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Land 2013, 2(3), 437-451; doi:10.3390/land2030437

The Effects of Land-Use Change from Grassland to Miscanthus x giganteus on Soil N2O Emissions
Brendan Roth 1,*, Michael Jones 1, James Burke 2,3 and Michael Williams 1
Department of Botany, Trinity College Dublin, Dublin 2, Ireland
Crops Environment and Land Use Programme, Teagasc Crop Research Centre, Oak Park, Carlow, Ireland
School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
Author to whom correspondence should be addressed; Tel.: +353-86-321-0957.
Received: 30 June 2013; in revised form: 12 August 2013 / Accepted: 22 August 2013 / Published: 3 September 2013


: A one year field trial was carried out on three adjacent unfertilised plots; an 18 year old grassland, a 14 year old established Miscanthus crop, and a 7 month old newly planted Miscanthus crop. Measurements of N2O, soil temperature, water filled pore space (WFPS), and inorganic nitrogen concentrations, were made every one to two weeks. Soil temperature, WFPS and NO3 and NH4+ concentrations were all found to be significantly affected by land use. Temporal crop effects were also observed in soil inorganic nitrogen dynamics, due in part to C4 litter incorporation into the soil under Miscanthus. Nonetheless, soil N2O fluxes were not significantly affected by land use. Cumulative yearly N2O fluxes were relatively low, 216 ± 163, 613 ± 294, and 377 ± 132 g·N·ha−1·yr−1 from the grassland, newly planted Miscanthus, and established Miscanthus plots respectively, and fell within the range commonly observed for unfertilised grasslands dominated by perennial ryegrass (Lolium perenne). Higher mean cumulative fluxes were measured in the newly planted Miscanthus, which may be linked to a possible unobserved increase immediately after establishment. However, these differences were not statistically significant. Based on the results of this experiment, land-use change from grassland to Miscanthus will have a neutral impact on medium to long-term N2O emissions.
land-use change; N2O; bioenergy crops; Miscanthus

1. Introduction

The agriculture and energy sectors are major contributors to greenhouse gas emissions, globally accounting for 13.5% and 25.9% of all anthropogenic emissions respectively [1]. In 2009, Irish agriculture was responsible for 29.1% of all national anthropogenic greenhouse gas emissions, the highest proportion of any sector, followed by energy at 21.1% [2]. In Western Europe, soil N2O emissions are the dominant source of agricultural greenhouse gas emissions [3]. In Ireland, N2O accounts for 38% of all agricultural greenhouse gas emissions, the vast majority of which is produced in the soil [4].

A potential mechanism to reduce greenhouse gas emissions from both these sectors is to use existing agricultural land for bioenergy crop production with perennial crops. In Ireland, 61% of the land cover is used for agriculture; of this 79% is dedicated to pasture, hay and grass silage, 11% to grazing and 10% to crop production [5]. Articles 3.3 and 3.4 of the Kyoto Protocol encourage the sequestration of atmospheric CO2 through changes in land use and management as a way to offset CO2 emissions, and bioenergy crops have been identified to show the greatest potential for carbon mitigation of all the available agricultural options [6]. Ireland has a significant potential for land-use change to bioenergy crops due to the large availability of agricultural land. The Irish government has highlighted the contribution non-food crops can make to reducing greenhouse gas emissions, specifically discussing Miscanthus as part of a national strategy for carbon mitigation [7]. The need for alternate farming practices has also been emphasised by the decoupling of EU direct payments from agricultural production, to land-area-based payments, hastening a trend for decreasing livestock numbers in Ireland [8].

As crops grow they fix CO2 from the atmosphere, this CO2 being released when the crops undergo combustion implying that the net atmospheric CO2 levels remain unchanged. This has led to the use of the term “Carbon neutral” in reference to energy produced from biomass [9]. Yet in reality this is a misrepresentation. There is a net CO2 output associated with energy crops due to the external energy costs associated with the planting, fertilisation, harvesting, and processing of the crops [10]. Other greenhouse gas costs—importantly N2O emissions—are often overlooked or crudely calculated when analysing the environmental impacts of bioenergy crops. Crutzen et al. [11] have indicated that for many first generation bioenergy crops such as rapeseed and maize the negative impacts of N2O emissions on radiative forcing outweigh the benefits achieved by the subsequent fossil fuel savings, but the same study did suggest that these results may be more favourable for grasses and woody coppices. One such grass gaining popularity in northern Europe as an energy crop is Miscanthus x giganteus [8].

Miscanthus x giganteus is a C4 grass originating from Asia, which has shown encouraging results when grown as a bioenergy crop in temperate climates. Field trials have produced yields of 10–30 t·ha1·yr1 across Europe [12], and 13–14 t·ha1·yr1 in Ireland at delayed spring harvest [12,13]. Miscanthus is a perennial rhizomatous crop with limited fertiliser requirements [14], and planting required roughly only every 15 years [9]. These attributes are favourable for bioenergy crops as they significantly reduce the external energy costs associated with their life cycle. A land-use change from conventional agricultural crops to Miscanthus may also lead to an increase in the quantity and quality of plant litter. Plant litter can account for up to almost 20% of total Miscanthus above ground biomass [15], when combined with large yields there is potential for a large quantity of litter to become incorporated into the soil. The quality of this litter is likely to be affected by the fact that C4 crops tend to contain larger proportions of recalcitrant tissues than C3 species [16,17]. These changes affect below ground substrate availability and consequent microbial community dynamics [18,19], in turn influencing the soil nitrogen balance and impacting processes such as nitrification, denitrification, and therefore N2O emissions. More immediate effects on soil N2O production are also likely following the establishment of Miscanthus. Recent research has found that land use change from grassland to another low input second generation bioenergy cropping system, short rotation woody crops (SRWC), can lead to significant short-term increases in N2O emissions, due to an acceleration of soil carbon and nitrogen cycling associated with the clearing and cultivation of grasslands [20,21].

A better understanding of the environmental impacts of land-use change to bioenergy crops is clearly required. This research has focused on the effects of land-use change from an unfertilised, un-grazed grassland (Lolium perenne) to Miscanthus x giganteus, on soil N2O fluxes. Here we address the question—does land-use change from Lolium perenne to Miscanthus x giganteus lead to a significant change in soil N2O emissions?

2. Materials and Methods

2.1. Site Description

The research presented here was carried out at Teagasc, Crop Research Centre, Oak Park, Co. Carlow, Ireland (52°51′N, 6°54′W, 50 m a.s.l.). The soil is classified as loamy sand with a pH of 6.8 and a mean C/N ratio of 10.7 [22]. The site has a mean annual precipitation of 830 mm and mean temperature of 9.3 °C for the period 1982–2002. During the study period there was rainfall of 946 mm and a mean temperature of 9.4 °C, with minimum and maximum temperatures of −7.0 °C to 26.8 °C. Meteorological data was collected from a station within 1 km of the site (Climate records supplied by Met Eireann, Irish Meteorological Service).

Measurements were carried out over a one year period from November 2008 to November 2009, in three adjacent plots; an 18 year old Lolium perenne grassland (approx. 0.3 ha), a 14 year old established Miscanthus crop (approx. 0.3 ha), and a seven month old newly planted Miscanthus crop (approx. 0.6 ha), herein referred to as G, EM, and NM respectively. All three received minimum management, with no fertiliser applied to any since 2002. The grassland was cut bi-annually in May and September, while the Miscanthus was harvested in April. A mean dry matter yield of 16 t·ha1 was measured at delayed harvest from the established Miscanthus in April 2008. The grassland and established Miscanthus were converted from a former arable site in 1990 and 1994 respectively, while the newly planted Miscanthus was converted from the grassland in 2008. Here, grassland was sprayed with herbicide (Roundup, Monsanto) once in February and again in April before being conventionally tilled and planted with Miscanthus at a density of two rhizomes per m2 in May. The plots were arranged in a split-plot design with land-use as the main treatment. Each consisted of four subplots (16 m2) from which two samples were taken from opposing corners, resulting in a zigzag sampling pattern (Figure 1).

Figure 1. Diagram of experimental layout (not to scale).
Figure 1. Diagram of experimental layout (not to scale).
Land 02 00437 g001 1024

2.2. N2O Emissions

N2O emissions were measured on 44 occasions at either one or two week intervals between November 2008 and November 2009 using non-flow-through, non-steady-state chambers (surface area: 0.0314 m2, height: 0.194 m, volume: 5 L). Two chambers at opposite ends of each subplot were attached to collars positioned permanently in the soil at a depth of 0.1 m—meaning a total of eight chambers per plot. 22 mL gas samples were taken using a 60 mL syringe and stored in an evacuated glass vial with a butyl rubber/PTFE septum. Samples were taken at 0.5 min after chamber deployment to allow for equilibration inside the chamber, and again after 40.5 min. Measurements were starting at 10.30, as morning has been found to give the best approximation of daily average flux [23]. Linearity was tested from four random chambers over five points at the start of the experiment (mean R2 = 0.94), and then again over three points in February 2009 (mean R2 = 0.90), May 2009 (mean R2 = 0.87), and August 2009 (mean R2 = 0.90). Samples were analysed for N2O within one month of collection using a gas chromatograph fitted with an electron capture detector (Clarus 500 GC, Perkin-Elmer, Waltham, MA, USA) and calibrated using standard concentrations of 0, 0.2, 2 and 10 ppm N2O. N2O fluxes are calculated using the following formula derived from Hutchinson and Livingston [24]:

Land 02 00437 i001
where C is the N2O concentration in the chamber (ppm), t is chamber deployment time, Mm is the molar mass of N2O (g), Vm is the molar volume of N2O (m3), t is time (hours), V is the chamber volume (m3) and A is the surface area covered by the chamber (m2). Cumulative annual emissions were calculated by plotting N2O flux against time, interpolating linearly between the points and then integrating beneath the line [25].

2.3. Ancillary Measurements

Replicated measurements of soil temperature and volumetric water content (n = 4) were made alongside N2O measurements, using a WET sensor (Delta-T). Measurements were made at a depth of 0–10 cm in a 0.25 m2 area surrounding the soil collars. Mean bulk density for each treatment (0–10 cm) was measured once in March 2009 using soil cores. Volumetric water content was converted into water-filled pore space (WFPS) using measured soil bulk density and a particle density of 2.65 g·cm−3 [26]. Soil samples were collected monthly from a depth of 0–10 cm for analysis of nitrate and ammonium concentrations (n = 8). Samples were collected from a 1 m2 area surrounding the soil collars. Nitrate and ammonium were extracted from the soil samples based on the techniques described in Compton and Boone [27]. Samples were homogenized and manually sieved through a 2 mm mesh. A 10 g subsample was taken from each sample. This was then added to 100 mL of 2 Molar KCl and shaken for one hour at 135 rpm. The resulting extract was passed through filter paper (<2.5 µm), and the filtrate frozen until ready for analysis using a colorimeter (QuickChem QC8500 Automated Ion Analyzer, Lachat, Milwaukee, WI, USA).

2.4. Statistical Analysis

The experiment was analysed as a split-plot design with land use as the main treatment. After testing for normal Gaussian distribution a constant was added to all N2O flux data, which were then log transformed, while an inverse transformation was applied to soil NH4+ concentration. A repeated measures two-way ANOVA followed by a Bonferroni post-test was used in order to test the effects of plot and time on N2O flux, WFPS, soil temperature, nitrate and ammonium concentrations (Prism 5, Graph Pad). Multiple regression analyses were also carried out for N2O flux versus these variables and their interactions, using stepwise bidirectional elimination to determine the most suitable models (R 3.0.1). The resulting models were then tested for autocorrelation with a Durbin-Watson test (R 3.0.1). A one-way ANOVA followed by a Tukey’s multiple comparison post-test was performed on bulk density data, and yearly cumulative N2O emissions (Prism 5, Graph Pad). All data were expressed as mean ± standard error (SE) values.

3. Results

3.1. Nitrous Oxide Flux

Figure 2 shows mean N2O flux (±SE, n = 8) from all land uses from November 2008 to November 2009. Mean fluxes showed large variation within treatment, with G ranging from −75 to 37 µg·N·m−2·hr−1, NM ranging from −59 to 70 µg·N·m−2·hr−1 and EM ranging from −43 to 76 µg·N·m−2·hr1. The majority of the positive and negative peaks occurred between the dates 22 April 2009 and 22 July 2009. Analysing these data by repeated measures two-way ANOVA showed that time (p = 0.0109, df = 43) and interaction (p = 0.0129, df = 86) had a significant effect on N2O flux, but land use did not (p = 0.5293, df = 2). It should also be noted that N2O consumption was frequent, with uptake observed in 36%, 40% and 42% of all measurements in NM, EM and G respectively.

Figure 2. Mean N2O Flux (µg·N·m−2·hr−1) ± SE (n = 8) from three land uses; G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004). Lines indicate the dates 22 April 2009 and 22 July 2009.
Figure 2. Mean N2O Flux (µg·N·m−2·hr−1) ± SE (n = 8) from three land uses; G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004). Lines indicate the dates 22 April 2009 and 22 July 2009.
Land 02 00437 g002 1024

Figure 3 illustrates cumulative N2O fluxes from each plot over the course of the year. Fluxes were highest from the newly planted Miscanthus (614 ± 294 g·N·ha−1·yr−1), as was the case for the majority of the year. Cumulative fluxes from the established Miscanthus were 378 ± 133 g·N·ha−1·yr−1. This was influenced by a period of consistent uptake between December 2008 and April 2009, and then a period of relatively high emissions between April and August 2009. The lowest mean yearly cumulative flux was seen in the grassland (217 ± 164 g·N·ha−1·yr−1). A one-way ANOVA of these data showed that land use had no significant effect. A subsequent Tukey’s multiple comparison post-test also showed no significant difference in yearly cumulative N2O flux between any of the treatments. Best fit multiple regression analyses were carried out to test which factors control N2O flux in each of the treatments. WFPS, soil temperature, soil nitrate and ammonium concentrations, and their interactions were examined. The resulting models where as follows:

G: N2O = NH4
NM: N2O = NO3 + NH4 + WFPS + Temp + (NO3)(NH4) + (NO3)(WFPS) + (NO3)(Temp) + (NH4)(WFPS) + (NH4)(Temp) + (WFPS)(Temp) + (NO3)(NH4)(Temp) + (NO3)(NH4)(WFPS)(Temp)
EM: N2O = WFPS + Temp + (NO3)(NH4) + (WFPS)(Temp) + (NO3)(NH4)(WFPS) + (NO3)(NH4)(Temp) + (NO3)(NH4)(WFPS)(Temp)

Results were inconsistent, with N2O flux poorly explained across the board. 27% of N2O fluxes from NM were found to be controlled by a large array of factors and complex interactions. Of these, the interaction between soil NO3 concentration, soil NH4+ concentration, WFPS, and soil temperature was found to be the most significant (p = 0.0002). Meanwhile, G and EM responded very poorly with only 1% and 9% of N2O fluxes accounted for by the regression analyses respectively. No significant relationship was found between any factor or interaction and N2O, from either land use. Analyses by Durbin-Watson tests indicated that the models were not affected by autocorrelation.

Figure 3. Cumulative N2O Flux (g·N·ha−1) ± SE (n = 8) over one year from three land uses; G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004).
Figure 3. Cumulative N2O Flux (g·N·ha−1) ± SE (n = 8) over one year from three land uses; G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004).
Land 02 00437 g003 1024

3.2. Soil Bulk Density, WFPS, and Soil Temperature

Mean soil bulk densities were 0.76 ± 0.02, 0.94 ± 0.04, and 0.87 ± 0.05 g·cm−3 in G, NM and EM respectively. Land use was shown to have a significant effect on bulk density by a one-way ANOVA (p = 0.0086). Specifically a significant difference was seen between G and NM, but not between G and EM, or NM and EM. Soil moisture (0–10 cm) was measured on each sampling date. WFPS was calculated using these measurements and bulk density. Figure 4a shows mean WFPS and standard error (n = 8) in each plot measured from November 2008 to November 2009. Analysis by repeated measures two-way ANOVA showed land use (p < 0.0001, df = 2), time (p < 0.0001, df = 11), and interaction (p < 0.0001, df = 22) to all have a significant effect on WFPS. Further analysis found land use to lead to a significant difference in WFPS between G and EM (19 dates), and NM and EM (22 dates) on a much greater number of occasions than between G and NM (3 dates). In general higher values were observed in EM than the other two. Figure 4b shows mean soil temperature and standard error (n = 8) from each land use measured from November 2008 to November 2009. Mean soil temperature ranged from 26.5 °C in plot NM to −4.7 °C in G. This low temperature was an exception with mean soil temperatures consistently found to be less in EM. Analysis by repeated measures two-way ANOVA showed land use (p < 0.0001, df = 2), time (p < 0.0001, df = 11), and interaction (p < 0.0001, df = 22) to all have a significant effect on soil temperature. Further analysis by a Bonferroni post-test showed land use to lead to a significant difference in soil temperature between all treatments, with significant differences found on a large number of occasions.

3.3. Soil Nitrate and Ammonium

Figure 5 illustrates soil NO3 (Figure 5a) and NH4+ (Figure 5b) concentrations from all three treatments. Soil NO3 (0–10 cm) was measured monthly (n = 8). G frequently contained the lowest mean concentrations, the least being 0.9 kg·ha−1 measured on 24 June 2009. Both NM and EM tended to contain higher concentrations of NO3, with the highest (11.5 kg·ha−1) being measured in NM on 24 June 2009. Analysis by repeated measure two-way ANOVA showed land use (p < 0.0001, df = 2), time (p < 0.0001, df = 43), and interaction (p < 0.0001, df = 86) to all have a significant effect on soil NO3 concentration. Comparing each individually by Bonferroni post-test found a greater number of dates with significantly different soil NO3 concentrations between G and NM (7), and G and EM (6) than NM and EM (2), with the majority of differences occurring between March and July.

Figure 4. Mean (a) water filled pore space (WFPS) (%) and (b) soil temperature (°C) ± SE (n = 8) from November 2008 to November 2009 from three land uses: G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004).
Figure 4. Mean (a) water filled pore space (WFPS) (%) and (b) soil temperature (°C) ± SE (n = 8) from November 2008 to November 2009 from three land uses: G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004).
Land 02 00437 g004 1024
Figure 5. Mean (a) soil NO3 and (b) NH4+ (kg ha−1) ± SE (n = 8) from three land uses: G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004).
Figure 5. Mean (a) soil NO3 and (b) NH4+ (kg ha−1) ± SE (n = 8) from three land uses: G ( Land 02 00437 i002), NM ( Land 02 00437 i003) and EM ( Land 02 00437 i004).
Land 02 00437 g005 1024

As with NO3, Soil NH4+ concentration (0–10 cm) was measured monthly (n = 8) from all three treatments. NH4+ showed more variation between land uses than NO3 but had similar results when statistically analysed. The lowest mean soil NH4+ concentration (1.1 kg·ha−1) was measured in NM on 22 April 2009, while the highest (8.6 kg·ha−1), also in NM, was seen on 16 December 2008. Analysis by repeated measure two-way ANOVA showed land use (p = 0.0037, df = 2), time (p < 0.0001, df = 43), and interaction (p < 0.0001, df = 86) to all have a significant effect on soil NH4+ concentration. Again comparing each land use individually by Bonferroni post-test found a greater number of dates with significantly different soil NH4+ concentrations between G and NM (6), and G and EM (5) than NM and EM (2).

Further illustrated in Figure 5 is the difference in mean soil nitrate and ammonium concentrations between EM and G, and NM. A pattern can be seen whereby variation in both compounds exhibited contrary patterns throughout the year. While the difference in NO3 increased from spring through summer, the opposite was seen with NH4+, which was greater during the winter months.

4. Discussion

In this experiment the difference in N2O fluxes, and common factors that are known to affect them, were examined in three adjacent plots containing grassland (G), a newly planted Miscanthus crop (NM) and a long established Miscanthus crop (EM). A randomized block experimental layout was not possible due to the need to use a well-established Miscanthus plot. Hence a split-plot design with land use as the main treatment was used. All three plots share a very close geographical location, soil type, and land-use history. Recently a comparable study has been carried out using the same plots [22].

N2O flux from all three land uses displayed large variation, with few noteworthy peaks measured throughout the year (Figure 2). Overall cumulative yearly fluxes were relatively low (216 ± 163, 613 ± 294, and 377 ± 132 g·N·ha−1·yr−1 from G, NM, and EM respectively) and fell within the range commonly observed for unfertilised grasslands dominated by perennial ryegrass (Lolium perenne). In a nearby unfertilized grazed pasture, yearly fluxes of 1,000 g·N·ha−1 have been recorded [28], while yearly N2O fluxes of 219–511 g·N·ha−1 have been observed in an unfertilised grassland in Scotland [29]. A recent study monitoring N2O emissions from a 3–4 year old unfertilised Miscanthus crop over two years found mean annual emissions of 324 g·N·ha−1·yr−1, again very similar to those found here [30]. Furthermore, the yearly cumulative N2O emissions measured during this experiment compare favourably with those from other bioenergy crops. Hellebrand et al. [31] monitored N2O emissions from a range of bioenergy crops. Yearly emissions from unfertilised plots in 2000 were; willow (540 g·N·ha−1), poplar (530 g·N·ha−1), hemp (940 g·N·ha−1), and rape (1,110 g·N·ha−1). Elsewhere, Jin et al. [32] have found yearly N2O emissions ranging from 600–700 g·N·ha−1 from unfertilised reed canary grass. Gauder et al. [33] have also found low N2O emissions from unfertilised bioenergy crops, reporting N2O emissions close to zero from both Miscanthus and willow. While recognising that factors other than crop type play an important role in soil N2O production, it is clear that soil N2O emissions from Miscanthus occupy a range commonly seen for unfertilised bioenergy crops.

Analyses by best fit multiple regression were inconsistent amongst land uses, with little more than a vague relationship observed. Adjusted R2 squared values were poor ranging from 0.25 in NM to 0.02 and 0.06 in G and EM respectively. Of the 25% of N2O fluxes accounted for from NM the interaction between soil NO3 concentration, WFPS, and soil temperature, and the interaction between soil NO3 concentration, soil NH4+ concentration, WFPS, and soil temperature, were found to be highly significant. These analyses indicate that a large number of complex factors and interactions as opposed to any single variable are likely to influence the rate of N2O flux throughout the year.

N2O consumption was common, occurring in 36%, 40% and 42% of all measurements in NM, EM and G respectively. The similarity of uptake frequencies indicates a lack of any crop effect. The frequently cited link between N2O consumption and low concentrations of soil inorganic N [34,35,36] is likely to have been an important factor in this case. N2O consumption is thought to generally occur when N2O is reduced to N2 by denitrifiers during denitrification [37,38], although nitrifiers are also known to contribute to N2O consumption via the nitrifier denitrification pathway [39,40,41]. It is unclear as to what was the main N2O consumption pathway here. A best fit multiple regression analysis of N2O versus WFPS, soil temperature, and nitrate and ammonium concentrations, on only occasions were N2O uptake occurred yielded no significant results. Most of the peaks observed in N2O emission and uptake occurred between 22 April 2009 and 22 July 2009. This period coincides with peak crop growth at the site, when competition for soil nitrogen between plants and soil microorganisms would be at its greatest. Similar results were found by Du et al. [42] who found an increase in N2O flux variation during the growing season in grassland soils. The difference in WFPS between the sites was also found to increase during this time and may be linked. Dobbie and Smith [25] have reported WFPS to be the main driving factor behind N2O flux during the growing season.

While land use was not found to significantly affect N2O flux, a trend towards higher fluxes from the newly planted Miscanthus can be seen when looking at cumulative yearly fluxes (Figure 3). This difference may be due to soil disturbance and substrate incorporation caused by conventional tilling before Miscanthus was planted. Studies have found N2O emissions to increase significantly following the disturbance of previously undisturbed soils. MacDonald et al. [43] found a significant increase in N2O emissions in the year following the ploughing of a managed grassland, while Velthof et al. [44] have found that the renovation of a grassland increased N2O emissions by a factor of 1.8–3.0. Furthermore, it has been reported that much of this effect can occur in a short period following tillage. Davies et al. [45] measured N2O emissions of 1.5–3.7 kg·N·ha−1 in the seven weeks proceeding ploughing. MacDonald et al. [43] have linked this with rapid rate of N mineralisation following soil disturbance. This experiment has not looked at these short-term effects. Work measuring the fluxes immediately following land use change to Miscanthus is currently being carried out in Ireland, and should be taken into account when considering the full N loss implications of land use change to Miscanthus.

As well as N2O fluxes, soil bulk density, WFPS, temperature, nitrate and ammonium concentrations were measured. Bulk density was found to be lowest in G and highest in NM. This is due to the fact that NM was found to contain a high density of stones beneath the soil surface. These were moved towards the surface during tillage 11 months prior to these measurements. Bulk density was found to differ significantly between G and NM. This is unsurprising as G is long established and with no significant disturbance in its recent history, while NM was subject to considerable disturbance when tilled. Bulk density did not differ significantly between the other land uses. Bulk density has been used to calculate WFPS, and as such land use was found to have a significant effect on WFPS. WFPS was found to be significantly different between G and EM, and NM and EM, on a substantially greater number of dates than between G and NM. Canopy cover is likely to have had a strong influence on these differences. A dense canopy was present at peak growth in EM compared to NM and G. A large leaf area will have affected soil water by increasing leaf evapotranspiration [46], as well as increasing the interception of radiant energy hence decreasing soil moisture losses by evaporation in EM [47]. The largest differences between treatments occur between the sampling dates 22 April 2009 and 22 July 2009, with WFPS being consistently higher in EM than G and NM. This is due to the emergence of crop cover as discussed above, but could also be affected by water use efficiency during this period of intense growth. C4 plants are known to be capable of better water use efficiency than C3 plants [48]. Land use was also found to affect soil temperature, with significant differences seen between all land uses (Figure 4b). This will have been affected by crop cover as discussed previously, but could also be linked with the differing bulk densities observed, as bulk density can directly affect soil temperature [49].

As none of the treatments were subject to additional nitrogen treatments soil nitrate and ammonium concentrations were found to be relatively low (0.9–11.5 kg·NO3·ha−1 & 1.1–8.6 kg·NH4+·ha1) when compared with conventionally fertilised arable sites. Both soil NO3 and NH4+ concentrations were found to be significantly affected by land use. Significant differences occurred more frequently between G and NM, and G and EM, than NM and EM for both soil nitrate and ammonium concentrations. It is also apparent that temporal differences between NO3 and NH4+ occur. NO3 increased in both Miscanthus treatments compared to the grassland throughout the growing season before decreasing again, whilst NH4+ levels were greater in Miscanthus during the winter months before dropping below grassland concentrations during the growing season. These trends suggest differences in soil nitrogen cycling pathways. That these patterns were observed in both Miscanthus treatments when compared to the grassland suggests a crop effect. Foereid et al. [50] found a higher rate of soil N mineralisation in C3 grass (Lolium spp) than Miscanthus during soil incubation experiments, while field studies have shown higher rates of soil N mineralisation in C3 communities than C4 during the growing season [51,52]. Slower rates of nitrogen mineralisation beneath C4 species has been linked with an increase in the quantity and quality of plant litter, this in turn can lead to an increase in rates of microbial nitrogen immobilisation [53]. This could explain the large positive difference in soil NH4+ between the grassland and Miscanthus treatments during the growing season. The difference in NO3 observed between the grassland and Miscanthus treatments during the growing season may indicate Miscanthus has a preference for root uptake of NH4+. Suspension cultures of a similar species Miscanthus x ogiformis Honda “Giganteus” have been found to show a strong preference for ammonium as a source of nitrogen for growth [54].

5. Conclusions

Based on the results of this experiment, land-use change from grassland to Miscanthus will not have a negative impact on medium to long-term N2O emissions. However, it is possible that there was a short-term increase in N2O emissions immediately after establishment but prior to monitoring began. No significant difference was found in N2O flux between the grassland and Miscanthus plots. Furthermore once established, Miscanthus can be a particularly low source of N2O, with yearly N2O fluxes found in this experiment similar to those from unfertilised un-grazed grassland. Another important consideration is the use of nitrogen fertiliser. While this study examines a scenario involving no nitrogen addition, fertilisation will be a key factor in N2O emissions from many land use change scenarios. Miscanthus’ low fertiliser requirements [55,56] have been found to lead to reduced N2O emissions when compared to other agricultural crops [33,57].


This work was funded by a Teagasc Walsh Fellowship. Thanks are due to John Finnan for his advice and support, and also to all the field staff of the Oak Park Research Centre for management and maintenance of the experimental site. Also, many thanks to the technical staff of the Botany Department and Centre for the Environment, Trinity College Dublin for providing support during soil analysis.

Conflicts of Interest

The authors declare no conflict of interest.


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