Carbon dioxide exchange of a perennial bioenergy crop cultivation on a mineral soil

Biogeosciences, 13, 1255–1268, 2016 www.biogeosciences.net/13/1255/2016/ doi:10.5194/bg-13-1255-2016 © Author(s) 2016. CC Attribution 3.0 License. Carbon dioxide exchange of a perennial bioenergy crop cultivation on a mineral soil Saara E. Lind1, Narasinha J. Shurpali1, Olli Peltola2, Ivan Mammarella2, Niina Hyvönen1, Marja Maljanen1, Mari Räty3, Perttu Virkajärvi3, and Pertti J. Martikainen1 1Department of Environmental Science, University of Eastern Finland, Yliopistonranta 1 E, P.O. Box 1627, Kuopio Campus, 70211, Finland 2Department of Physics, P.O. Box 48, 00014 University of Helsinki, Helsinki, Finland 3Natural Resources Institute Finland, Green Technology, Halolantie 31 A, 71750 Maaninka, Finland Correspondence to: Saara E. Lind ([email protected]) Received: 4 September 2015 – Published in Biogeosciences Discuss.: 19 October 2015 Revised: 11 February 2016 – Accepted: 13 February 2016 – Published: 1 March 2016 Abstract. One of the strategies to reduce carbon dioxide (CO2)emissions from the energy sector is to increase the use of renewable energy sources such as bioenergy crops. Bioenergy is not necessarily carbon neutral because of greenhouse gas (GHG) emissions during biomass production, field management and transportation. The present study focuses on the cultivation of reed canary grass (RCG, Phalaris arundinacea L.), a perennial bioenergy crop, on a mineral soil. To quantify the CO2exchange of this RCG cultivation system, and to understand the key factors controlling its CO2exchange, the net ecosystem CO2exchange (NEE) was measured from July 2009 until the end of 2011 using the eddy covariance (EC) method. The RCG cultivation thrived well producing yields of 6200 and 6700kgDWha−1in 2010 and 2011, respectively. Gross photosynthesis (GPP) was controlled mainly by radiation from June to September. Vapour pressure deficit (VPD), air temperature or soil moisture did not limit photosynthesis during the growing season. Total ecosystem respiration (TER) increased with soil temperature, green area index and GPP. Annual NEE was −262 and −256gCm−2 in 2010 and 2011, respectively. Throughout the study period from July 2009 until the end of 2011, cumulative NEE was −575gCm−2. Carbon balance and its regulatory factors were compared to the published results of a comparison site on drained organic soil cultivated with RCG in the same climate. On this mineral soil site, the RCG had higher capacity to take up CO2from the atmosphere than on the comparison site. 1 Introduction Anthropogenic increase in the atmospheric concentration of greenhouse gases (GHGs) has been considered the major reason for the global climate warming (IPCC, 2013). The carbon dioxide (CO2)concentration in the atmosphere has increased from 278 to 391ppm between 1750 and 2011 and is still increasing (IPCC, 2013). Carbon dioxide emitted to the atmosphere originates mainly from respiration (plants and microorganisms) and fossil fuel combustion with the main sinks being photosynthesis and oceans (IPCC, 2013). In Finland the energy sector and agriculture are the most important in the total national GHG emissions (Statistics Finland, 2014). One of the strategies to reduce CO2emissions from the energysector is toincrease theuse of renewable energysources, e.g. using biomass. Bioenergy produced from biomass is not necessarily carbon neutral because of GHG emissions during biomass production, field management and transportation. Life-cycle assessment (LCA) results have been recently reported for reed canary grass (RCG, Phalaris arundinacea L.) cultivation on cut-away peatlands in Finland (Shurpali et al., 2010) and Estonia (Järveoja et al., 2013). In these studies, the RCG sites were net sinks for CO2and hence, RCG is suggested to be a good after-use option for such marginal soils which are known to release large amount of CO2as a result of decomposition of residual peat, when left abandoned (Kasimir-Klemedtsson et al., 1997). Cultivation of RCG has been popular in Finland since the mid-1990s and at the peak approximately 19000ha (2007 and 2008) were cultivated with RCG. However, owing to Published by Copernicus Publications on behalf of the European Geosciences Union. 1256 S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation technical difficulties with the burning of the RCG biomass in combustion plants, the scope of RCG as a source of biomass bioenergy has declined in the last few years. In 2014, the average cultivation area was around 6000ha. Nevertheless, the scope for RCG as a source of liquid biofuel, a digestate in biogas plants, oil spill absorption and a buffer crop between terrestrial and aquatic landscape is wide (Pasila and Kymäläinen, 2000; Partala et al., 2001; Powlson et al., 2005; Kandel et al., 2013b). RCG is a perennial crop which is well adapted to the northern climatic conditions. It has a rotation time of up to 15 years. The annually harvested yield up to12000kgDWha−1 has been reported (Lewandowski et al., 2003). As a perennial crop, it has advantages over theannual cropping systems. The crop growth following the first overwintering starts earlier as the re-establishment of the crop in the spring is not needed. This cultivation style also reduces the use of machinery at the site since e.g. annual tilling is not required. While continuous and long-term measurements of GHG balance from bioenergy crops are needed to evaluate the atmospheric impact of the whole production chain, to our knowledge, there are no GHG flux measurements from RCG cultivation on mineral soils. With this in view, we measured the CO2balance of RCG crop cultivation (2009–2011) on a mineral soil by the eddy covariance (EC) technique. Our objectives in this paper are to quantify and characterize the net ecosystem exchange (NEE) of a perennial crop cultivated on a mineral soil and to investigate the factors controlling its CO2balance. Additionally, we aim to compare our findings from the mineral soil site to the published data on of a RCG cultivation system on a drained organic soil (referred to hereafter as comparison site) in the same climate region. 2 Materials and methods 2.1 Study site and agricultural practices The study site is located in Maaninka (63◦0904900 N, 27◦140300 E, 89mabove the mean sea level) in eastern Finland. Long-term (30 years, reference period 1981–2010; Pirinen et al., 2012) annual air temperature in the region is 3.2◦C with February being the coldest (−9.4◦C) and July the warmest (17.0◦C) month. The annual precipitation in the region is 612mm with a seasonal amount of 322mm during the May–September period. The experimental site is a 6.3ha (280×220m) agricultural field cultivated with RCG (cv. “Palaton”). During the last 10 years prior to planting of RCG, the field was cultivated with grass (Phleum pratense L.; Festuca pratensis Huds.), barley (Hordeum vulgare L.) or oat (Avena sativa L.). For a detailed soil analysis three 100cm deep soil pits were excavated and eight 6cm deep horizons between 0 and 93cm were sampled in July 2010. Three undisturbed soil samples from each horizon were taken with steel cylinders (height 6.0cm, diameter 5.7cm). Two soil samples were used for determination of soil physical properties and one for the chemical properties. To characterize properties of top soil (0–18cm) in general, soil samples taken at depths of 0–6, 6–12, 12–18cm were analysed separately, and mean values over the horizons were calculated for each pit. The results shown here are means (±standard deviation) over the three pits. The soil samples were oven dried at 35◦C and ground to pass through a 2mm sieve. The particle-size distribution was determined with the pipette method (Elonen, 1971). Total organic C and total N contents were determined by dry combustion using a Leco®analyser, the soil particle density with a stoppered bottle pycnometer method and bulk density was calculated as a ratio of the dry weight (oven dried at 105◦C) and sampling volume of the soil. Soil pH and electrical conductivity were measured in soil–water suspension (1:2.5 v/v). The easily soluble P and exchangeable K were extracted with acid ammonium acetate at pH4.7, as described by Vuorinen and Mäkitie (1955). The soil was classified as a Haplic Cambisol/Regosol (Hypereutric, Siltic) (IUSS Working Group WRB, 2007), the topsoil being generally silt loam (clay mean 25±5.6%, silt 53±9.0% and sand 22±7.8%) based on the US Department of Agriculture (USDA) textural classification system. The average soil characteristics in the topsoil were as follows: pH (H2O) 5.8±0.19, electrical conductivity 14±2.4mSm−1, soil organic matter 5.2±0.90%, organic carbon 3.0±0.52%, total nitrogen 0.2±0.03%, C:N ratio 15±0.4, the acid ammonium acetate extractable K 104±12.9mgL−1soil, P 5.4±1.28mgL−1 soil, particle density 2.65±0.014gcm−3and bulk density 1.1±0.11gcm−3. Based on the soil moisture retention curve, the field capacity was 39.7±1.2% (soil moisture (v/v)) and wilting point was 21.6±0.8% (soil moisture (v/v)). In the beginning of June 2009, the sowing of RCG was done with a seed rate of 10.5kgha−1together with the application of a mineral fertilizer (60kgNha−1, 30kgPha−1and 45kgKha−1). The field was rolled prior to and after sowing. Additional sowing was done to fill the seedling gaps in June and July. Herbicide (mixture of MPCA 200gL−1, clopyralid 20gL−1and fluroxypyr 40gL−1, 2L in 200L of waterha−1)was applied by the end of July 2009 to control the weeds. Mineral fertilizer was applied as surface application in spring 2010 (70kgNha−1, 11kgPha−1and 18kgKha−1)and spring 2011 (76kgNha−1, 11kgPha−1 and 19kgKha−1). The biomass produced during the first growing season was not harvested but left on the site. During the following years, the harvesting was done in the spring after the growing season (28 April 2011 and 9 May 2012). Thus, the spring 2011 was the first time when the crop was harvested after its establishment in the summer of 2009. As produced biomass was used for burning, keeping the crop at the site over winter is a standard RCG cultivation practise in the Nordic countries, as spring harvesting has been shown Biogeosciences, 13, 1255–1268, 2016 www.biogeosciences.net/13/1255/2016/ S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation 1257 to improve the quality of the biomass for burning (Burvall, 1997). The biomass was harvested using farm-scale machinery. The naturally dried vegetation was cut with a conventional disc mower (without conditioner) to approx. 5cm stubble height, swathed and baled into round bales 1–2 days after cutting. 2.2 Micrometeorological measurements Measurements of CO2, latent heat (LE) and sensible heat (H) fluxes were carried out from July 2009 until the end of 2011 using the closed-path EC method (Baldocchi, 2003). A measurement mast was installed approximately in the middle of the field and the instrument cabin was located about 10m east of the EC mast. The prevailing wind direction was northerly with a 24% occurrence during the study period. The EC instrumentation consisted of an infra-red gas analyser (IRGA) for CO2and water vapour (H2O) concentrations (model: Li-7000 (primary) or Li-6262 (backup), LiCor) and a sonic anemometer (model: R3-50, Gill Instruments Ltd, UK) for wind velocity components and sonic temperature. The mast height was 2, 2.4 or 2.5m, adjusted according to the vegetation height. Except for the wind sector from 85 to 130◦downwind of the instrument cabin, all wind directions were acceptable because no other obstacles were present and the sonic anemometer in use had an omnidirectional geometry. A heated gas sampling line (inner diameter 4mm, length 8m polytetrafluoroethylene (PTFE)+0.5m metal) with two filters (pore size 1.0µm, PTFE, Gelman®or Millipore®) was used to draw air with a flow rate of initially 6Lmin−1(until 31 March 2011). Subsequently, a flow rate of 9Lmin−1was used. The IRGA was housed in a climate-controlled cabin. Reference gas flow, created using soda lime and anhydrone, also fitted with a Gelman®filter, was 0.3Lmin−1. The IRGA was calibrated approximately every second week with a twopoint calibration (0 and 399µLL−1of CO2, AGA Oy, Finland) and additionally with a dew point generator (model: LI-610, LiCor) for H2O mixing ratio during conditions when air temperature was above +5◦C. Data collection was done at 10Hz using the Edisol program (Moncrieff et al., 1997). The 30min EC flux values were calculated from the covariance of the scalars and vertical wind velocity (e.g. Aubinet et al., 2000). Data processing was done using EddyUH post-processing software (Mammarella et al., 2016). Despiking was done by defining a limit for the difference in subsequent data points for CO2 (15µLL−1)and H2O (20mmolmol−1)concentrations, wind components (u=10ms−1,v=10ms−1and w=5ms−1) and temperature (5◦C). A data point defined as a spike was replaced with the previous value. Point-by-point dilution correction was applied after the despiking. Two-dimensional coordinate rotation (mean lateral and vertical wind equal to zero) was done on the sonic anemometer wind components. Angle of attack correction was not applied. Detrending was done using block-averaging. Lag time due to the gas sampling line was calculated by maximizing the covariance. Low-frequency spectral corrections were implemented according to Rannik and Vesala (1999). For high-frequency spectral corrections, empirical transfer function calculations were done based on the procedure introduced by Aubinet et al. (2000). Humidity effects on sonic heat fluxes were corrected according to Schotanus et al. (1983). From the processed data, flux values measured when winds were from behind the instrument cabin and those during rain events were removed. The available flux data were further quality controlled using filters as follows. We plotted the nighttime NEE with u∗and found no correlation between the two. Nevertheless, a default u∗filter of 0.1ms−1was used. Flux was considered non-stationary following Foken and Wichura (1996). In this paper, we used a limit of 0.4 (e.g. 40% difference between the sub-periods and the total averaging period). Both skewness and kurtosis of the data were checked and the acceptable skewness range was set from −3 to 3 and kurtosis from 1 to 14. Overall flags (according to Foken et al., 2004) higher than 7 were removed. Finally, the data were visually inspected. From the available data, approximately 30% of the CO2and Hflux data and 40% of the LE flux data were rejected. The random errors of 30min averaged and quality-controlled CO2fluxes were determined following Vickers and Mahrt (1997). The random error was 13, 12 and 14% during July–September 2009, May–September 2010 and May–September 2011, respectively. Footprints were calculated for each 30min averaging period with the analytical footprint model developed by Kormann and Meixner (2001). The model is valid within the surface layer and it utilizes power law profiles for solving the footprint sizes analytically in a wide range of atmospheric stabilities. Based on the analysis, 80% of the flux was found to originate from within 130m radius of the mast. The data gap filling and flux partitioning was done using the online tool (http://www.bgc-jena.mpg.de/~MDIwork/ eddyproc/index.php). This gap-filling method considers both the co-variation of the fluxes with global radiation, temperature and vapour pressure deficit (VPD) and temporal autocorrelation of the fluxes (Reichstein et al., 2005). Flux partitioning was done excluding gap-filled data. Total ecosystem respiration (TER) was defined as the night-time measured net ecosystem CO2exchange (NEE). The regression between night-time NEE and air temperature (T) was calculated using an exponential regression model (Lloyd and Taylor, 1994) of the form R(T)=RrefeE0(1 Tref−T0−1 T−T0)(1) where T0= −46.021◦C, Tref =10◦C and fitted parameters were Rref (the temperature independent respiration rate) and E0(temperature sensitivity). Using the model outputs for Rref and E0, the half-hour TER was estimated using the measured air temperature. Finally, gross photosynthesis (GPP) www.biogeosciences.net/13/1255/2016/ Biogeosciences, 13, 1255–1268, 2016 1258 S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation was calculated as a difference between NEE and TER. In this paper, CO2released to the atmosphere is defined as a positive value and uptake from the atmosphere as negative. As a final step, the EC measurements were validated using the energy balance closure (EBC) determined as the slope of the regression between net radiation (Rn) and latent heat (LE), sensible heat (H) and the ground heat flux (G). The EBC is expressed in the following formulation (Arya, 1988) and it is a simplified formula which is valid for ideal surfaces, i.e. with no mass and heat capacity: Rn=LE+H+G(2) The EBC was determined using data from only those 30min time periods when all of the energy components were available. The slope of the regression was 0.70 in May–September 2010 and 2011. Incomplete closure is a common problem due to e.g. large eddies (Foken, 2008), angle of attack issues (Nakai et al., 2006) and also because part of the available energy is also stored in different parts of the ecosystem (Foken, 2008). Therefore, EBC was calculated so that it includes different storage terms, i.e. heat in the soil, crop canopy, amount of energy used in photosynthesis, sensible and latent heat below the EC mast (following Meyers and Hollinger, 2004 and Lindroth et al., 2010) to give a more precise estimation of the EBC. With this approach, the slope increased to 0.75. The obtained EBC is well within the range of EBCs reported for several FLUXNET sites by Wilson et al. (2002). Mauder et al. (2013) suggested that the EBC could be used as a metric for systematic uncertainty in EC fluxes. Based on this approach the systematic uncertainties of the EC fluxes reported in this study were similar to those published in other studies. 2.3 Supporting measurements A weather station was set up close to the EC mast. Height of the weather station mast was adjusted according to the EC mast height. Supporting climatic variables, i.e. net radiation (model: CNR1, Kipp&Zonen B.V.), air temperature and relative humidity (model: HMP45C, Vaisala Inc), photosynthetically active radiation (PAR, model: SKP215, Skye instruments Ltd.), amount of rainfall at 1m height (model: 52203, R.M. Young Company), soil temperature at 5, 10 and 30cm depths (model: 107, Campbell Scientific Inc.), soil moisture at depths of 5, 10 and 30cm (model: CS616, Campbell Scientific Inc.), soil heat flux at 7.5cm depth (model: HPF01SC, Hukseflux) and air pressure (model: CS106 Vaisala PTB110 Barometer) were measured. Data were collected using a datalogger (model: CR 3000, Campbell Scientific Inc.). All meteorological data were collected as 30min mean values (precipitation as 30min sum), except air pressure which was recorded as an hourly mean. Supporting data collection began on 14 August 2009. Short gaps in the data were filled using linear interpolation. If air temperature, relative humidity, pressure or rainfall data were missing for long periods, data from Maaninka weather station, located about 6km to the southeast of the site and operated by the Finnish Meteorological Institute (FMI), were used. The RCG green area index (GA) was estimated following Wilson et al. (2007). Measurements were done approximately on a weekly basis during the main growing period and less frequently in the autumn. Three locations (1×1m2) were selected and within those, three spots (8×8cm2)were used to count the number of green stems (Sn) and leaves (Ln) per unit area. Three plants adjacent to small spots were selected for measurements of green area of leaves (La) and stems (Sa). Following equation was used to calculate GA (m2m−2): GA =(Sn·Sa)+(Ln·La). (3) Leaf area index (LAI) was measured using a plant canopy analyser (model: LAI-2000, LiCor) with a 180◦view cap. The LAI was measured close to GA plots at the same interval and at the same day as GA was estimated. A measurement was accepted when the standard error of LAI was less than 0.3 and the number of above and below vegetation observation pairs was more than 3. Above-ground biomass samples were collected approximately on a monthly basis from three locations in the field during the snow-free season in 2009, 2010 and 2011 (and root samples in 2009 and 2010). Above-ground biomass was collected from a 20×20cm2area. Samples were dried in the oven until (+65◦C) the weight of the samples remained unchanged (approximately 24h) and dry weight (DW) was measured. Root biomass (0–25cm) was sampled from the same areas as the above-ground biomass using a soil corer (diameter 7cm). Living roots (fine and coarse roots) were picked and washed. After drying (+65◦C) for 24h, DW was measured. To analyse the performance of the crop, water use efficiency (WUE) was determined following Law et al. (2002). For this purpose, evapotranspiration (ET) was determined by dividing LE with the latent heat of vaporization (L=2500kJkg−1). Monthly sums of GPP and ET from the May to September period were obtained and WUE was determined as the slope of the linear regression between monthly GPP and ET. The Bowen ratio was calculated from daytime (PAR>20µmolm−2s−1)measured Hand LE fluxes. 2.4 Analysis of environmental factors governing CO2 exchange The relationship between GPP and PAR was examined on a monthly basis from mid-May to September separately for 2010 and 2011. Prior to the analysis, PAR data were binned at an interval of 10µmolm−2s−1. The bin-averaged values of GPP were plotted against PAR and the data were fitted with a rectangular hyperbolic model of the form (e.g. Thornley and Johnson, 1990) GPP =GPmax ·PAR·α GPmax +PAR·α,(4) Biogeosciences, 13, 1255–1268, 2016 www.biogeosciences.net/13/1255/2016/ S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation 1259 where GPmax (µmolm−2s−1)is the theoretical maximum rate of photosynthesis at infinite PAR and αis the apparent quantum yield. Additionally, data with PAR levels greater than 1000µmolm−2s−1were used to study the relationship between GPP and air temperature, VPD and soil moisture. To analyse the relationship between GPP and GA and also LAI, a weekly averaged GPP was constructed for those weeks when the plant variables were available. These data were fitted with a linear regression. To be able to compare the results in detail with the earlier findings on RCG at an organic soil site in Finland (Shurpali et al., 2010) another regression model was used to assess the relationship between TER and soil temperature, nighttime measured NEE (PAR<5µmolm−2s−1)from May to September separately for 2010 and 2011 was used. Prior to the analysis, the data were binned with soil temperature at 2.5cm depth (from 0 to 21.5 with a 0.5◦C interval). The binaveraged values of TER were plotted against soil temperature and the data were fitted with an exponential regression model of the form (e.g. Shurpali et al., 2009) TER =R10 ·Q(Ts/T10) 10 ,(5) where Tsis the measured soil temperature (◦C) at 2.5 depth, T10 =10◦C and the fitted parameters are R10 (base respiration, µmolm−2s−1, at 10◦C) and Q10 (the temperature sensitivity coefficient). To analyse the relationship between TER and vegetation, we constructed weekly means from daily TER values for the weeks during which GA was estimated for 2010 and 2011. To assess the relationship between GPP and TER, daily sums of TER and GPP from May to September separately for 2010 and 2011 were used in the linear regression analysis. 2.5 Comparison site characteristics The comparison site with organic soil has been intensively studied and several papers report results from it (e.g. Shurpali et al., 2008; Hyvönen et al., 2009; Shurpali et al., 2009, 2010, 2013; Gong et al., 2013). The comparison site is located in eastern Finland (62◦300N, 30◦300E, 110m above mean sea level).Long-term (30 years, reference period 1981–2010) annual air temperature in the region is 3.0◦C and the annual precipitation in the region is 613mm. The area was originally an ombrotrophic Sphagnum fuscum pine bog (for more details, see Biasi et al., 2008). From 1976 onwards the site was prepared for peat extraction – i.e. it was drained and the vegetation was removed. Peat extraction was started in 1978. In 2001, when the peat depths were between 20 and 85cm, a 15ha area was sown with RCG (cv. “Palaton”). Since then, the site has been annually fertilized with 50kgNha−1, 14kgPha−1and 46kgKha−1. Lime was added as dolomite limestone (CaMg(CO3)2)at the rate of 7.8tha−1in 2001 and 2006. The average surface peat characteristics were as follows: pH 5.4, bulk density 0.42gm−3and C:N ratio 40.3 (Shurpali et al., 2008). The climatic conditions during the years 2004–2007 at the site were such that the annual air temperature was 2.7, 3.7, 3.1 and 3.2◦C and annual precipitation was 862, 544, 591, 700mm in 2004, 2005, 2006 and 2007, respectively (Hyvönen et al., 2009). During May–September period, the precipitation was 554, 246, 249 and 423mm in 2004, 2005, 2006 and 2007, respectively. The difference to the long-term mean (312 mm) was approximately 20% during the dry years (2005 and 2006) and 36 and 78% during the wet years (2004 and 2007, respectively). Water table level was on average 0.65m, varying from 0.4 to 0.7m during the years (Hyvönen et al., 2009). The volumetric water content (VWC) at 30cm depth was always high and did not vary between the years. The VWC at surface layers (2.5 and 10cm depths) fluctuated in response to precipitation events and ranged from 0.1 to 0.8m3m−3. The biomass at the site was used for burning purpose and, therefore, it was harvested in the spring. The spring-harvested yields were 3700, 2000, 3600 and 4700kgha−1in 2004, 2005, 2006 and 2007, respectively (Shurpali et al., 2009). The CO2exchange was measured using an open-path EC system and details of the measurements and data processing can be found in Shurpali et al. (2009). 3 Results 3.1 Seasonal climate and crop growth The mean annual air temperature at the study site was 3.5, 2.2 and 4.5◦C in 2009, 2010 and 2011, respectively, with the daily means varying from −30.0 to +27.1◦C (Fig. 1a). Annual precipitation was 421, 521 and 670mm in 2009, 2010 and 2011, respectively. In May–September period the precipitation was 40 and 28% lower in 2009 (192mm) and 2010 (228mm) than the long-term mean. Precipitation was about the same as the long-term mean in 2011 (327mm, Fig. 1b). The growing season is defined as having commenced when the mean daily air temperature exceeds 5◦C for 5 consecutive days with no snow and ended when the mean daily air temperature is below 5◦C on 5 consecutive days. Growing season commenced on 1 May 2009, 9 May 2010 and 23 April 2011 and lasted 152, 156 and 182 days in the three consecutive years. The daily averaged VWC ranged from 0.12 to 0.54m3m−3, from 0.09 to 0.37m3m−3and from 0.11 to 0.45m3m−3in 2009, 2010 and 2011, respectively (Fig. 1c). The summer maxima were recorded at 2.5cm depth in July 2010 (20.9◦C) and 2011 (19.1◦C) (Fig. 1d). During the winter 2009–2010 and 2010–2011 the soil temperatures were close to zero. The lowest soil temperatures were recorded at 2.5cm depth in December 2009 (−7.5◦C) and November 2010 (−3.4◦C). The estimated evapotranspiration (ET), was 110, 330 and 370 mm in August–September 2009, May–September 2010 www.biogeosciences.net/13/1255/2016/ Biogeosciences, 13, 1255–1268, 2016 1260 S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation                          !  !    ""# "!"# "#"# "" "!" "#" "" "!" "#" "" $ !  !  !   !    %$       Figure 1. Climatic conditions at the study site during the measurement years. (a) Daily averaged air temperature (◦C) during 2009– 2011, (b) Daily precipitation (mmd−1, grey line) and its cumulative sum (mm, black line) during the growing seasons. (c) Daily averaged volumetric water content (VWC, m3m−3)at 2.5cm (dark grey line), 10cm (light grey line) and 30cm (black line) during the growing seasons, from 14 August 2009 onwards. (d) Soil temperatures (◦C) at the 2.5cm (dark grey line), 10cm (light grey line) and 30cm (black line) depths as daily means from 14 August 2009 until 2 December 2011. and May–September 2011, respectively. During those time periods, the ecosystem used more water than was received through rainfall as the corresponding precipitation amounts were 80, 220 and 320mm in 2009, 2010 and 2011, respectively. A clear linear relationship was found between GPP and ET (adjusted R2=0.73, p<0.01, n=12) during the May–September period in 2010 and 2011. The water use efficiency (WUE) of the RCG cultivation determined from this relationship was 12gCO2per kgH2O. Averaged daytime Bowen ratio was 0.18 and 0.28 during the May–September period in 2010 and 2011, respectively. During the first growing season (2009), the vegetation development was slow and the maximum plant height was low when compared to the subsequent years (0.6, 1.7 and 1.8m in 2009, 2010 and 2011, respectively). In the following years, the initial sprouting in early spring was followed by vigorous plant growth which lasted about 9 weeks. The rapid plant growth resulted in a steep increase in green area (GA) and leaf area indices (LAI) in 2010 and 2011 (Fig. 2b, c). Both                                   !"#$%   &    ' Figure 2. Vegetation parameters determined on the reed canary grass (RCG) cultivation. Approximately monthly determined above-ground (grey bars) and root biomass (hatched bars) in gdryweight (DW) m−2between week 15 and 45 in (a) 2009, (b) 2010 and (c) 2011. Also approximately weekly determined normalized green area index (GA, black dots) and leaf area index (LAI, grey dots) for (b) 2010 and (c) 2011 is shown. GA and LAI levelled off in the beginning of June. The maximum above-ground biomass was recorded at the end of the season (560, 1100 and 1600gDWm−2in 2009, 2010 and 2011, respectively) (Fig. 2a, b and c). The maximum root biomass was 480gDWm−2in 2010 (Fig. 2b). Depending on the sampling occasion, 70 to 80% of the roots were distributed within the 0–10cm depth. The crop yield was 6200 and 6700kgDWha−1in 2010 and 2011, respectively. 3.2 CO2exchange patterns 3.2.1 Measured net ecosystem CO2and energy exchange Measured 30min values of NEE, Hand LE during 2009, 2010 and 2011 prior to the gap filling are shown in Fig. 3. In 2009, the NEE measurements began 45 days after the sowing in mid-June. The maximum amplitude of the diurnal NEE cycle varied from −26 to 20µmolm−2s−1during the growing season in 2009. The amplitude of the diurnal NEE cycle was noticeable around mid-May onwards until November in 2010 and 2011. The maximum amplitude of diurnal NEE cycle varied from −31 to 18µmolm−2s−1and from −37 to 20µmolm−2s−1during the growing seasons in 2010 and 2011, respectively (Fig. 3a). Outside the growing seasons, respiratory losses dominated the net CO2balance. The ecosystem CO2loss was 0.62µmolm−2s−1from October 2009 to mid-May 2010, 0.76µmolm−2s−1during a similar period in 2010–2011 and 1.1µmolm−2s−1for a shorter time period in 2011 (November and December). The diurnal LE cycle had the maximum amplitude during the summer months and ranged from −30 to 400, from 0 to 400 and from 0 to 600Wm−2in 2009, 2010 and 2011, respectively. LE was close to zero during the non-growing season. The amplitude of diurnal Hcycle was at the maximum during the summer months and ranged from −50 to 130, from −100 to 210 and from −100 to 190Wm−2in 2009, 2010 and 2011, Biogeosciences, 13, 1255–1268, 2016 www.biogeosciences.net/13/1255/2016/ S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation 1261 Figure 3. Measured CO2and energy fluxes from July 2009 to December 2011. (a) Net ecosystem CO2exchange (NEE, µmolm−2s−1).(b) Latent heat flux (LE, Wm−2).(c) Sensible heat flux (H, Wm−2). respectively. Hranged from −60 to 20Wm−2during the non-growing seasons. 3.2.2 Diurnal trends To examine the diurnal trends, the data on air temperature, VPD, PAR and NEE in June 2010 and 2011 were averaged to generate half-hour diurnal means (Fig. 4). In both years, June presented conditions of high CO2uptake during the day and of CO2loss at night. Air temperature was lower in 2010 than in 2011 but both years showed typical diurnal patterns with minimum values during early morning hours and maximum values late in the afternoon (Fig. 4a). Similarly, the VPD was lower in 2010 than 2011 (Fig. 4b). The maximum in VPD (0.96kPa) occurred late afternoon in 2010 whereas in 2011 the maximum (0.89kPa) occurred around noon. In both years, the amplitude of diurnal mean of temperature and VPD was moderate. The mean diurnal pattern of NEE was similar between 2010 and 2011 and the patterns were fairly symmetrical (Fig. 4d). During the night-time, from 22:00 to about 02:00 (UTC+2), CO2exchange between the ecosystem and atmosphere was constant and dominated by respiration. Mean NEE during this time was 4.5µmolm−2s−1in 2010 and 6.6µmolm−2s−1in 2011. In the morning hours, with increasing PAR (Fig. 4c), NEE began to decline and the light compensation point occurred at a PAR level of about 200µmolm−2s−1at around 05:00 (UTC+2). After this, the uptake dominated the CO2balance. The peaks in mean NEE occurred around 12:00 (UTC+2) at the same time as the peaks in the mean PAR. The maximum mean NEE in June Figure 4. Mean diurnal variations in June 2010 (open grey triangles) and 2011 (open black circles). (a) Air temperature (◦C). (b) Vapour pressure deficit (VPD, kPa). (c) Photosynthetically active radiation (PAR, µmolm−2s−1).(d) Net ecosystem CO2exchange (NEE, µmolm−2s−1). Data are half-hour means with standard error. was −21 and −23µmolm−2s−12010 and 2011, respectively. With declining PAR levels, the plant CO2uptake also declined. The secondary light compensation point occurred at around 20:00 (UTC+2). 3.2.3 Daily patterns Seasonal patterns of daily sums of GPP, TER and NEE are shown in Fig. 5. From the start of NEE measurements in late July to mid-August in 2009, the site was a net source of CO2to the atmosphere. By mid-August, GPP began to overwhelm TER turning the site into a CO2sink. During the growing season, the maximum daily values of NEE, TER and GPP were −5.8, 9.7 and −10.5gCm−2d−1, respectively. The uptake of CO2ended by late October. Respiration levelled off by mid-December. From mid-December 2009 until May 2010, TER remained low at an average rate of 0.46gCm−2d−1. In May 2010 and 2011, the daily GPP and TER were clearly distinguishable. During the growing season, the maximum daily values of NEE, TER and GPP were −9.4, 11.5 and −18.0gCm−2d−1, respectively. Reswww.biogeosciences.net/13/1255/2016/ Biogeosciences, 13, 1255–1268, 2016 1262 S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation Figure 5. The components of daily CO2exchange over the measurement period. Daily sum of net ecosystem CO2exchange (NEE, grey bars), gross primary production (GPP, open black circles) and total ecosystem respiration (TER, open grey triangles) as gCm−2d−1. Horizontal solid black lines show the zero level and vertical dashed black lines mark beginning of the year. piration levelled off at the end of November and TER remained low during the wintertime until the beginning of May in 2011. Wintertime TER averaged 0.51gCm−2d−1. During the growing season in 2011, the maximum daily values of NEE, TER and GPP were similar to those in 2010. Respiration levelled off by the beginning of December, with an average value of 0.76gCm−2d−1for December 2011. 3.3 Factors controlling CO2exchange 3.3.1 Gross photosynthesis The strong relationships between bin-averaged GPP and PAR from May to September in 2010 and 2011 can be seen in Fig. 6a–e. The rectangular hyperbolic model provided good fits to the data (adjusted R2>0.90, Table 1) except in May 2010 and 2011 (adjusted R2=0.52 and R2=0.76, respectively) and all relationships were statistically significant (p<0.01). There was no clear indication of GPP saturation even at PAR levels close to 1800µmolm−2s−1during June and July (Fig. 6a–e). The estimated monthly GPmax values are shown in Table 1. There were no differences in the GPmax values for May, June and July during 2010 and 2011, whereas in August and especially in September, the monthly average GPmax was higher in 2011 than in 2010. The seasonal variation in monthly GPmax values was clear (Table 1) and in May, September and August, the monthly averaged GPmax were low while the maximum values were observed in June and July. The range of the monthly α-values (quantum yield) varied from −0.04 to −0.06 in 2010 and from −0.05 to −0.07 in 2011. Further analysis under conditions with PAR level greater than 1000µmolm−2s−1revealed that effect of other climatic variables such as air temperature, the VPD and soil moisture on GPP was masked by the dominant role of PAR. We studied the relationships between weekly averaged GPP, GA and LAI. GPP increased with an increasing GA implying a positive linear relationship between these variables; Figure 6. Relationship of gross primary production (GPP) to incident photosynthetically active radiation (PAR). Measured monthly (mid-May–September) GPP (µmolm−2s−1) averaged with binned (steps of 10µmolm−2s−1)PAR (µmolm−2s−1)for 2010 (closed grey triangles) and 2011 (closed black circles). Data are fitted with nonlinear regression (GPP=(GPmax ×PAR×α/(GPmax+PAR×α)) between GPP and PAR (fit results in Table 1). Only measured data were used in the analysis. the adjusted R2value of the regression was 0.28 in 2010 (p=0.011) and 0.45 in 2011 (p<0.01). A relationship between GPP and LAI was not evident in 2010; however, they were better correlated in 2011 with an adjusted R2value of 0.42 (p<0.01). 3.3.2 Ecosystem respiration There was a clear relationship between bin-averaged nighttime TER and soil temperature from May to September in 2010 and 2011 (Fig. 7a). The exponential regression model provided good fits to the data (adjusted R20.71 and 0.69 for 2010 and 2011, respectively) and the relationships were statistically significant (p<0.01). The Q10 values were similar between the 2 years (2.17 and 2.35). The R10 values were 1.75 and 1.66µmolm−2s−1in 2010 and 2011, respectively. Additionally, TER increased with the increasing GA in 2010 (Fig. 7b), however, the linear correlation was not statistically significant (adjusted R2=0.16, p=0.053). TER and GA were better correlated in 2011 (adjusted R2=0.51, p<0.01). There was a strong positive linear relationship between TER and GPP (p<0.01) in both years (Fig. 7c). GPP explained 82 and 75% of the variation in the TER in 2010 and 2011, respectively. 3.4 Annual balance The estimated annual balances of TER, GPP and NEE are shown in Table 2. The site acted as a CO2sink during the studied years and the annual NEE was −56.7, −262 Biogeosciences, 13, 1255–1268, 2016 www.biogeosciences.net/13/1255/2016/ S. E. Lind et al.: Carbon dioxide exchange of a perennial bioenergy crop cultivation 1263 Table 1. Monthly fit results of a rectangular hyperbolic model together with average climatic conditions – the fit results between gross primary production (GPP, µmolm−2s−1)binned with photosynthetically active radiation (PAR, µmolm−2s−1, bins from 0 to 1800µmolm−2s−1 with an interval of 10µmolm−2s−1)from mid-May to September in 2010 and 2011. A rectangular hyperbolic model of the form GPP=(GPmax ×PAR×α/(GPmax+PAR×α), where GPmax (±SE, µmolm−2s−1)is the theoretical maximum rate of photosynthesis at infinite PAR and α(±SE) is the apparent quantum yield – i.e. the initial slope of the light response curve was used. Adjusted R2of regression and number of PAR bins (n) are shown. Also monthly average (±SD) of air temperature (T,◦C), volumetric water content (VWC, m3m−3) at 2.5cm depth and vapour pressure deficit (VPD, kPa) are shown together with number of rain event days (when precipitation>0.2mm) in month, precipitation sum (prec., mmmo−1)and monthly averaged green area (GA, m2m−2)and leaf area (LAI, m2m−2)indices. Month GPmax α R2n T VWC VPD Prec. sum GA LAI (µmolm−2s−1)(◦C) (m3m−3)(kPa) events 2010 May −21.5±1.7 −0.057±0.009 0.52 133 14.3±5.3 0.26±0.05 0.65±0.6 7 23 8.7 1.8 Jun −44.5±1.7 −0.047±0.002 0.93 158 13.0±4.6 0.26±0.05 0.54±0.4 9 72 19.0 4.3 Jul −40.1±1.1 −0.053±0.002 0.95 163 21.0±4.7 0.14±0.03 0.85±0. 7 7 34 17.2 4.0 Aug −25.2±0.7 −0.057±0.003 0.91 148 15.8±6.2 0.14±0.05 0.53±0.5 14 42 14.0 3.9 Sep −18.1±2.2 −0.040±0.007 0.93 19 9.8±3.9 0.21±0.04 0.14±0.2 16 53 14.1 4.0 2011 May −21.2±1.0 −0.056±0.005 0.76 134 11.2±4.0 0.30±0.03 0.45±0.4 11 38 5.7 1.8 Jun −45.8±1.4 −0.060±0.002 0.94 163 16.1±4.9 0.21±0.05 0.73±0.6 11 41 16.2 4.6 Jul −40.4±1.5 −0.050±0.002 0.92 154 19.1±4.4 0.20±0.06 0.65±0.5 11 91 15.5 5.3 Aug −29.9±1.0 −0.069±0.004 0.90 141 15.0±3.5 0.25±0.05 0.38±0.4 10 80 12.5 3.7 Sep −24.2±0.7 −0.074±0.004 0.94 103 11.1±3.3 0.31±0.04 0.20±0.2 13 70 8.0 4.3 Figure 7. Relationships between total ecosystem respiration (TER) and environmental variables. (a) TER (µmolm−2s−1)and soil temperature (◦C) at 2.5cm depth (binned with steps of 0.5◦C) in May–September period fitted with an exponential nonlinear regression (TER=R10 ×Q(Ts/ T10) 10 , where R10 and Q10 are fitted parameters). (b) Weekly averaged TER (gCm−2d−1)and green area index (GA, m3m−3)in May–October period fitted with linear regression. (c) Daily values of TER (gCm−2d−1)and gross primary production (GPP, gCm−2d−1, binned with steps of 0.25gCm−2d−1)in May–September period fitted with linear regression. Closed grey triangles are data for 2010 and closed black circles for 2011. Fit results are given in the text. and −256gCm−2in 2009 (23 July to 31 December), 2010 and 2011, respectively. The pattern in NEE accumulation is shown in Fig 8. During the 3-week time period from late July to mid-August 2009, the site acted as a source of atmospheric CO2. After the transition from a source to a sink in mid-August 2009, the site sequestered atmosphere CO2 for about 60 days leading to a negative cumulative NEE of −160gCm−2. During the winter dormancy period (from Figure 8. Cumulative NEE over the study period. Negative values indicate uptake of CO2and positive values emission to the atmosphere. Horizontal solid black lines show the zero level and vertical dashed black lines mark beginning of the year. late October 2009 to May 2010) the site lost 183gCm−2 and the cumulative NEE was 23gCm−2. After this, the site was an annual CO2sink, since the summer time uptake was higher than the wintertime CO2loss. In 2010, CO2 uptake period lasted approximately 120 days (May to midSeptember) and in mid-September the cumulative NEE was −403gCm−2. During the second winter dormancy, from mid-September 2010 to mid-May 2011, the site lost approximately 168gCm−2. In 2011, the CO2uptake period lasted about 135 days (from mid-May to early October) with a cumulative NEE of −679gCm−2by the end of this season. By the end of 2011, the cumulative NEE was −575gCm−2. This final cumulative value of CO2-C represents the amount of carbon the site accumulated from the start of the measurements in July 2009 until the end of 2011. www.biogeosciences.net/13/1255/2016/ Biogeosciences, 13, 1255–1268, 2016