance of wood-based systems is
lower in terms of GHG offset than that of other bioenergy crops, e.g.
switchgrass (1300 g CO2eq m-2 yr-1), Miscanthus (1600 g CO2eq m-2 yr-1), and fibre sorghum
(1800 g CO2eq m-2 yr-1). In the present study the role of GHG offset was
relevant in the GHG balance; however, it is important to consider that natural
gas, while being the most used fossil fuel for heating systems in Italy, also has a lower carbon intensity for heat production (55.862
g CO2eq MJ-1) compared to coal (76.188 g CO2eq MJ-1)
and oil (73.693 g CO2eq MJ-1; Romano et al., 2014). A different
scenario, where biomass would substitute the use of other energy sources
with higher emission factors (such as coal) would lead to a higher GHG offset.
Our study confirmed that farming operations only have a limited importance
in the overall GHG budget when the conditions of relevant CO2 uptake by
vegetation are met, and the values we found were similar to the ones found
by Gelfand et al. (2011). At the SRC site, irrigation was more important than
other contributions and caused more emissions than irrigation at the REF
site. This suggests that belowground irrigation was less efficient in terms
of GHG emissions than the sprinkler. Fertilizers and other chemical products
often have a higher impact on the GHG balance compared to other field
operations due to the off-site GHG emissions (Ceschia et al., 2010). At the
study sites the amount and frequency of applications were relatively small,
and this explains the minor role of fertilization in the total GHG budget.
Thus, the importance of farming operations can vary from year to year,
depending on climate conditions and on farmer decisions.
This study reports on the GHG budget of poplar SRC in Mediterranean areas.
However, when considering the implications of SRC in a wider perspective,
other factors should also be considered to assess the overall sustainability
of this type of LUC. Among them, irrigation is one of the most important
(Dougherty and Hall, 1995), as poplar cultivations in a Mediterranean climate
require considerable amounts of water. In the LUC presented here, both the
SRC and the REF sites were irrigated with similar amounts of water, using a
less efficient technique at the REF site (sprinkler system) than at the SRC
site (belowground drip system; e.g. Camp, 1998). The impact of the LUC on
the local water balance is thus expected to be small in this particular
case but not in general. An appropriate design of these systems is also
crucial to avoid water dispersion: in the present study we observed that
irrigation could not compensate for the drought stress experienced by the SRC
site in 2012; thus, concerns arise regarding the proper location of the belowground
tubes and regarding the amounts of water applied. The aim of this study was to
analyse the LUC from a GHG perspective at a farm level. The boundary of our
system constituted the main difference from a full LCA analysis, where the
iLUC is considered in addition to the direct land use change. iLUC can cause
GHG emissions elsewhere, thus reducing the mitigation potential of the
studied SRC on a global scale.