PhD Thesis, 2010 - University College Cork

National University of Ireland, Cork 

University College Cork — Coláiste na hOllscoile Corcaigh 

Department of Civil and Environmental Engineering 

Head of Department: Dr. Michael J. Creed 

Supervisor: Prof. Gerard Kiely 

Nitrous oxide flux evaluation by 

eddy covariance 

Thesis presented by 

MIKHAIL MISHUROV 

For the degree 

DOCTOR OF PHILOSOPHY 

February, 2010

TABLE OF CONTENTS 

Declaration 

Acknowledgements 

Abstract 

iv 

v 

vi 

1. Introduction 1 

1.1. General introduction 1 

1.2. Aims and objectives 2 

1.3. Thesis layout 2 

2. Literature review 4 

2.1. Nitrous oxide, its chemistry, role in the atmosphere and production in soils 4 

2.2. Managed grassland ecosystem 8 

3. Material and methods 10 

3.1. Site description 10 

3.2. The eddy covariance method 12 

4. Distinguishing peaks from background emission of nitrous oxide 17 

4.1. Abstract 17 

4.2. Introduction 18 

4.3. Methods 20 

4.3.1. Site description and data collection 20 

4.3.2. Flux calculation 21 

4.3.3. Distinguishing technique 23 

4.4. Results 24 

i

4.5. Discussion 26 

4.5.1. Peak triggers 26 

4.5.2. Peak event 28 

4.5.3. Validation 30 

4.5.4. Choice of technique 30 

4.6. Conclusions 31 

4.7. Acknowledgements 32 

5. Nitrous oxide flux dynamics of grassland undergoing afforestation 33 




5.3.1. Study site 37 

5.3.2. Nitrous oxide flux measurements 39 



5.5.1. Environmental parameters 46 

5.5.2. Management 47 


6. Gap-filling techniques for the annual sums of nitrous oxide 50 




6.3.1. Site description and flux measurements 52 

ii

6.3.2. Gap distribution 54 

6.3.3. Gap-filling techniques 54 



6.6. Conclusions 59 


7. General discussion 60 

8. Recommendations for further research 62 

References 64 

Appendix 73 

iii

DECLARATION 

I declare that this thesis has not been previously submitted as an exercise for a 

degree at the National University of Ireland or any other university and I further 

declare that the work embodied in it is my own. 

Mikhail Mishurov 

iv

ACKNOWLEDGEMENTS 

Firstly, I would like to thank my supervisor Prof. Ger Kiely, for gentle guidance 

and academic freedom throughout my study. 

Technical and intellectual support by the present and past members of the 

Hydromet research group was invaluable for the successful and timely completion of 

this research. Discussions with Drs. Paul Leahy, Ken Byrne, Matteo Sottocornola, 

Dong-Gill Kim, Mathias Peichl helped to improve my understanding of the basic and 

more advanced concepts related to methods used. Dedicated instrument maintenance 

and data collections provided over the many years by the technical engineers Adrian 

Birkby, Killian Murphy, Jimmy Casey and Nelius Foley had made this thesis as datarich 

as it became. 

My fellow students Ann-Kristin Köhler, Michael Wellock, Ciaran Lewis, Rashid 

Rafique, Ann Brune, Fraçois Clement, as well as James Eaton, Nicola McGoff, 

Cristina LaPerle and Vesna Jakšić made my journey enjoyable and friendly. 

I am indebt to the Department of Agriculture, Fisheries and Food that funded my 

studentship and research through the Research Stimulus Fund programme (project RSF 

06 372) and the Environmental Protection Agency of the Irish government that 

provided funding for the earlier years under CelticFlux programme (2001-CC/CD- 

(5/7)). 

My last but not least thanks go out to all the people that helped, supported and 

taught me throughout the years in school, universities and life. 

v

ABSTRACT 

Nitrous oxide (N 2 O) is a powerful greenhouse gas with global warming potential 

298 times that of carbon dioxide in the 100-year horizon. It also a highly-reactive gas 

that has a damaging influence on the ozone layer. These properties of the nitrous oxide 

require attention to the influence of the anthropogenic activities on the production and 

emission into the atmosphere. In Ireland one the major sources of the nitrous oxide 

emission is the agricultural activity. Since the intensive use of grassland pastures 

requires nitrogen fertiliser application, the ecosystems exist in almost permanent state 

of nitrogen abundance. Such conditions make them prone to nitrous oxide production 

and emissions. The temporal patterns and variations of the N 2 O flux were the primary 

interest of this research. We analysed intra-annual dependency of the instantaneous 

flux on management and environmental conditions, inter-annual changes in the 

grassland undergoing afforestation; and approaches to gap-filling of the time series for 

the purpose of calculating the annual sums. As a result of these sub-projects a method 

was developed to separate peak from background emissions, it was used to analyse 

events preceding the peaks and this information was in turn used for fine-tuning of a 

gap-filling approach. The quantitative separation showed that the peak flux, while is a 

dominant component of the annual emission, depends in part on environmental 

conditions that are hard to manage or predict. The long-term observations of the nitrous 

oxide emission from the grassland undergoing afforestation showed that while 

significant decrease in emissions over the years was not entirely surprising forestestablishment 

works might lead to major emission bursts. The explored gap-filling 

techniques introduced the simple estimates and laid the foundation for more elaborated 

approaches. 

vi

Introduction 

1. INTRODUCTION 

1.1. General introduction 

Nitrous oxide (N 2 O) is a powerful greenhouse gas with a global warming potential 

(GWP) 298 times that of carbon dioxide (CO 2 ) in the 100-year horizon (Forster et al. 

2007). It also a highly-reactive gas that has a damaging influence on the ozone layer 

(Crutzen 1970). These properties of nitrous oxide require attention with regard to the 

influence of anthropogenic activities on its production and emission into the 

atmosphere. 

Managed grasslands in Ireland are one of the major sources of the nitrous oxide 

emission to the atmosphere. Much of these emissions are related to nitrogen fertiliser 

applications which are used to enhance the productivity of grasslands. This does mean, 

however, that during most of the year grasslands in Ireland are in the state of the over 

abundance of nitrogen. The temporal and spatial patterns of the N 2 O flux are varied 

and dependent on management and environmental conditions, which are poorly 

understood. 

One of the reasons for limited progress in understanding of N 2 O fluxes is 

methodological in nature: the use of manual closed chambers severely limits the time 

span and the scale of measurements. Other techniques, such as automated closed 

chambers or eddy-covariance allow for more frequent measurements, including the 

night periods. Despite these advancements a number of methodological issues remain 

unresolved: no gap-filling technique has been widely accepted in the scientific 

community; the description of the emission pattern remains largely quantitative; and 

1

Introduction 

little information exists about N 2 O emissions for ecosystems undergoing change (e.g., 

grassland transitioning to forest). 

1.2. Aims and objectives 

The aim of this study was to explore the behaviour and temporal trends of the 

nitrous oxide emissions from a managed grassland ecosystem. Two major 

methodological problems were addressed: (1) the definition and quantification of the 

peak periods in the emission time series and (2) the gap-filling of the time series with 

the purpose of calculating annual emission sums. Furthermore, a unique long-term data 

set of N 2 O fluxes was used to investigate the behaviour of an ecosystem transitioning 

from the grassland to broadleaf forestry. 

1.3. Thesis layout 

This thesis consists of 8 chapters. Following these introductory remarks, Chapter 2 

is devoted to the review of existing body of literature on the topic of soil production of 

nitrous oxide and its emissions in natural and managed ecosystems. Chapter 3 

describes conditions and location of the study site, along with the typical management 

practices, as well as details of the eddy covariance technique and its limitations. 

Three following chapters (Chapter 4 through 6) present manuscripts corresponding 

to their respective sub-projects: 

• Identification of the peaks and background emissions of nitrous oxide. 

• Dynamics of nitrous oxide emissions from the grassland undergoing 

afforestation. 

2

Introduction 

• Comparison of the gap-filling techniques for nitrous oxide for the purpose of 

calculating annual emissions. 

The concluding Chapter 7 summarises the findings and gives a final overview of 

the study, Chapter 8 suggests ways to proceed in this field of research. Included in the 

Appendix 1 is the fourth manuscript co-authored with Kim and Kiely on the “Effect of 

increased N use and dry periods on N 2 O emission from a fertilized grassland” 

(accepted for publication in Nutrient Cycling in Agroecosystems on 1 April 2010). 

3

Literature review 

2. LITERATURE REVIEW 

2.1. Nitrous oxide, its chemistry, role in the atmosphere and 

production in soils 

Nitrous oxide (IUPAC name: dinitrogen monoxide) is a colourless gas with the 

chemical formula N 2 O. The molecule of N 2 O consists of two atoms of nitrogen (N) one 

of which is bound to an atom of oxygen (O). Such molecular structure and linear 

spatial configuration ensures that the molecule is fairly inert and stable under normal 

conditions, but will act as an oxidant or will decompose with increased temperature. 

Atmospheric life time, which is the period required to equilibrate the atmospheric 

concentration of the gas after change, of nitrous oxide was estimated to be 114 years. A 

succession of the IPCC reports highlighted significant greenhouse warming potential 

(GWP) of N 2 O: 298 in the 100-year horizon (Ramaswamy et al. 2001; Forster et al. 

2007). GWP is a measure of relative importance of a gas to planetary warming as 

compared to carbon dioxide (CO 2 ). Additional to being a greenhouse gas, nitrous oxide 

is also able to react with atmospheric ozone, a phenomenon that at present makes it 

responsible for the largest portion of the ozone layer destruction (Crutzen 1970). 

Since 1750 the concentration of the N 2 O in the atmosphere has increased by about 

17%. It is argued that the discovery of the Haber-Bosch process in 1909, (i.e., artificial 

synthesis of the ammonia used in the production of fertilisers) has facilitated the 

accelerated accumulation of atmospheric N 2 O. Fertilisers are widely used in most 

agricultural practices, in the Western Europe fertilisers were often used in excessive 

quantities beyond the agronomic plant requirements. As a result of this, soluble forms 

of N-compounds were leaching into the natural streams causing pollution and insoluble 

4


forms were retained in the upper soil profile or were transformed into volatile 

compounds. 

Typically, artificial nitrogen fertilisers are in the form of nitrates or ammonium 

salts, or both. Chemically these compounds represent either the most oxidised form of 

N (in nitrate, NO − 3 , oxidation number of N is +5) or the most reduced form (in 

ammonium, NH + 4 , oxidation number of N is −3, as in urea, (NH 2 ) 2 CO). This diversity 

encourages redox reactions facilitated by microbiological organisms as the major route 

of production of the nitrous oxide in soils. 

As most of the living organisms are concentrated in the top 10 cm of the soil, it is 

expected that this layer will be responsible for production of the most quantities of 

nitrous oxide. It was reported, however, that the concentration of N 2 O at the lower 

depths (30–50 cm) could reach 10 ppm (Müller et al. 2004; Goldberg and Gebauer 

2009). This implies that production might not be restricted to the top soil horizons, 

although it is not clear how this phenomenon might be reflected in atmosperic 

emissions of nitrous oxide. 

Nitrous oxide itself is most frequently a by-product of the transformational 

processes: two of the most-widely recognized are nitrification and denitrification 

(Wrage et al. 2001). Nitrification and denitrification are redox processes that describe 

N-oxidation and reduction, respectively. As such both processes are generally 

governed by the conditions characteristic for either reduction or oxidation. The 

reductive environment in the soil typically is an aerobic environment with low-to-mid 

water content; on the other hand the oxidative environment is anaerobic with average 

water content 70 to 90% of total porosity. Equations 2.1 and 2.2 show the step-by-step 

processes describing nitrification and denitrification respectively. 

5


NH 3 → NH 2 OH → NO 2 − → NO 3 

− 

(2.1) 

NO 3 − → NO 2 − → NO → N 2 O → N 2 (2.2) 

Each of the steps relies on the catalytic properties of certain enzymes: relevant 

reductaces in case of the denitrification and ammonia monooxygenase and relevant 

oxydoreductaces during nitrification. The initial step of nitrification (oxidation of the 

ammonia to hydroxylamine) requires the presence of oxygen (O 2 ) and free electrons to 

reduce oxygen to water. These electrons can be obtain during the second step 

(hydroxylamine oxidation) forming a positive feedback in the chain. Nitrous oxide is 

thought to be produced as a result of anabiotic transformation (chemodenitrification) 

from hydroxylamine or nitrite. 

A number of other processes have been identified as possible source of N 2 O: 

nitrifier denitrification is a phenomenon in which ammonia is oxidised to nitrite (NO − 2 ) 

which in turn reduces to molecular nitrogen (N 2 ). As can be seen from the Equation 

2.3, ammonia oxidation corresponds to the part of the nitrification processes and the 

reduction phase of this process is similar to part of the denitrification processes, hence 

the name. 

NH 3 → NH 2 OH → NO 2 − → NO → N 2 O → N 2 (2.3) 

All previously described pathways of N transformation are most commonly 

performed by autotrophic bacteria. Some heterotrophic bacteria, however, are able to 

carry out the nitrification (heterotrophic nitrification) and denitrification under aerobic 

conditions. As the nature of these organisms imply they prefer carbon-rich 

environments. 

6


Due to the natural complexity of the soil structure, the moisture, oxidation and 

aeration conditions differ significantly across even a few millimetres (size of the soil 

aggregates). Typically, a surface of the aggregate is better aerated and prone to quickly 

changing conditions of water and gas content, while on the other hand, conditions 

inside aggregates are tightly controlled by the size of the intra-aggregate pores and as a 

result high-moisture reduced environments tend to dominate. This means that all 

transformational processes are present in most natural soils at any given time; however, 

depending on the macroscopic conditions certain processes will dominate (Bateman 

and Baggs 2005). The macroscopic conditions, such as average water content or 

temperature of the soil horizon cannot change suddenly and therefore the intensity of 

the nitrous oxide flux typically lags behind any triggering events. To the best of our 

knowledge there was no information reported on the duration of the lag periods for any 

kind of trigger events. 

The importance of lag periods is now becoming clear when considering the 

importance of the high-intensity fluxes, referred to as “burst”, “peaks” or “events” 

(Clayton et al. 1997; Leahy et al. 2004), further clarification for these terms will be 

given in the Chapter 4. Such fluxes are the opposite of “background emissions”, which 

are characterised by low intensities, long duration and high frequencies of occurrence. 

These bursts were reported to comprise up to 85% of the annual flux from the fertilised 

grassland (Hsieh et al. 2005) on the basis of difference with the background flux. 

While qualitative descriptions of the peaks are not uncommon, it is hard to find any 

quantitative definition of the peak events. Further investigation into causes and 

phenomenon of the emission peaks is imperative if the understanding of the temporal 

patterns of the nitrous oxide to be improved. 

7


2.2. Managed grassland ecosystem 

Grasslands are a type of ecosystem dominated by grass and other non-woody 

plants. Due to these plants’ phenotype in the wild they are not able to compete with 

taller plants for solar radiation and as a result are limited to the areas that are unable to 

support trees as a major vegetation type, i.e., more arid regions. However, many 

afforested areas around the world were clear cut or otherwise converted to the 

grassland by humans. Such man-made grasslands tend to be located in the more humid 

temperate regions such as north-western and central Europe, New Zealand, and parts of 

Australia, etc. (Whitehead 2000). Most of the present Irish grasslands were established 

after conversion of forestry or badlands (including swamps) to agricultural use. As of 

2000, pasture grasslands accounted for 55% of the total area of the Republic of Ireland 

(Eaton et al. 2008) or approximately 90% of the agricultural land. 

Whether man-made or natural, grasslands are characterised by the relatively quick 

turn over of organic matter and significant proportion of the underground biomass. The 

root system is usually diffuse and forms dense intertwined network in the upper 5–10 

cm of the soil profile. The root density as well as organic matter (OM) content drop 

significantly below the root layer, which typically is underlined by the horizon 

transitional to the parent material. In the presence of shallow ground waters, 

hydromorphic processes can occur within the profile. 

Due to high-productivity of grasslands, they are usually used for grazing of diary 

and beef cattle, as well as grass harvesting for winter silage. Due to the amount of farm 

produce, the balance of soil nutrients is affected and is usually corrected with the 

addition of fertilisers. The amount and timing of fertilisation is dependent of the type of 

agricultural use and the soil type. Some nutrients’ content might be affected by the 

8


grassland species: clover, for example, is known to bind atmospheric nitrogen 

increasing soil nitrogen content. For the purpose of practical applications, the Irish 

institution for Agricultural Research issues the advised levels for various practices 

(Teagasc 2008). 

Since the total biomass of grasslands is lower than that of forests, it can be 

expected that given similar environmental conditions the transformational processes 

operate at a lower intensities in grasslands. In a series of laboratory experiments it was 

shown (Menyailo and Huwe 1999) that N 2 O release from natural grasslands to the 

atmosphere is lowest when compared to soil from forest ecosystems. The managed 

grasslands, however, were reported to produce very high fluxes (Scanlon and Kiely 

2003; Leahy et al. 2004; Hyde et al. 2006). While fertiliser application is the most 

striking difference in terms of supply of available nitrogen to the ecosystem, in 

pastures dung and urine patches contribute significantly towards the pool of available 

organic nitrogen, which is then transformed to nitrous oxide (Maljanen et al. 2007). 

Assessment of the N 2 O emissions that exceed natural ecosystem level of the same 

type is rarely possible: it is, therefore, widely accepted that the flux measured between 

the peak periods is similar to the background flux (Neftel et al. 2007). This, however, 

does not account for increased availability of N during these periods which might be 

responsible for higher background flux; or increased production of N 2 O in the deeper 

soil horizons (20–50 cm) that might never reach surface and being emitted, or being 

leached into the ground waters and being emitted elsewhere (Müller et al. 2002). 

Therefore, quantification of the anthropogenic agricultural emissions is essential to 

estimate the contribution of this particular human activity towards the balance of 

greenhouse gases (Bouwman 1996). 

9

Material and methods 

3. MATERIAL AND METHODS 

3.1. Site description 

The study site is located near the village of Donoughmore, Co. Cork, in South- 

Western Ireland. The geographical coordinates are 51°59' N, 8°45' W, and the mean 

elevation is 187 m above sea level (Figure 3.1). 

Figure 3.1 Location of the study site within the Republic of Ireland. 

Most of the site is poorly drained with a small area prone to winter water logging. 

The soil is a gley brown earth with an annual water table ranging from approximately 

10


0.6m to 2.5m below the surface. A portion of the land near the measurement site is 

reclaimed over 100 years ago from its original bog land. The topsoil is rich in organic 

matter to a depth of about 15 cm (about 12% organic matter), overlying a dark brown B 

horizon of sand texture (Scanlon and Kiely 2003). Within the 0–10 cm layer the soil 

bulk density was 1.02 g cm −3 , pH was 6.7, total soil organic carbon (SOC) was 4.5%, 

and total soil nitrogen was 0.35% (C:N ratio of 13). Averaged over the top 10 cm, the 

soil porosity was 0.60, the saturation moisture level was 0.57, the field capacity was 

0.32, and the wilting point was 0.12 m 3 m −3 . The dominant grass species was perennial 

ryegrass (Lolium perenne L.) with a minor presence of clover. The typical land use in 

the region is grassland with a mix of dairy and beef cattle grazing and grass harvesting 

for silage and hay. The general view of the study site is presented in Figure 3.2. 

Figure 3.2 General view of the study site near Donoughmore village, Co. Cork with the eddy 

covariance tower in the foreground 

11


Over the last few years, due to changes in agricultural regulations and practices, 

the management of grassland has become less intensive: smaller amount of nitrogen 

and other fertilisers are being applied; and smaller number of stock mean less intensive 

grazing. Information about the management practices was collected every few months 

directly from the farmers. Additionally, on a plot of land near the measurement site a 

broadleaf forest was established in early 2005. The forest was managed by an external 

company for a 4-year period until the end of 2008. 

The equipment used in this study included a variety of instruments: tipping-bucket 

rain gauges (0.1–0.2 mm resolution, ARG100 and TE525MM-L, Campbell Sci., USA), 

time-domain reflectometer probes for soil moisture (107, Campbell Sci., USA) 

different in installation depths and arrangements, air humidity and temperature probe 

(HMP 45C, Vaisala, Finland). The minute measurements from these instruments were 

averaged (or in case of rainfall, totalled) every half hour and logged to data-loggers 

(CR23x and CR1000, Campbell Sci., USA). 

3.2. The eddy covariance method 

Eddy covariance is a micrometeorogical technique that uses the properties of the 

atmospheric boundary layer (ABL) to calculate the fluxes of gas species and energy 

between an ecosystem and the atmosphere. This method is based on the ability to 

calculate the covariance of simultaneous fluctuations in the vertical component of the 

turbulent flow of air along with the fluctuations in the specific gas concentration (or 

density) for gas fluxes, temperature for sensible heat flux and horizontal wind speed for 

the flux of momentum. The nature of the turbulent flow requires high-frequency 

measurements at the scale of 10–20 Hz; precise measurements of the gas 

12


concentrations at such frequencies were until recently impossible (Kaimal and 

Finnigan 1994; Lee et al. 2004). 

The theoretical basis for the eddy covariance technique lies in the ability to 

Reynolds-average the high-frequency turbulent eddy flux over a period of time. The 

duration of the period over which the averaging can be carried out varies from a few 

minutes to several hours (or even days) depending on stationarity of the time series. 

Typical duration used across many research sites and in the global FLUXNET 

programme is 30 minutes. The flux of a gas can be expressed as: 

F = w′ρ′ 

(3.1) 

where w′ and ρ′ are fluctuations in the vertical wind speed and gas density (or 

concentration), respectively. This relationship is derived from the general definition of 

the flux: 

F = wρ 

(3.2) 

that is a flux is an amount of matter (or energy) passing through a unit area per unit 

time. When averaging over time period, each instantaneous value can be considered a 

sum of the mean value and fluctuation about the mean, for example for the vertical 

wind: 

w = w + w′ 

(3.3) 

Considering Equation 3.3, Equation 3.2 can be re-written as: 

w ρ = wρ 

+ wρ 

′ + w′ 

ρ + w′ 

ρ′ 

(3.4) 

13


Applying the Reynolds-averaging (over-bar operator) means that the correlation 

between primed and averaged values (second and third right-side terms) will equal to 0 

The positioning of the measurement instruments can be then exploited to eliminate the 

mean vertical wind speed and as a result the first term in Equation 3.4. If all the 

requirements are fulfilled then Equation 3.1 is the result. 

Eddy covariance is a non-invasive technique, that is, it does not interfere with the 

actual process of gas exchange at the soil-atmosphere boundary. Depending on the 

height of the sensor the area that the observed flux corresponds to, known as footprint 

area, might vary from the tens of metres to a few kilometres. The exact value will 

differ depending on the type of type of vegetation, atmospheric conditions and the 

height of the instruments. 

In this study an explicit algebraic expression by Hsieh et al. (2000) was used to 

estimate the footprint. This method defines two characteristics of the footprint: peak 

and fetch, corresponding to the most-contributing and 90% distance in the down-wind 

direction, respectively. While assumptions regarding the distribution of the 

contributing areas from the measuring sensors might not always hold, inclusion of the 

ABL parameters improves the reliability of the calculations over the previously-used 

rule of thumb: fetch to sensor height ratio set to 100. 

The equipment that was used in this study was all produced by Campbell Sci., 

USA: 3-D sonic anemometer CSAT3 and tuneable-diode laser trace-gas analyser (TDL 

TGA 100A), both collecting data at 10 Hz and installed at 6 m on a meteorological 

tower. The TGA uses absorption spectroscopy to measure gas concentration in the air 

sample. A built-in Nafion ® drier prevents water vapour entering the sampling cell, 

eliminating the need for WPL correction The 10 Hz-records of 3-dimensional wind 

14


speed, sonic temperature and CSAT diagnostic word, along with N 2 O concentration 

were recorded on a CR1000 datalogger (Campbell Sci., USA). 

used: 

All processing and corrections were done in-house. The following algorithm was 

1. The raw values were filtered out if they exceeded absolute limits (25 m/s 

for u and v wind components, 5 m/s for w, 100 to 500 ppb for N 2 O 

concentration, −15 to 35 °C for sonic temperature, CSAT diagnostic flag 

raised/non-zero). The record is kept of what parameters have failed and all 

values corresponding to a given time stamp are discarded. 

2. The N 2 O concentration time series is aligned with other time series by 

shifting it forward by 9 records (0.9 s) that corresponds to tube travel time 

previously established for the current setup (Scanlon and Kiely 2003). 

3. De-spiking at three standard deviations of the concentration time series is 

carried out: number of spikes is recorded. 

4. If the percentage of remaining good values (i.e., values that can be used in 

further calculations) is less than 95%, averaging is suspended and the 

period is recorded as a gap. As the algorithm demonstrates so far, the 

reasons for gaps generated at this stage are mostly of an instrumental 

nature. 

5. Yaw and pitch rotations carried out and followed by linear-regression detrending. 

15


6. The average 30-min fluxes are calculated along with the number of 

adjacent statistical parameters from u, v, w, c and T time series and their 

permutations. 

7. A stationarity test based on Foken and Wichura (1996) is then carried out, 

using pre-calculated 5-min averaged flux values to test for the atmospheric 

stationarity during half-hourly period. In case the stationarity condition was 

not upheld, the 30-min flux value was discarded and was considered a gap 

in the time series. The gaps associated with this stage of algorithm have to 

do with atmospheric conditions. 

Despite the different nature of the gaps in the time series of instantaneous 30-min 

averaged flux, in our analysis all gaps were treated similarly, since no further 

information could be extracted from unknown values. 

The programmatical implementation done in Python programming language was 

flexible enough to vary most of the presented parameters and values, even if it was not 

required in the final version of the analysis. The code is included into the Appendix 2 

for reference. 

16

Distinguishing peaks from background emission of nitrous oxide 

4. DISTINGUISHING PEAKS FROM BACKGROUND EMISSION 

OF NITROUS OXIDE 

Mishurov Mikhail, Paul Leahy and Gerard Kiely 

Department of Civil and Environmental Engineering, University College Cork, 

Cork, Ireland 

4.1. Abstract 

Nitrous oxide, a powerful greenhouse gas, has a temporally and spatially variable 

emission pattern. Two different modes of flux are widely recognized: high-intensity 

short-term bursts; and low-intensity long-duration (background) fluxes. Either mode 

can be an emission or an uptake, but emission dominates. However, there is no 

quantitative approach for the separation of these two modes. In this study we propose 

an original method of analysing eddy covariance N 2 O flux time series to estimate these 

two modes. We found that peak flux occurrences are irregular but are related to trigger 

events such as rainfall occurrences and fertilisers’ applications; and uptake and 

emission peaks sometimes appear in close sequences. We observed flux events, 

emission and uptake, that lasted up to 18 and 6.5 hours, respectively. Emission events 

were longer on average and of higher intensity than uptake events. Additionally, this 

method was shown to perform well when applied to the aggregated (daily) time series. 

The proposed technique will be of use for modelling as well as for the estimation of 

peak fluxes. 

Keywords: nitrous oxide; time series; grass land; eddy correlation 

17


4.2. Introduction 

Nitrous oxide (N 2 O) is a powerful greenhouse gas (GHG) having, according to the 

IPCC 4 th assessment report (2007), a warming potential of 298 in a 100-year horizon. 

Additionally, N 2 O as a highly reactive gas is involved in the reduction of stratospheric 

concentration of ozone. A recent article by Flechard et al. (2007) provides an overview 

of the possible magnitude of nitrous oxide fluxes characteristic for European grassland 

ecosystems. It is remarkable how high the variability is within various management 

practices, i.e., fertilized / no grazing, no fertilizer / no grazing, etc. The range annual 

fluxes was from −0.50 to 6.48 kg N 2 O–N ha −1 a −1 (positive is emission). Some of the 

variability is likely due to climatic differences between sites, however, reported yearto-year 

differences in any single site often exceed a hundred percent or of order 1 to 2 

kg N 2 O–N ha −1 a −1 . Instantaneous fluxes across sites varied between −300 and 1500 ng 

N 2 O–N m −2 s −1 . Similar inter-annual variability in an Irish grassland was observed by 

Hyde et al. (2006). 

The temporal and spatial emission patterns of N 2 O are complex; unlike carbon 

dioxide (CO 2 ), the diurnal pattern of N 2 O emission is superseded by the influence of 

other factors, such as rainfall or N deposition (Skiba and Smith 2000). On a longer time 

scale, the flux pattern can be classified into two parts: short duration intensive “bursts” 

separated by long periods of relatively low-intensity flux, also known as background 

flux. The former are typically associated with recent applications of N fertilizers and 

recent rainfall events combined with suitable soil temperature (Dobbie et al. 1999; 

Scanlon and Kiely 2003; Barton et al. 2008). The background emission mode generally 

depends on the soil nitrogen amount and its availability, type of soil and regional 

climate (Jørgensen et al. 1998; Leahy et al. 2004). Hence, if there are no drastic 

18


changes in management practices or environmental conditions, the contribution of this 

low-intensity mode to the annual sums may be considered constant. However, it is 

widely reported (Leahy et al. 2004; Hsieh et al. 2005; Calanca et al. 2007; Jones et al. 

2007) that the dominant part of emissions occurs in the form of burst events closely 

associated with the timing of applications of N-fertilizers, both mineral and organic. 

Hsieh et al. (2005) estimated with the DnDc model that such events might account for 

up to 85% of total N 2 O emissions on an annual basis. Fertilizers alone, however, do not 

regulate or produce peaks: only under favourable environmental conditions their 

application could trigger an emission event (Scanlon and Kiely 2003). On the other 

hand, there exists a class of natural peaks associated with frost-thawing events 

(Calanca et al. 2007) or with a rain event after an extended dry period (Dobbie and 

Smith 2003b). 

It is worth emphasizing that the both modes of nitrous oxide emission (and uptake) 

are associated with the different dynamics of environmental parameters: quick changes 

in soil water content, soil temperature or N-availability are likely to trigger a peak 

event in a flux time series. There is a need for an improved understanding of peak and 

background flux, one that would go further than a simplistic “anthropogenic peaks” 

and “natural background” approach, since some peaks might be of non-anthropogenic 

origin. 

The quantitative separation of one mode of emission from the other is a first step to 

an improved understanding. Existing attempts to separate peak emissions from 

background emissions (Scanlon and Kiely 2003; Flechard et al. 2005) remain 

qualitative in nature, using only visual inspection of fluxes from chamber-obtained 

time series of N 2 O fluxes. Such an approach is appropriate for infrequent static- 

19


chamber measurements, but it is inappropriate for the flux time series obtained with the 

eddy covariance (EC) technique. 

The EC technique provides good-quality measurements of ecosystem-wide 

emissions at different time scales and around the clock under a variety of weather 

conditions. Therefore, it is important to conduct time series analysis without prior 

knowledge of weather conditions or management practices, such as the timing of 

fertilizers’ applications, grazing, etc. 

The aim of this paper is to explore and establish a quantitative approach that can be 

used with EC N 2 O measurements to distinguish peak events from the background N 2 O 

fluxes and to investigate the environmental factors driving these fluxes. 

4.3. Methods 

4.3.1. Site description and data collection 

The eddy covariance (EC) tower is located at an agricultural site near the village of 

Donoughmore in County Cork, Ireland. The geographical coordinates are 51°59' N, 

8°45' W, and the elevation is 187 m a.s.l. The tower is centred in a grassland area (see 

Scanlon and Kiely (2003) Fig. 1 for the location). 

Eddy covariance measurements began in 2002. However, in the south-west wind 

sector there is now a young broadleaf forest (planted in 2005) with trees at a height up 

to 1 m in 2008 which can be considered rough grassland for the year 2007. The sector 

south-west to north-east is the prevailing wind direction. 

The time series in this paper covers the period from mid April until the end of 

December 2007 (259 days in total). The fields within 500 m of the EC tower are split 

20


between two farms. The farm in the northern direction is of low-to-medium 

management intensity with some beef cattle and crops. The second is a dairy farm of 

medium-to-high intensity, located in the southern sector. Regular surveys of the farm 

management included information on the type and date of application of mineral and 

organic animal fertilizers (slurry) as well as dates of grass cutting for silage and animal 

grazing periods. 

The EC system includes: a 3-D sonic anemometer CSAT3 (Campbell Sci., USA) 

installed at 6 m height; a closed-path tuneable diode laser trace gas analyzer 

(TGA100A, Campbell Sci., USA) used to measure nitrous oxide concentration at 10 

Hz and a temperature and relative-humidity probe (HMP45C, Campbell Sci., USA). A 

built-in Nafion ® drier prevents water vapour entering the sampling cell, eliminating the 

need for WPL correction. The EC system generates 10 Hz records of 3-D wind speed 

N 2 O concentration and CSAT3 diagnostic word, logged with a CR1000 data logger 

(Campbell Sci., USA). Additional on-site instruments collect half-hour records of soil 

temperature (thermistore probes CS107, Campbell Sci., USA), soil moisture (TDR 

CS615, Campbell Sci., USA) and precipitation (generic tipping bucket rain gauges, 

ARG100 and TE525MM-L, Campbell Sci., USA). 

4.3.2. Flux calculation 

Access to raw 10 Hz data allows for control over calculations of fluxes and quality 

assurance. Pre-filtering of raw 10 Hz data and post-filtering of estimated fluxes are 

used to maintain data quality. The main algorithm includes: shifting the concentration 

series by a constant period of time to account for a tube travel time (0.9 s); de-spiking 

(at three standard deviations), yaw and pitch rotations, de-trending of the concentration 

series, followed by the calculation of flux and adjacent statistics. The primary 

21


averaging interval used in this research is 30 minutes, even though our implementation 

potentially allows for a greater variety of averaging intervals from 1 min to 24 hours. 

The half-hour averaging interval is widely accepted and incurs a lower penalty in the 

form of faulty (gap) periods than longer averaging periods. As shown by Voronovich 

and Kiely (2007), the half-hour interval is representative of a wide variety of the 

averaging intervals. It is also in keeping with meteorological data and is a standard in 

the FLUXNET global research program. The filtering is based on a stationarity test 

(Foken and Wichura 1996). No gap-filling was performed on the time series and the 

gaps accounted for 25.1% of all records; for the purpose of calculating cumulative 

sums of fluxes, gaps were considered to be neutral periods, i.e., null-flux periods. The 

post-processed half-hourly fluxes are presented in Figure 4.1. 

Figure 4.1 Full nitrous oxide flux time series for the period of mid April to end December 2007 

(averaging interval 30 min). The arrows indicate known dates of fertilizer applications. 

The total flux for 259-days period was 3.03 kg N 2 O–N ha −1 . The instantaneous flux 

values ranged from −370 to 770 ng N 2 O–N m −2 s −1 . This range corresponds well to that 

reported by Flechard et al. (2007): between −300 and 1500 ng N 2 O–N m −2 s −1 . The 

positive-flux (emission) periods dominates the time series, with 56.2% vs. 18.7% ratio 

22


between positive and negative flux periods, gaps accounted for the remaining 25.1%. 

There are easily distinguishable extreme peak values within the time series. Some are 

preceded by the application of the fertilizers (the timing is indicated by the arrows in 

Figure 4.1). However, in some cases, extreme fluxes appear to have not been related to 

the timing of fertilizers applications. In Figure 4.1, where the arrows indicate when 

fertilizer applications were made, it is important to note that the applications were 

typically made only over a part of the EC footprint. Our intention was to avoid using a 

visual—inherently subjective—technique for the purpose of separating peaks from 

background fluxes. 

4.3.3. Distinguishing technique 

We assume that the peak flux values can be treated as outliers in relation to the 

background flux. In this case, the total period of peak flux cannot exceed half of the 

total flux period under consideration. Based on this assumption we employed a 

backward elimination technique with the following test for a single outlier. A 

“suspected” outlier value is normalized according to: 

y = |x – µ| / σ (4.1) 

where µ and σ, are mean and standard deviation of a “clean” time series. Not to 

pass the outlier test, a normalized value should be smaller than 3. 

The proposed technique is summarized as follows: 

1. The time series of observed fluxes is partitioned into two sub-sets of 

approximately equal size: one containing potential peak values and one 

certainly (according to the above stated assumption) containing only 

background flux values. In our implementation we used an interquartile 

23


range, i.e., difference between lower and upper quartiles, to accomplish this 

step. 

2. The least extreme of the outlier values (from the previous step) is 

normalized according to Equation 4.1 and tested. 

3. If the test is not passed, i.e., the test value is less than 3, the value is moved 

from the “potential peaks” part to “certain background”. New mean and 

standard deviation values for the enlarged “certain background” series are 

calculated and step 2 is repeated. 

4. If the test described in step 2 is passed, the peak and background portions 

of the time series are considered separated. 

4.4. Results 

The full time series of fluxes shown in Figure 4.1 was partitioned into the 

background and peak fluxes. The resulting background and peak fluxes are presented 

in Figure 4.2. 

The background flux periods constituted 93.0% of the time series, excluding the 

gaps, and the remaining 7.0% were taken by the peaks. When the whole time series 

was considered the 69.6 and 5.3% corresponded to background and peaks, respectively, 

with 25.1% of gaps. In terms of emission values, these corresponded to 2.24 and 0.79 

kg N 2 O–N ha −1 for background and peaks, respectively, and amounted to 73.9 and 

26.1% out of the total flux registered during the study period (3.03 kg). 

The magnitude of the instantaneous background flux was in the range −58 to 87 ng 

N 2 O–N m −2 s −1 , with a mean value of 14 ng N 2 O–N m −2 s −1 . The background uptake to 

24


emission periods ratio was 1:3.1, whereas the negative and positive peak periods were 

distributed in 1:2 ratio. 

Figure 4.2 Background (a) and peak (b) portion of the flux (note difference in scale of a flux 

axis for background portion). The arrows indicate known dates of fertilizers’ applications. 

The separate contribution of positive (emission) peak flux was 1.22 vs. −0.42 kg 

N 2 O–N ha −1 for negative (uptake) peaks. As Figure 4.2b demonstrates, in many cases 

fertilizer applications were not immediately followed by the peaks; and peaks were 

registered up to a few months following the fertilisation. 

The peak fluxes are distributed unevenly throughout the observation period. It is 

interesting to note that sometimes high and low magnitude peak fluxes tend to occur in 

groups, irrespective of flux direction, i.e., whether it is an emission or an uptake flux. 

Such behaviour can be attributed to a dependency of peak appearance on the short 

25


irregular events: intensive rainfall, fertilizers’ applications, etc. In the background 

portion of the time series, no such phenomenon was observed, which confirms that 

background flux notion as a permanent low-intensity flux. Many peak periods (about 

42%) occur surrounded by non-peak periods, be it background periods or gaps, 

suggesting possible limitations on the variables influencing N 2 O production. 

4.5. Discussion 

4.5.1. Peak triggers 

When peak occurrences are analysed with respect to preceding meteorological and 

management events, it is notable that certain conditions caused significant changes in 

the frequency of peak occurrence (Figure 4.3). Compared to the average “peak rate” of 

5.3%, the peak occurrence increases more than twice that value within 22 hours of an 

occurrence of intense (>3 mm / 30 min) rainfall. Across all rain intensities it is notable 

that the maximum peak rate values occur between 12 and 40 hours after the rain event, 

but stay elevated for as long as 4 days after the event. 

Interestingly, periods immediately following rainfall were characterised by 

significantly diminished (as low as zero) peak rates, and the duration of these periods 

was up to 3.5 hours. In our opinion, it might be explained by saturation of the soil 

pores in the surface layer, preventing any intensive gaseous exchange with the 

atmosphere. Within two hours after heavy rains (> 3.2 mm/30 min) no reliable flux 

values were observed, producing gaps in the flux time series. This was likely due to 

wetting of the surface of the transducers of the 3D sonic anemometer resulting in its 

temporary failure as indicated by CSAT diagnostic value. 

26


Figure 4.3 Effect of rainfall events on peak occurrences. The rainfall intensity values 

correspond to rainfall greater or equal than the given values. The colour bar values are the 

percentage of peak occurrences during given period. 

A somewhat similar shape of peak-rate curve was observed with respect to 

fertilisers’ applications (data not shown). The maximum is reached between 50 and 65 

hours after the event, with other local maxima at about 10, 30 and 80 hours. The initial 

dip in the peak rates also occurs slightly later, between 2.5 and 5 hours. All these 

features of the curve, in our opinion, show that the consumption of fertilisers is a multistage 

process, where the chemical form of fertilisers and their carbon content (in case 

of organic fertilisers) play a major role. 

It is necessary to mention that such straightforward analysis did not reveal any 

possible multiplicative effect of triggers when periods overlap. It is, however, a good 

indication of the scale at which triggers act as well as the approximate response time. 

27


Unfortunately, we were not able to conduct such a test due to relatively low accuracy 

of the management data. 

4.5.2. Peak event 

The notion of an emission/uptake event implies long and continuous peak flux, 

extending for days or even weeks. However, this understanding was developed 

primarily for the daily-averaged flux time series. In our analysis of half-hourly time 

series we did not observe events of such long duration (see Validation subsection for 

details regarding daily series analysis). Figure 4.4 presents a few examples of emission 

and uptake events that we were able to identify. Typically, an emission or uptake event 

appears to have an almost bell-curve shape with rising and falling limbs, in terms of 

absolute values. It can be seen, that overall a bell shape curve of the event extends 

beyond immediately adjacent periods, i.e., there is a visible trend among close peak 

periods that are separated by non-peak periods. The explanation for this variation may 

lie in the variability of the wind direction that might transport low flux from nonemitting 

fields to the sensor. When breaks and shape are taken into account, sustained 

events with durations up to 18 hours can be identified. The duration of uptake events is 

shorter on average and does not exceed 6.5 hours. 

28


Figure 4.4 Examples of events. Light grey are periods of peak flux, dark grey are periods of 

background flux. Dates: a – 25 th April, b – 5 th June, c – 16 th May. 

The identification of a peak event remains a subjective process. The event 

presented in the Figure 4.4b was estimated to be 9.5 hours in length, spanning between 

10.00 and 19.00 hrs. The inclusion of a 2 hour non-peak period (4 half-hourly 

episodes) into the event is allowed due to the trend in the adjacent peak values. 

Likewise, events in Figures 4.4a and 4.4c, in our opinion are described as 10.5 and 

4 hour long, respectively. We were unable to overcome this subjectivity in technique 

when evaluating peak events, but we hope that criteria can be established to 

quantitatively define this phenomenon. 

The peaks tend to occur during the daily hours: 63.0% of peaks were observed 

between 9:00 and 18:00. The length of this “daily” period corresponds to 37.5% of the 

whole time series. 

29


4.5.3. Validation 

We compared the results of our analysis with previously published data from the 

same location for the year 2003 (Hsieh et al. 2005). They report gap-filled daily fluxes, 

which makes the time series significantly shorter—only 365 values—and, more 

importantly, of different time scale. Using our approach for the 2003 data, 7.32 kg 

N 2 O–N ha −1 (62.8% of total annual flux) was delivered over 33 peak-value days (9.0% 

by time). Given the errors involved in modelling with DnDc (the annual fluxes were 

overestimated by almost a third) and the model being unable to account for negative 

fluxes, our estimation reflects the partition between peaks and background more 

accurately than DnDc partitioning determined by Hsieh et al. (2005) (85 vs. 15%). The 

length of emission events according to our analysis was typically up to 4 days, with a 

single event up to 14 days long, which is in good agreement with the estimates of 

Hsieh et al (2005). 

A daily time series, aggregated from half-hourly time series of our 2007 data, was 

partitioned into 11-days long (4.3%) peak series and 242-day long (93.4%) background 

series; gaps comprised 2.3% of the time series. When applied to flux, the 

corresponding values were 0.77 and 2.26 kg N 2 O–N ha −1 for peaks and background, 

respectively (cf. 0.79 and 2.24 kg N 2 O–N ha −1 

for half-hourly time series). This 

remarkable closeness suggests that the daily time series might be adequate to use for 

background-emission estimation. Only two events were registered in the daily time 

series with lengths up to 6 days. 

4.5.4. Choice of technique 

In developing the technique we strived for a conceptually- and computationallysimple 

approach. One might suggest that this led to over-simplification and the use of a 

30


subjective criterion. There are a number of statistical methods developed for 

identification of outliers (Hawkins 1980); however, most of them require an 

assumption regarding the distribution of a time series or its non-outliers containing 

portion. In our opinion, a priori restricting time series with such an assumption would 

be inadequate. Assuming a certain cut off value (in our case 3σ) is—in essence— 

similar to using a certain value of significance level, which is required for all the tests 

we were able to find. 

4.6. Conclusions 

To separate the high intensity short-term peak fluxes from the low intensity 

background flux, which dominates the nitrous oxide flux time series, we proposed and 

successfully employed a new simple technique. It was tested with a short intensive 

time series. According to our calculations (for a 259-day period time series) peak 

fluxes occurred in 7.0% of all flux periods and produced 0.79 kg N 2 O–N ha −1 (26.2%) 

out of a total 3.03 kg N 2 O–N ha −1 . 

Peak fluxes can sometimes occur in sequences of varying length (treated as 

emission or uptake events). Our observations show that the length of the uptake events 

is usually shorter, with a median around 2 hours, whereas emission events could last up 

to 18 hours and are longer on average. The quantitative assessment of trigger events 

shows that peaks are more likely to occur between 12 and 40 hours and between 50 and 

100 hours after rainfall and fertiliser applications, respectively. 

It is proposed that this method can be used in separating the background and peak 

N 2 O fluxes in a time series of sufficient length, i.e. month to year, or similar. 

31


4.7. Acknowledgements 

We would like to acknowledge support of the Department of Agriculture of Irish 

Government under Research Stimulus Fund Programme (RSF 06 372) and 

Environmental Protection Agency of Ireland under CelticFlux Programme (2001 – CC 

/ CD – (5/7)). Killian Murphy provided essential instrumental and data collection 

support for this research. 

32

Nitrous oxide flux dynamics of grassland undergoing afforestation 

5. NITROUS OXIDE FLUX DYNAMICS OF GRASSLAND 

UNDERGOING AFFORESTATION 

Mishurov Mikhail and Gerard Kiely 



Submitted to the journal “Agriculture, Ecosystems and Environment” on 2 nd 

February 2010 and is currently under review. 

5.1. Abstract 

In Ireland fertilised grasslands are major source of the nitrous oxide (N 2 O), a 

powerful greenhouse gas. We present five years (2004–2008) of eddy-covariance (EC) 

observations of N 2 O fluxes from an ecosystem transitioning from wet managed 

grassland to broadleaf forestry. One sector of the EC footprint was converted to 

forestry during the observation period, while the remainder of the footprint remained 

under intensively managed grassland. The daily N 2 O fluxes were responsive to the 

rainfall and changes in soil moisture, whereas the annual and monthly emissions 

depended on the amount of nitrogen fertilisers applied. During the year of forest 

establishment, mechanical disturbances increased N 2 O emissions from the forest 

sector, even higher than the adjacent grassland; in the following 3 year, the intensity of 

the nitrous oxide flux dropped approximately three times from the previous “grassland” 

level of emission. At the same time, the grassland sector recorded approximately a 20% 

decrease of flux intensity, coinciding with a reduction in fertiliser application. These 

observations led us to conclude that while reduced fertilisation and the cessation of 

33


grazing contributed to the reduction of N input, a certain period is required for the new 

forest to mature and attain a stable level of N 2 O emissions. 

Keywords: forest, afforestation, nitrous oxide, grassland, eddy covariance 


Nitrous oxide (N 2 O) is a powerful greenhouse gas having, according to the IPCC 

4 th assessment report (2007), a warming potential of 298 (relative to CO 2 ) in a 100-year 

time horizon. Additionally, N 2 O as a highly reactive gas is involved in the reduction of 

stratospheric concentration of ozone (Crutzen 1970). One of the major parameters 

regulating the emission of nitrous oxide from soils is the type of land use (Skiba and 

Smith 2000). Relevant aspects of land use include: input of organic or inorganic 

fertilizers; animal grazing (creating concentrated urea and manure patches); and the 

ecosystem and plants’ ability to fix molecular nitrogen from the atmosphere (Smith 

2005). 

It is known that the emission rate of nitrous oxide from mature forests is on 

average higher than that from natural (unfertilised) grasslands, with deciduous forests 

having higher emissions than coniferous forests (Menyailo and Huwe 1999; von 

Arnold et al. 2005). The observed difference is attributed to species-specific soil 

microbiological community composition. Fertilised grasslands and pastures typically 

have higher emission rates than forest and most natural ecosystems (Flechard et al. 

2007). The literature on nitrous oxide emission from forests focuses on the mature 

stands (Kiese and Butterbach-Bahl 2002; Ullah et al. 2008) and ecosystems 

regenerating after clear-felling or logging (Zerva and Mencuccini 2005; Yashiro et al. 

2008). Generally, while increased mineralization rates after clear felling may 

34


significantly increase N 2 O emission in the short term (Steudler et al. 1991; Huttunen et 

al. 2003), the flux from undisturbed mature stands is governed by the tree type and the 

predominant hydrological situation (Jungkunst et al. 2008; Ullah et al. 2008). 

However, the parameters affecting N 2 O emission differ significantly between 

young and mature forests and include: distribution and quality of organic matter (OM); 

C:N ratio; microbial community composition and hydrological conditions at the site 

(Inagaki et al. 2004; Gartzia-Bengoetxea et al. 2009; Macdonald et al. 2009). 

Ball et al. (2002) showed that afforestation of well-drained arable land can lead to 

a continuous decrease in N 2 O emissions over several years. This result, however, has 

not been confirmed and no data exist for different types of soil or hydrological 

conditions. In addition we have found no published data on N 2 O emissions from newly 

afforested grasslands. While it is likely that similar phenomenon might occur due to 

reduced fertilisation and soil disturbance, changing soil moisture and temperature could 

tip the balance towards increased N 2 O emission. Such observations would provide 

information on the magnitude of background-type emission, intra-annual emission 

patterns and vital information regarding changes occurring in a changing ecosystem. 

One of the most remarkable qualities of nitrous oxide emission is the magnitude of 

variability within similar ecosystems and even the temporal variability within a single 

site. An overview of N 2 O emission from European grasslands (Flechard et al. 2007) 

demonstrated the range of possible magnitudes of annual fluxes. While the median 

values between extreme management practices in grasslands were between 0.17 to 0.74 

kg N 2 O–N ha −1 a −1 (mean values range: 0.32 to 1.77 kg N 2 O–N ha −1 a −1 ), the range of 

all annual fluxes was from −0.50 to 6.48 kg N 2 O–N ha −1 a −1 (negative is uptake). Some 

of the variability is likely due to climatic differences between sites. However, the 

35


reported year-to-year differences in any single site often exceed a hundred percent or of 

order 1–2 kg N 2 O–N ha −1 a −1 . Similar results in a grassland in Ireland were obtained by 

Hyde et al. (2006). Leahy et al. (2004) have shown that fertilizer-driven emission may 

be a major factor determining whether managed grasslands is a net global warming 

potential (GWP) sink or source. In such cases, conversion of the grassland to forestry 

and associated reduced fertilization is likely to lead to an increased role of CO 2 in the 

GWP balance and a reduced role of N 2 O. 

Uptake (negative) fluxes of N 2 O are observed less frequently during forest 

measurements, but fluxes between −0.02 to 7.0 kg N 2 O–N ha −1 a −1 were reported for 

deciduous temperate and mixed boreal forests (von Arnold et al. 2005; Matson et al. 

2009). This upper limit is close to the emission rate after clear cut at a Puerto Rican 

sub-tropical wet forest (6.7 kg N 2 O–N ha −1 a −1 ), with the flux of 0.48 kg N 2 –N ha −1 a −1 

at an undisturbed reference plot (Steudler et al. 1991). The flux rates characteristic for 

the wet tropical forests on Australian krasnozems were reported to be in the range of 

4.36–7.45 kg N 2 –N ha −1 a −1 (Kiese and Butterbach-Bahl 2002) with particularly 

intensive emissions during the wet rainy season. The detailed information about these 

forest N 2 O studies is summarized in Table 5.1. 

In this paper we present a unique data set of continuous eddy covariance N 2 O flux 

observations of an ecosystem transitioning from fertilised grassland to a newly planted 

broadleaf forest over a five year period. The aim of this paper was to study the 

dynamics of nitrous oxide emission to quantify the change in N 2 O emissions 

accompanying afforestation of grassland on an impeded-drainage gley soil. 

36


Table 5.1 Details of the selected publications on the nitrous oxide studies in the forests. 

Site 

Central 

Saskatchewan, 

Canada 

Asa 

Experimental 

Forest, 

Sweden 

Liquillo 

Experimental 

Forest, Puerto 

Rico 

Pin Gin Hill, 

Australia 

Kauri Creek, 

Australia 

Bellender Ker, 

Australia 

Tyllicorthie, 

Scotland 

Bush Estate, 

Scotland 

Forest type 

Mixed boreal 

Deciduous 

temperate 

Sub-tropical 

wet 

Wet tropical 

Mixed 

Tree type / 

conditions 

Jack pine 

Soil 

Orthic eutric 

brunisol 

Black spruce Peaty-phase 

gleysol 

Aspen 

Orthic grey 

luvisol 

Mean annual 

precipitation, 

mm 

Annual N 2O 

flux, kg 

N 2O–N ha −1 Years Reference 

a −1 

594 0.05–0.08 

541 −0.02–0.13 

587 0.12–0.14 

Drained birch 1.3 

Drained alder Peaty 

organic soil 

662 3.8–7 

Undrained 

alder 

0.6 

After clear-cut 6.70 

Hurricane 

damaged 

Undisturbed 

Undisturbed 

Planted in 

1995 

Planted in 

1990 

Acid upland 

ultisol 

ca. 3500 

1.45 

0.48 

Krasnozems 3609 6.89 

Ustochrept 

Cambisol 830 

Cambisol 

Gleysol 

1594 4.36 

4395 7.45 

845 

1–1.5 

0.2 

2006– 

2007 

2000– 

2002 

1989– 

1990 

2000– 

2001 

1996– 

1999 

Matson et 

al., 2005 

von Arnold 

et al., 2005 

Steudler et 

al. 1991 

Kiese and 

Butterbach- 

Bahl, 2002 

Ball et al., 

2002 

5.3. Methods 

5.3.1. Study site 



elevation is 187 m (above sea level). Most of the site is poorly drained with a small 

area prone to winter water logging. The soil is a gley brown earth with an annual water 

table ranging from approximately 0.6m to 2.5m below the surface. The topsoil is rich 

in organic matter to a depth of about 15 cm (about 12% organic matter), overlying a 

37


dark brown B horizon of sand texture (Scanlon and Kiely 2003). Within the 0–10 cm 

layer the soil bulk density was 1.02 g cm −3 , pH was 6.7, total soil organic carbon 

(SOC) was 4.5%, and total soil nitrogen was 0.35% (C:N ratio of 13). Averaged over 

the top 10 cm, the soil porosity was 0.60, the saturation moisture level was 0.57, the 

field capacity was 0.32, and the wilting point was 0.12 m 3 m −3 . The typical land use in 


for silage and hay. 

The study period was from 2004 to the end of 2008. In 2005 a sector of the eddy 

covariance (EC) footprint was converted into a broadleaf forestry (Figure 5.1). The 

chronology of land use of this sector was: 

• Up to and including 2004 the land use was intensive grassland (dairy pasture) 

with approximately 300 kg N ha −1 a −1 applied. The dominant grass species was 

perennial ryegrass (Lolium perenne L) with a minor presence of clover. 

• In 2005 – the soil was prepared in the February–March period and the 

broadleaf tree seedlings were planted in the April. The young forest species 

composition was 30% Black Alder (Alnus glutinosa) and 70% European Ash 

(Fraxinum excelsior). 

• From 2006, the trees grew from a seeded height of approximately 0.5 m to 

approximately 2 m by the end of 2008. 

38


Figure 5.1 Map of the study area and the footprint showing the sector converted to forestry 

from grassland in 2005. The straight lines indicate sector that was used to separate the forest 

sector from grassland. Ground elevation contours indicate elevation above see level in metres. 

Non-shaded areas represent non-agricultural spaces. 

For consistency, hereafter this sector is referred to as “forest” sector despite its pre- 

2005 land use as grassland. The forest sector, as shown in Figure 5.1, had the size of 36 

degrees. The forest extends at least for 300 m from the eddy covariance tower, so that 

most of the flux coming from the direction of this sector originates within the forest 

boundaries. 

5.3.2. Nitrous oxide flux measurements 

The N 2 O fluxes were determined using the eddy covariance (EC) technique. Wind 

speed and direction at 10 Hz were obtained with a CSAT 3-D sonic anemometer 

(Campbell Sci., USA). The concentrations of N 2 O at 10 Hz were obtained with a 

closed-path trace gas analyser (TGA 100A, Campbell Sci., USA). All data was logged 

on a CR1000 datalogger (Campbell Sci., USA). The TGA 100A measures the 

39


concentration of the gas sample using tuneable diode laser absorption spectroscopy 

which allows for fast and sensitive measurements (Edwards et al. 2003). The sonic 

anemometer and air intake for gas measurements was installed at 6 m height on the EC 

tower. 

While setup costs of the EC technique are higher than those of more widely used 

closed chamber method, the maintenance costs and requirements are typically much 

lower. Ground instrumentation included soil temperature probes at the depth of 1.5 cm 

(Campbell Sci, USA), time-domain reflectometry (TDR) probes for soil moisture at 5 

cm (Campbell Sci., USA) and rain gauges (Campbell Sci., USA) installed within 5 

metres of the EC tower in the grassland sector. Due to the special location of the 

converted forest sector to the west of the EC tower, all flux coming from the direction 

corresponding to the forest sector was considered to be the forest flux (Figure 5.1). 

The basic idea behind the eddy covariance method is that of measuring the high 

frequency (in our case 10 Hz) vertical wind speed and gas species concentration. In the 

case of stationary atmospheric conditions, the time series of these two parameters are 

Reynolds-averaged and the covariance represents the ecosystem flux from the footprint 

area. The analytical model of Hsieh et al. (2000) was used to calculate the footprint of 

the EC system. Such experimental setup allows for continuous measurements while the 

more traditional closed-chambers technique typically is at a frequency of about one 

measurement per day or per week. 

The flux of the trace gas can be expressed as: 

F χ = wρ χ (5-1) 

40


where w is a vertical wind velocity and ρ χ is a density of trace gas. Each of these 

two components can be separated into the mean and fluctuation parts. The eddy 

covariance technique then exploits the instrument positioning to eliminate parts 

containing mean values and the final flux value is the product of fluctuations of both 

vertical wind speed and trace gas density ( w ′ρ′ 

χ ) within a certain averaging interval 

(Fowler et al. 1995). 

The primary averaging interval used in this study is 30 minutes, even though our 

implementation potentially allows for a greater variety of averaging intervals from 5 

min to 24 hours. The half-hour averaging interval is widely accepted and incurs a lower 

penalty in the form of faulty (gap) periods than longer averaging periods. As was 

previously shown by Voronovich and Kiely (2007) the half-hour interval is 

representative of a wide variety of the averaging intervals. It is also in keeping with the 

meteorological data and is the preferred time interval in the FLUXNET global research 

programme (FLUXNET 2009). 

To ensure data quality, the following procedure was employed: (1) pre-filtering of 

raw 10 Hz readings; (2) shifting the concentration series by a constant period of time to 

account for the tube travel time (0.9 s); (3) yaw and pitch rotations; (4) de-spiking (at 

three standard deviation), de-trending and de-meaning of the concentration time series, 

following by (5) the calculation of flux and adjacent statistics; (6) the post-filtering 

with stationarity test (after Foken and Wichura 1996). 

The gaps in the data, mostly due to weather conditions, normally accounted for 30 

to 45% of all annual records. Due to significant datalogger downtime, the period 

between June 2006 and March 2007 was excluded from the analysis. No gap-filling 

41


was performed on the time series. For the purpose of calculating cumulative sums of 

fluxes, gaps were considered to be neutral periods, i.e., null-flux periods. Hence our 

annual sums are underestimates of the true values. 

5.4. Results 

The daily values of water-filled pore space (WFPS), rainfall, soil temperature and 

N 2 O flux are presented in Figure 5.2. Typical rainfall intensities were in the range of 5– 

15 mm day −1 and remained relatively constant throughout the study period, with only 

few exceptional values exceeding 50 mm day −1 . Soil temperature varied seasonally 

from 3 to 15 °C; with a mean annual temperature of about 10 °C and exhibiting little 

inter annual variation. 

Figure 5.2 Daily values of WFPS, rainfall (a), soil temperature at 1.5cm (b), and average 

intensities of nitrous oxide flux (c) from the whole footprint. The gap in soil temperature time 

series was due to the failed sensor. Flux values between May 2006 and March 2007 were 

excluded from the time series due to the high percentage of gaps caused by failed data logger. 

42


Intra annual variation of the rainfall was well reflected in the WFPS time series. 

Both Figure 5.2a and the Table 5.2 show that summer 2008 lacked the typical dry 

period of any significant duration. The summer rainfall of 2008 exceeded the average 

value for the previous four years by 31%. Since spring and summer are the main 

seasons of fertiliser applications and cattle grazing, deviations from the long-term 

averaged values during those seasons play an important role in the annual sums of the 

N 2 O emission. 

Table 5.2 Meteorological information by quarters and years. The first letters of the months are 

used to indentify the quarter. Values marked with asterisk were averaged over the periods that 

included a prolonged gap period. The upper value in the quarterly cell is total rainfall and the 

lower value is average soil temperature. 

Year 

2004 

2005 

2006 

2007 

2008 

Rainfall (mm) and temperature (°C) by 

quarters 

JFM AMJ JAS OND 

394.8 

5.3 

415.4 

6.0 

332.6 

- 

483.6 

6.2 

513.8 

6.3 

254.8 

10.1 

302.6 

9.9 

261.6 

10.3 

296.6 

10.7 

243.0 

9.7 

373.6 

13.2 

313.2 

13.8 

364.8 

13.9 

326.8 

13.2 

452.3 

13.2 

407.0 

8.1 

586.4 

8.3 

637.6 

8.7 

379.6 

9.5 

369.5 

8.1 

Annual 

rainfall, mm 

Annual 

temperature, 

°C 

1430.2 9.3 

1617.6 9.7* 

1596.6 11.1* 

1489.0 9.9 

1578.6 9.3 

The intensity of the nitrous oxide flux is shown in Figure 5.2c and was typically 

higher during the months of April–October when most of the agricultural activity 

occurs (including N fertilisation – both mineral and slurry application and silage 

harvesting). In the winter months, the N 2 O flux intensities are usually much lower, 

which is related to the lack of fertiliser application as well as frequently saturated soil 

moisture conditions at the site. The emission peaks occur often as a result of coinciding 

rainfall and fertiliser application. 

43


Figure 5.3 N 2 O flux intensities from grassland and forest sectors on a monthly scale. The gap 

period was due to the failed data logger. 

The averaged monthly values of instantaneous flux are presented in Figure 5.3. It is 

noticeable that during the observation period, the flux intensities decrease for both 

forest and grassland sectors, while the magnitude of decrease is higher for the forest 

sector. This is in accordance with the history of fertiliser application which decreased 

in the grassland sector over the study period from 320 to 175 kg N ha −1 (Table 5.3). A 

significant emission burst from the forest sector was registered in March 2005. This 

event was associated with soil preparation work done in the forest sector that included 

ploughing and digging of furrows and the application of N fertilisers. During 2004, the 

N 2 O emissions from the forest sector (while still a grassland) were typically of the 

same intensity as the rest of the grassland footprint. The same is true for the 2005 (with 

the exception of the high March value). For 2007–2008 the grassland emissions far 

exceed the forest emissions, except for a few winter months. These values were further 

aggregated into the annual intensities presented in Table 5.3. 

44


Table 5.3 Annual characteristic of the N 2 O flux (Annual flux for 2006, likely to be 

underestimated due to poor data quality, cf. percentage of gaps). 

Year 

Mean annual flux intensities 

by sector, µg N 2O–N m −2 s −1 

Forest 

Grassland 

Annual total 

footprint 

flux, 

kg N 2O–N 

ha −1 

Forest sector 

direction, % 

of time 

Gaps, % of 

time 

Total N 

applied in 

grassland, 

kg N ha −1 

2004 0.0255 0.0191 4.4 10.8 30.5 316.2 

2005 0.0243 0.0137 2.8 9.7 42.4 243.2 

2006 0.0113 0.0120 0.7* 2.9 82.2 251.2 

2007 0.0134 0.0189 3.1 8.0 46.2 269.1 

2008 0.0091 0.0123 2.1 9.3 44.8 175.0 

The downward trend in flux intensities from 2004 to 2008 in both sectors is 

evident; however, the rate of this decrease is different. The drop in emissions in the 

grassland sector is due to the lower fertilisation rate and amounts only to a 20% 

emission reduction, whereas the forest sector emissions were reduced by a factor of 

three and can be attributed to the cessation of the fertilisers applications and the 

introduction of the new forest ecosystem. Also notable are the values of 2005, with the 

forest flux being 80% more intensive than the grassland flux. The comparison of 2004 

values in Table 5.3 indicates that the forest sector corresponded to farm fields used 

more intensively than the rest of the footprint fields. Thus, the decrease in flux 

intensities from 2006 onwards is even more significant. 

The cumulative annual fluxes for the whole ecosystem as well as contributions of 

forest and grassland sectors are presented in Table 5.3. The occurrences of the flux 

originating from the forest sector is slightly less frequent than its physical dimensions 

(36 degrees corresponding to 10%), although the forest sector corresponds 

approximately to the direction of the prevailing south westerly winds. The total flux 

values for 2006 and 2007 are for the first five and last nine months, respectively. The 

range of grassland ecosystem fluxes is similar to previously reported values for a 

45


grassland ecosystem in Ireland (Hyde et al. 2006). The frequency of times listed as 

percentages in Table 5.3, however, characterises only periods with atmospheric 

conditions suitable for eddy covariance technique; considering this the presented 

values seem reasonable. The seemingly high percentage of gaps across the years is a 

result of a heterogeneous distribution of gaps within any given period. For example, 

rainfall events usually result in gaps due to interference with the sonic anemometer; the 

same is true for TGA outages. The percentage of gaps associated only with 

atmospheric stability was typically under 20%. 


5.5.1. Environmental parameters 

The soil temperature had the most stable pattern of all measured parameters across 

the study period (Figure 5.2b). Despite the missing portion of the time series it is 

evident that the soil temperature pattern remained very stable throughout the 5 years 

without any noticeable trend or inter-annual variation. Seasonal variations are reliably 

predictable as well. Typically for this region the warmest months were July and 

August, with January and February being the coldest. The Pearson’s coefficients of 

correlation between the forest and grassland fluxes and the soil temperature were 0.766 

and 0.907 respectively, (p-values were < 0.001 for both cases), for the period of 

February–December 2008. January 2008 was excluded due to the outlying forest flux 

value. In the year 2008, due to the increased summer rainfall and soil moisture, soil 

temperature was a “limiting” parameter. The lower correlation value for the forest 

could be explained by the fact that the soil temperature sensor was in the grassland 

area, and furthermore due to the higher complexity of the forest ecosystem having 

more influencing parameters. No similar correlation was observed for other years. 

46


The distribution of rainfall events is known to affect the N 2 O emission (Dobbie and 

Smith 2003b) and such phenomenon has also been observed in this study (Figure 5.2). 

The time scale of these relationships is shorter than that of the soil temperature: the 

effect of rainfall on the intensive flux bursts can be easily seen on the daily scale. Some 

N 2 O emission burst events, however, were not associated with the rainfall or changes 

in WFPS, which suggest another force responsible for this phenomenon, such as 

fertiliser application. 

5.5.2. Management 

The timing and amount of the application of fertiliser is one of the most important 

regulating factors of the nitrous oxide emission (Dobbie and Smith 2003a). While the 

influence of the management practice might be reflected in the daily time series of N 2 O 

flux, the information about the land management unfortunately lacked such precision. 

We, therefore, centre the following discussion on the monthly time series. 

The first year of observation, 2004, was assumed to have little difference between 

forest and grassland sectors, because no major difference existed in either management 

practices or the ecosystem characteristics at the time. This assumption was generally 

confirmed, even though in a few cases discrepancies between the two sectors were 

noted. 

The extremely high flux intensity in March 2005 (Figures 5.2c and 5.3) coincides 

with the soil preparatory works in the forest sector. It is likely to be a result of 

mechanical disturbance of the soil as well as increased oxidation caused by newly dug 

drains. The latter (by permanently keeping the water table below about 0.5m) might 

also affect long-term flux intensities, which could be seen throughout 2005, with the 

47


forest sector having similar intensities to the regularly fertilised adjacent grassland 

sector. After 2005 the grassland emission was typically more intensive than the forest, 

with the exception of winter 2007–2008. This second event might be related to 

herbicide spraying in the forest sector carried out in October 2007. The general 

downward trend of intensities of nitrous oxide emission from the grassland sector 

followed the corresponding trend in reduction in fertilisers’ applications (Table 5.3). 

Aside from the two aforementioned periods associated with forest management 

activity, the intensity of the forest flux was lower than that of the grassland sector by 

25% by the end of third year after the establishment of the forest (2008) (Table 5.3). 

Although this might be expected, the phenomenon was noticeable during the third year 

of observation (2006) despite the soil being not N deficient (C:N ratio 13). This suggest 

that most of the N 2 O emission originates immediately from the applied fertilisers and 

animal slurry, while stored organic N plays a minor role, most likely due to chemically 

immobilised forms that constituted this organic N storage. 

The sensitivity of a newly afforested ecosystem as well as the drop in flux 

intensities across the study area suggest that continued studies over longer time span 

are needed to establish how fast the levelling of the emissions occurs. Data of Ball et 

al. (2002) suggest that 7–8 year long observations might be sufficient to fully trace this 

phenomenon. Monitoring of environmental parameters along with periodic analysis of 

soil nitrogen and carbon contents would provide deeper insight into the changes 

occurring in an ecosystem transitioning from agricultural use to the forestry. 

48



The authors would like to acknowledge support of the Department of Agriculture, 

Fisheries and Food of Irish Government (DAAF) under the Research Stimulus Fund 

Programme (RSF 06 372) and Environmental Protection Agency of Ireland under the 

CelticFlux Programme (2001-CC/CD-(5/7)). Nelius Foley, Killian Murphy and Adrian 

Birkby provided essential instrumental and data collection support for this research. 

We are also grateful to Dr. Paul Leahy for the discussion on the earlier versions of the 

manuscript and his contribution to the data collection over the years. 

49

Gap-filling techniques for the annual sums of nitrous oxide 

6. GAP-FILLING TECHNIQUES FOR THE ANNUAL SUMS OF 

NITROUS OXIDE 

Mishurov Mikhail and Gerard Kiely 



6.1. Abstract 

The full accounting of the greenhouse gas emissions is only possible when all 

components are measured throughout the observation period. While some field 

techniques such as eddy covariance or automatic closed chamber measurements have 

attempted near-continuous observations, however, it is often beyond the control of 

experimentalist to ensure such conditions. Therefore, the final time series of fluxes will 

inevitably have periods without reliable values (gaps) and will need to be gap-filled. 

The literature on the gap-filling methods for nitrous oxide is non-existent. We 

investigate three approaches for gap-filling nitrous oxide time series: linear 

extrapolation, moving average and look-up tables. As a time series we used two yearlong 

observations of nitrous oxide emissions from an intensively used grassland site in 

South-Western Ireland. There were about 54% gaps in both years, but each year had 

different distribution of gaps. Look-up table technique produced consistently good 

results even in case of long gaps in the time series. In the absence of very long gaps, 

more trivial extrapolation technique performed equally well. It is essential that this 

work will be extended with more complex methods, and that methods in this paper be 

further evaluated under different environmental conditions. 

50


Keywords: nitrous oxide, gap-filling, eddy-covariance, grassland 


Nitrous oxide (N 2 O) is a powerful greenhouse gas (GHG) having, according to the 

IPCC 4 th assessment report (2007), a warming potential of 298 (relative to CO 2 ) in a 

100-year time horizon. Additionally, N 2 O as a highly reactive gas is involved in the 

reduction of stratospheric concentration of ozone (Crutzen 1970). These characteristics 

imply that the robust and precise accounting of the nitrous oxide emission is required 

in order to assess its contribution to the GHG balance. Since no single method provides 

uninterrupted continuous measurement of the N 2 O soil-atmosphere exchange for any 

prolonged period of time, the monthly and annual sums are estimated from the 

available observations. The specific estimation technique depends on the particular 

GHG species, the amount and distribution of available observations as well as the 

experimental technique’s own performance. 

Unfortunately, there are not many gap-filling techniques for N 2 O fluxes in the 

literature. Leahy et al. (2004) used the modified moving daily average method to 

calculate the annual sum. The instantaneous fluxes (30-min averages) were aggregated 

into a daily values by linear extrapolation or in case of less than 12 values available 

(out of possible 48) were gap-filled with the moving average algorithm using the 5-day 

window. Such approach, while appearing reasonable, does not seem to be justified and 

the authors did not provide any support for the selected limits. Little explanation was 

provided in the earlier paper by Scanlon and Kiely (2003) that used a similar approach, 

with the linear interpolation instead of the moving average. 

51


There is, however, an available body of work developed for the purpose of gapfilling 

time series of the carbon dioxide fluxes (Falge et al. 2001; Reichstein et al. 

2005; Moffat et al. 2007). Due to natural differences between carbon dioxide and 

nitrous oxide, it would not be possible to directly apply all of the CO 2 methods to the 

N 2 O. For example, the lack of diurnal variation in the N 2 O flux prevents direct re-use 

of the mean diurnal interpolation method (Falge et al. 2001). Some of the approaches, 

however, can be transferred onto the N 2 O data set with modifications. For example, the 

look-up table technique (Reichstein et al. 2005) could be applied for nitrous oxide gapfilling 

given appropriate parameters. 

The existing methods can be classified as sequential—based on the location of the 

gap within the time series (e.g., mean diurnal); based on environmental-variables 

(look-up tables); governed by the environmental processes (non-linear regressions, 

biosphere models); black box (artificial networks). Of these groups, the most 

straightforward translation to the nitrous oxide application can be made only for the 

environmental-variables based approach. 

The aim of this paper was to investigate possible gap-filling approaches for the 

purpose of calculating the annual emission of the nitrous oxide. We limited this study 

to approaches that are straightforward to implement and required only the most-widely 

employed environmental characteristics. 

6.3. Methods 

6.3.1. Site description and flux measurements 



52


elevation is 187 m (above sea level). Most of the site is poorly drained with a small 

area prone to winter water logging. The soil is a gley brown earth with an annual water 

table ranging from approximately 0.6m to 2.5m below the surface. The topsoil is rich 

in organic matter to a depth of about 15 cm (about 12% organic matter), overlying a 

dark brown B horizon of sand texture (Scanlon and Kiely 2003). Within the 0–10 cm 

layer the soil bulk density was 1.02 g cm −3 , pH was 6.7, total soil organic carbon 

(SOC) was 4.5%, and total soil nitrogen was 0.35% (C:N ratio of 13). Averaged over 

the top 10 cm, the soil porosity was 0.60, the saturation moisture level was 0.57, the 

field capacity was 0.32, and the wilting point was 0.12 m 3 m −3 . The typical land use in 


for silage and hay. 

The eddy-covariance setup used to measure the N 2 O flux consists of a CSAT3 3-D 

sonic anemometer (Campbell Sci., USA) and a trace-gas analyser (TGA 100A, 

Campbell Sci., USA). Both the anemometer and the N 2 O intake were mounted at 6 m 

height, and collected data at a frequency of 10 Hz. The ground instrumentation 

consisted of soil temperature probes at 5 cm, time-domain reflectometry (TDR) probes 

for soil moisture at the same depth and generic tipping-bucket rain gauges with 

resolution 0.1 mm. 

The collected 10 Hz data were processed into 30-minute average fluxes according 

to the following algorithm: shifting the concentration series by a constant period of 

time to account for the tube travel time (0.9 s); de-spiking (at three standard 

deviations), yaw and pitch rotations, de-trending of the concentration series, followed 

by the calculation of flux and adjacent statistics. This followed by the post-processing 

filtering based on a stationarity test (Foken and Wichura 1996). 

53


At the end of this process out of 17520 and 17568 flux values for 2007 and 2008, 

respectively, there was 9482 (54.1%) and 9493 (54.0%) gaps. 

6.3.2. Gap distribution 

While the number and percentage of gaps were comparable in 2007 and 2008, the 

distributions of the gaps were slightly different (Figure 6.1). Due to the data logger 

failure in the early 2007, three months of data were lost which resulted in the lower 

count of short gaps. The existing long gaps were not suitable for sequential gap-filling, 

i.e. type of gap-filling that is based on the position of the gap in the time series, rather 

then environmental factors. It might be expected, therefore, that the sequential method 

might underestimate the annual flux in such cases. 

Figure 6.1 Distribution of the gap periods in the annual time series (note the logarithmic scale 

of both axes) 

6.3.3. Gap-filling techniques 

Three fundamentally different gap-filling approaches were investigated: linear 

extrapolation, moving average and look-up table. 

54


6.3.3.1. Linear extrapolation 

Linear extrapolation is routinely used in the calculation of the annual fluxes based 

on infrequent chamber measurements. The higher frequency of eddy-covariance 

measurements allows for extrapolating of instantaneous fluxes to different temporal 

scales with higher reliability than in case of calculations based on a typical closedchambers 

measurements. The essence of this method can be expressed as follows: 

n 

N 

F = ∑ f i 

(6.1) 

n 

i 

where F is the gap-filled flux for the certain period (e.g., a day), n is a total number 

of periods (e.g., 30 minutes periods) with the good flux, N is a total number of 

averaging periods in a gap-filled value; f i are the values of instantaneous flux for the 

good periods. Three temporal scales were used to evaluate the performance of gapfilling: 

daily, monthly and annual. 

6.3.3.2. Moving average 

The moving average technique was used by Leahy et al. (2004). Similar to the 

linear extrapolation (at daily and monthly scales) this technique might not gap-fill 

some of the longer gaps (those that are longer than arbitrarily an declared window 

length). This technique was included in the study as the only EC-specific gap-filling 

method approved in the peer-review process. 

6.3.3.3. Look-up table 

The look-up table (LUT) is a predictive technique that assigns flux in the gap 

period according to the environmental and management conditions prevailing at the 

time of the gap. This approach consists of creating a table with the flux values binned 

55


based on the corresponding values of the external parameters (e.g., soil temperature, 

rainfall, etc). The determination of the relevant parameters and their critical values is a 

crucial step if this technique is to be successful. In this study the following parameters 

were used to create the table: 

• cumulative rainfall in the preceding 12 hours with three categories: no rainfall, 

0–2 mm and 2 mm or more; 

• soil temperature at 5 cm with four categories, separated by the values of three 

quartiles; 

• a binary flag for fertilizer application in the preceding 5 days. 

In this way, 24 groups could be arranged from the whole data set. For each group 

of flux values the mean of existing values was calculated and assigned to the gap 

periods that had the same conditions. Due to the long gap at the beginning of 2007, the 

two-year time series were used to create the LUT. Therefore, the total number of 30- 

min periods was 35088 and the number of gap periods was 18975. The choice of the 

particular parameters was based on our earlier work (see Chapter 4). 

At the end of the exercise the values in gap-filled time series were summed 

individually for each year to estimate the total annual flux. 

6.4. Results 

The results of the gap-filling are presented in Table 6.1. As was mentioned in 

Methods section, some of the techniques cannot produce a time series that is fully gapfilled, 

in these cases percentage of remaining gaps were added. The range of annual 

56


sums is rather high, but when the percentage of the remaining gaps is taken into 

account, it is evident that the annual sums are dependant on the amount of gaps in the 

final time series. When values corresponding to the similarly gap-filled time series 

considered, performance of different methods does not differ much. On the other hand, 

value obtained with the annual extrapolation in 2007 does not seem completely 

realistic since it is exceeds the next value by almost a quarter. 

Table 6.1 The annual sums obtained with different gap-filling techniques 

Method 

Annual sums (kg N ha −1 ) Remaining gaps, % 

2007 2008 2007 2008 

Not gap-filled 2.74 1.79 54.1 54.0 

Daily 4.62 2.95 29.3 21.6 

Extrapolation Monthly 4.67 3.33 24.7 - 

Annual 5.97 3.89 - - 

Moving average 4.48 3.04 26.3 18.3 

Look-up table 4.81 4.01 - - 

Various approaches could be chosen to evaluate the performance of selected 

methods. The consensus estimate is a convenient measure when the method precision 

is higher than the required accuracy. Such estimate could be approximated at about 4.5 

and 3.5 kg N ha −1 , respectively for 2007 and 2008. This approximation suffers, 

however, from the inclusion of the values with the remaining gaps; because of these 

gaps, it is likely that the true values are slightly higher. 

In terms of complexity of programmatical implementations and the run time, 

methods ranged from trivial in case of extrapolation to average complexity in case of 

look-up table, typically calculations took only a few seconds to finish. Under more 

complex inputs or with larger data sets it is expected that complexity will grow, but 

performance will remain good. 

57



The presented approaches had similar outcomes. As was expected sequential 

methods still had some gaps in the final time series. The same was observed by Leahy 

et al. (2004), who observed two gap periods longer than 5 days in the final time series. 

In the case of moving-average technique the length and quantity of the remaining gaps 

is influenced by different averaging windows and methods based on extrapolation are 

likely to always have some gaps at least in the final daily time series. 

In 2007, the largest contribution to the remaining gaps was due the down-time in 

the beginning of year. Sequential techniques, effectively, failed to alleviate this issue; 

therefore, the obtained annual are considered an underestimation of the true annual 

flux. As the first three months of a year have typically little to no agricultural activity, 

the N 2 O flux is likely to be low; from our observation in other years, it is estimated that 

for January–March period total flux amounts to approximately 0.3 kg N 2 O–N ha −1 . 

It is notable that performance of the most trivial approach (annual extrapolation) is 

very close to the consensus value in the absence of very long gaps. It shows that when 

the required precision is about 0.1 kg N 2 O–N ha −1 this method might serve as good 

first estimate of the annual flux. 

The look-up table was the only approach that considered the environmental 

variables of the flux. This method is sensitive to the parameters selected as well as the 

separation limits or bin sizes. All the chosen parameters are well known as the main 

drivers of the N 2 O emission (Dobbie and Smith 2003b; Dobbie and Smith 2003a). Lag 

periods, 12-hour rainfall and 5-day fertiliser applications, were based on our previous 

work at this site and reflected precision of the available data. In this study the LUT 

58


technique was the only method that performed well with data sets of both years. While 

in 2008 the LUT annual sum was highest compared to other techniques, it did not 

deviate significantly from the consensus estimate and was indeed very close to another 

well performing method—the annual extrapolation. The two-year data set that was 

used to build the table was necessary because of the long gap in 2007, it is, however, 

expected that given even distribution of gaps throughout the year, a one-year time 

series would suffice. 

6.6. Conclusions 

In this study, different approaches were used to gap-fill the time series of nitrous 

oxide. We considered traditional sequential and environmental variable-based 

approaches. Some of the sequential methods do not eliminate gaps from the time series 

completely, as it was expected. The best performing technique was the look-up table 

(LUT); although, the simpler annual extrapolation method was comparably good in the 

absence of the very long gaps. The continuation of this study and the use of these 

methods under different conditions are necessary to fully understand and explore 

agricultural emissions of nitrous oxide. 


The authors would like to acknowledge support of the Department of Agriculture, 

Fisheries and Food of Irish Government (DAAF) under the Research Stimulus Fund 

Programme (RSF 06 372) and Environmental Protection Agency of Ireland under the 

CelticFlux Programme (2001-CC/CD-(5/7)). Nelius Foley and Killian Murphy 

provided essential instrumental and data collection support for this research. 

59

General discussion 

7. GENERAL DISCUSSION 

The aim of this work was to investigate the behaviour and temporal trends in the 

nitrous oxide emissions from a managed grassland in the temperate humid climate of 

South-Western Ireland. 

The first sub-project was devoted to post-hoc analysis of the time series, 

attempting to separate the peak and background fluxes. A novel technique was 

proposed that allows distinguishing between normally distributed background flux and 

more intensive bursts. In our study, about a quarter of all flux was delivered by 

emission bursts, occurring over only 7% of the flux period. Applying this technique to 

the earlier published data, we found good agreement between our estimate and that of 

Hsieh et al. (2005), which was based on DnDc modelling of the background flux. We 

also observed that the peak events are more likely to occur within 12–40 and 50–100 

hours after the rainfall and fertiliser application, respectively. This information was 

further used to develop an environmental-variable based approach for time series gapfilling. 

The long-term observations of nitrous oxide flux dynamics in the newly afforested 

grassland comprised the second sub-project of this study. We observed a reduction in 

emissions from the afforested sector over several years as compared to the grassland. It 

was, however, preceded by the very intensive burst, coinciding with the soil 

preparatory work at the afforested site. These observations lasted for 5 years and will 

need to be continued to assess changes in emission as the forest matures. 

The final sub-project was the methodological study on gap-filling of the N 2 O time 

series for the purpose of calculating annual sums. We studied both published and new 

60

General discussion 

methods of gap-filling on a two-year data set. Generally, the annual emissions 

calculated by different approaches were similar. However, in the presence of the very 

long gaps, the performance of most of the techniques studied is likely to be 

unsatisfactory. The look-up table technique produced satisfactory results and was based 

on the environmental parameters (soil temperature, rainfall, fertiliser application), 

whose influence was quantified in the first sub-project. It was, however, necessary to 

use a two-year data set to build the table due to the existence of a long gap in one of the 

years. 

In this research we focused on the analysis of the eddy-covariance time series of 

nitrous oxide flux from intensive grassland and young forestry. It was established that 

the most important conditions affecting the flux at the site were: 

• preceding rainfall (between 12 and 40 hours prior to a given averaging period); 

• recent fertilisers application (between 2 and 4 days); 

• mechanical disturbances of the soil; 

• grazing. 

Our work on gap-filling of nitrous oxide time series proposed an easy-toimplement 

technique, and presented the evaluation of already known methods. We 

hope that the contribution made towards the improved understanding of the nature of 

nitrous oxide emission from the agricultural ecosystems, and tools developed will 

enable further research and better accounting of flux of this significant greenhouse gas. 

61

Recommendations for further research 

8. RECOMMENDATIONS FOR FURTHER RESEARCH 

The result of the present work identifies additional questions that could be 

recommended for further investigation. 

• Method used for distinguishing peak and background flux need to be tested 

under various conditions and different ecosystems. The analysis carried out on 

the basis of the peak-background flux separation might indicate different 

criteria affecting the burst emission or time lags characteristic for these 

triggers. 

• The long-term study is necessary to be continued as some research indicates an 

increase of the N 2 O emissions as the forest matures. Such scenario seems 

particularly likely due to the presence of alder which was reported to produce 

high emission in the mature forests. Essential part of such studies needs to be 

continuous monitoring of the amounts of soil nitrogen and environmental 

conditions in the afforested area. 

• Our work on the subject of gap-filling was only a beginning of the 

methodological research in this area. A more extensive study examining varied 

approaches under different environmental conditions and scales is needed. 

While it might be unrealistic to try to gap-fill the instantaneous flux time series, 

gap-filling of the daily and monthly time series should be very valuable for 

understanding the short-term and management driven patterns of emission. 

• When focusing on the nitrogen cycle of the wet grassland, it is also important 

to analyse nitrate leaching and ammonia volatilisation as possible exit routes of 

the labile nitrogen. Observation of the water regime at the site suggest that 

62

Recommendations for further research 

analysing methane (CH 4 ) production and emission might be beneficial to both 

better accounting of the greenhouse gas balance and the understanding of the 

N 2 O flux. 

To further explore close occurrences of intensive uptake and emission periods, as 

well as noted gaps in peak events (Chapter 4), a number of approaches can be used to 

investigate possible causes: 

• Co-spectra analysis of the identified peak periods, with the emphasis on the 

uptake peaks. This would allow for analysis of advective effects that are 

present in flux time series originating from heterogeneous footprint area as the 

one studied in this project. 

• Studying clustering properties of the flux and associated time series with 

telegraph approximation allows for magnitude-independent analysis of 

turbulent time series. The different types of signal will have generically 

different types of probability density function of the interpulse distance of the 

telegraph approximation signals. 

• Using wavelet thresholding method to identify groups of periods with high and 

low contributions towards the annual N 2 O balance. It was shown by Katul and 

Vidakovic (1996) that the Lorentz thresholding function is particularly suitable 

for such application. 

63

References 

REFERENCES 

Ball, B. C., I. P. McTaggart and C. A. Watson (2002). "Influence of organic ley-arable 

management and afforestation in sandy loam to clay loam soils on fluxes of 

N2O and CH4 in Scotland." Agriculture, Ecosystems & Environment 90(3): 

305-317. 

Barton, L., R. Kiese, D. Gatter, K. Butterbach-Bahl, R. Buck, C. Hinz and D. V. 

Murphy (2008). "Nitrous oxide emissions from a cropped soil in a semi-arid 

climate." Global Change Biology 14(1): 177-192. 

Bateman, E. J. and E. M. Baggs (2005). "Contributions of nitrification and 

denitrification to N 2 O emissions from soils at different water-filled pore space." 

Biology and Fertility of Soils 41(6): 379-388. 

Bouwman, A. F. (1996). "Direct emission of nitrous oxide from agricultural soils." 

Nutrient Cycling in Agroecosystems 46(1): 53-70. 

Calanca, P., N. Vuichard, C. Campbell, N. Viovy, A. Cozic, J. Fuhrer and J.-F. 

Soussana (2007). "Simulating the fluxes of CO 2 and N 2 O in European 

grasslands with the Pasture Simulation Model (PaSim)." Agriculture, 

Ecosystems & Environment 121: 164-174. 

Clayton, H., I. P. McTaggart, J. Parker, L. Swan and K. A. Smith (1997). "Nitrous 

oxide emissions from fertilised grassland: A 2-year study of the effects of N 

fertiliser form and environmental conditions." Biology and Fertility of Soils 

25(3): 252-260. 

Crutzen, P. J. (1970). "The influence of nitrogen oxides on the atmospheric ozone 

content." Quarterly Journal of the Royal Meteorological Society 96(408): 320- 

325. 

64

References 

Dobbie, K. E., I. P. McTaggart and K. A. Smith (1999). "Nitrous oxide emissions from 

intensive agricultural systems: Variations between crops and seasons, key 

driving variables, and mean emission factors." Journal of Geophysical Research 

104(D21): 26891-26899. 

Dobbie, K. E. and K. A. Smith (2003a). "Impact of different forms of N fertilizer on 

N 2 O emissions from intensive grassland." Nutrient Cycling in Agroecosystems 

67(1): 37-46. 

Dobbie, K. E. and K. A. Smith (2003b). "Nitrous oxide emission factors for 

agricultural soils in Great Britain: the impact of soil water-filled pore space and 

other controlling variables." Global Change Biology 9(2): 204-218. 

Eaton, J., N. McGoff, K. Byrne, P. Leahy and G. Kiely (2008). "Land cover change 

and soil organic carbon stocks in the Republic of Ireland 1851–2000." Climatic 

Change 91(3): 317-334. 

Edwards, G. C., G. W. Thurtell, G. E. Kidd, G. M. Dias and C. Wagner-Riddle (2003). 

"A diode laser based gas monitor suitable for measurement of trace gas 

exchange using micrometeorological techniques." Agricultural and Forest 

Meteorology 115(1-2): 71-89. 

Falge, E., D. Baldocchi, R. Olson, P. Anthoni, M. Aubinet, C. Bernhofer, G. Burba, R. 

Ceulemans, R. Clement, H. Dolman, A. Granier, P. Gross, T. Grьnwald, D. 

Hollinger, N.-O. Jensen, G. Katul, P. Keronen, A. Kowalski, C. T. Lai, B. E. 

Law, T. Meyers, J. Moncrieff, E. Moors, J. W. Munger, K. Pilegaard, Ь. 

Rannik, C. Rebmann, A. Suyker, J. Tenhunen, K. Tu, S. Verma, T. Vesala, K. 

Wilson and S. Wofsy (2001). "Gap filling strategies for defensible annual sums 

of net ecosystem exchange." Agricultural and Forest Meteorology 107(1): 43- 

69. 

65

References 

Flechard, C. R., P. Ambus, U. Skiba, R. M. Rees, A. Hensen, A. van Amstel, A. v. d. 

P.-v. Dasselaar, J. F. Soussana, M. Jones, J. Clifton-Brown, A. Raschi, L. 

Horvath, A. Neftel, M. Jocher, C. Ammann, J. Leifeld, J. Fuhrer, P. Calanca, E. 

Thalman, K. Pilegaard, C. Di Marco, C. Campbell, E. Nemitz, K. J. 

Hargreaves, P. E. Levy, B. C. Ball, S. K. Jones, W. C. M. van de Bulk, T. 

Groot, M. Blom, R. Domingues, G. Kasper, V. Allard, E. Ceschia, P. Cellier, P. 

Laville, C. Henault, F. Bizouard, M. Abdalla, M. Williams, S. Baronti, F. 

Berretti and B. Grosz (2007). "Effects of climate and management intensity on 

nitrous oxide emissions in grassland systems across Europe." Agriculture, 

Ecosystems & Environment 121(1-2): 135-152. 

Flechard, C. R., A. Neftel, M. Jocher, C. Ammann and J. Fuhrer (2005). "Bi-directional 

soil/atmosphere N 2 O exchange over two mown grassland systems with 

contrasting management practices." Global Change Biology 11: 2114-2127. 

FLUXNET. (2009). "Methodology." from 

http://www.fluxnet.ornl.gov/fluxnet/methodology.cfm. 

Foken, T. and B. Wichura (1996). "Tools for quality assessment of surface-based flux 

measurements." Agricultural and Forest Meteorology 78(1-2): 83-105. 

Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D. W. Fahey, J. 

Haywood, J. Lean, D. C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. 

Schulz and R. Van Dorland (2007). Changes in Atmospheric Constituents and 

in Radiative Forcing. Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. 

Manninget al. Cambridge, United Kingdom and New York, NY, USA. 

66

References 

Fowler, D., K. J. Hargreaves, U. Skiba, R. Milne, M. S. Zahniser, J. B. Moncrieff, I. J. 

Beverland, M. W. Gallagher, P. Ineson, J. Garland and C. Johnson (1995). 

"Measurements of CH 4 and N 2 O Fluxes at the Landscape Scale Using 

Micrometeorological Methods." Philosophical Transactions: Physical Sciences 

and Engineering 351(1696): 339-356. 

Gartzia-Bengoetxea, N., A. González-Arias, A. Merino and I. Martínez de Arano 

(2009). "Soil organic matter in soil physical fractions in adjacent semi-natural 

and cultivated stands in temperate Atlantic forests." Soil Biology and 

Biochemistry 41(8): 1674-1683. 

Goldberg, S. D. and G. Gebauer (2009). "N 2 O and NO fluxes between a Norway 

spruce forest soil and atmosphere as affected by prolonged summer drought." 

Soil Biology and Biochemistry 41(9): 1986-1995. 

Hawkins, D. M. (1980). Identification of Outliers. London, Chapman and Hall. 

Hsieh, C.-I., G. Katul and T.-w. Chi (2000). "An approximate analytical model for 

footprint estimation of scalar fluxes in thermally stratified atmospheric flows." 

Advances in Water Resources 23(7): 765-772. 

Hsieh, C.-I., P. Leahy, G. Kiely and C. Li (2005). "The effect of future climate 

perturbations on N 2 O emissions from a fertilized humid grassland." Nutrient 

Cycling in Agroecosystems 73: 15-23. 

Huttunen, J. T., H. Nykänen, P. J. Martikainen and M. Nieminen (2003). "Fluxes of 

nitrous oxide and methane from drained peatlands following forest clear-felling 

in southern Finland." Plant and Soil 255(2): 457-462. 

Hyde, B. P., M. J. Hawkins, A. F. Fanning, D. Noonan, M. Ryan, P. O’ Toole and O. 

T. Carton (2006). "Nitrous oxide emissions from a fertilized and grazed 

67

References 

grassland in the South East of Ireland." Nutrient Cycling in Agroecosystems 

75(1): 187-200. 

Inagaki, Y., S. Miura and A. Kohzu (2004). "Effects of forest type and stand age on 

litterfall quality and soil N dynamics in Shikoku district, southern Japan." 

Forest Ecology and Management 202(1-3): 107-117. 

Jones, S. K., R. M. Rees, U. M. Skiba and B. C. Ball (2007). "Influence of organic and 

mineral N fertiliser on N 2 O fluxes from a temperate grassland." Agriculture, 

Ecosystems & Environment 121: 74-83. 

Jørgensen, R. N., B. J. Jørgensen and N. E. Nielsen (1998). "N 2 O emission 

immediately after rainfall in a dry stubble field." Soil Biology and Biochemistry 

30(4): 545-546. 

Jungkunst, H. F., H. Flessa, C. Scherber and S. Fiedler (2008). "Groundwater level 

controls CO 2 , N 2 O and CH 4 fluxes of three different hydromorphic soil types of 

a temperate forest ecosystem." Soil Biology and Biochemistry 40(8): 2047- 

2054. 

Kaimal, J. C. and J. J. Finnigan (1994). Atmospheric boundary layer flows: their 

structure and measurments. New York, Oxford University Press. 

Kiese, R. and K. Butterbach-Bahl (2002). "N 2 O and CO 2 emissions from three different 

tropical forest sites in the wet tropics of Queensland, Australia." Soil Biology 

and Biochemistry 34(7): 975-987. 

Leahy, P., G. Kiely and T. M. Scanlon (2004). "Managed grassland: A greenhouse gas 

sink or source?" Geophysical Research Letters 31: L20507. 

Lee, X., W. Massman and B. E. Law, Eds. (2004). Handbook of Micrometeorology: a 

guide for surface flux measurement and analysis. Atmospheric and 

Oceanographic Sciences Library, Kluwer Academic Publishers. 

68

References 

Macdonald, C. A., N. Thomas, L. Robinson, K. R. Tate, D. J. Ross, J. Dando and B. K. 

Singh (2009). "Physiological, biochemical and molecular responses of the soil 

microbial community after afforestation of pastures with Pinus radiata." Soil 

Biology and Biochemistry 41(8): 1642-1651. 

Maljanen, M., M. Martikkala, H. T. Koponen, P. Virkajärvi and P. J. Martikainen 

(2007). "Fluxes of nitrous oxide and nitric oxide from experimental excreta 

patches in boreal agricultural soil." Soil Biology and Biochemistry 39(4): 914- 

920. 

Matson, A., D. Pennock and A. Bedard-Haughn (2009). "Methane and nitrous oxide 

emissions from mature forest stands in the boreal forest, Saskatchewan, 

Canada." Forest Ecology and Management 258(7): 1073-1083. 

Menyailo, O. V. and B. Huwe (1999). "Activity of denitrification and dynamics of N 2 O 

release in soils under six tree species and grassland in central Siberia." Journal 

of Plant Nutrition and Soil Science 162(5): 533-538. 

Moffat, A. M., D. Papale, M. Reichstein, D. Y. Hollinger, A. D. Richardson, A. G. 

Barr, C. Beckstein, B. H. Braswell, G. Churkina, A. R. Desai, E. Falge, J. H. 

Gove, M. Heimann, D. Hui, A. J. Jarvis, J. Kattge, A. Noormets and V. J. 

Stauch (2007). "Comprehensive comparison of gap-filling techniques for eddy 

covariance net carbon fluxes." Agricultural and Forest Meteorology 147(3-4): 

209-232. 

Müller, C., M. Martin, R. J. Stevens, R. J. Laughlin, C. Kammann, J. C. G. Ottow and 

H. J. Jäger (2002). "Processes leading to N 2 O emissions in grassland soil during 

freezing and thawing." Soil Biology and Biochemistry 34(9): 1325-1331. 

69

References 

Müller, C., R. J. Stevens, R. J. Laughlin and H. J. Jäger (2004). "Microbial processes 

and the site of N 2 O production in a temperate grassland soil." Soil Biology and 

Biochemistry 36(3): 453-461. 

Neftel, A., C. R. Flechard, C. Ammann, F. Conen, L. Emmenegger and K. Zeyer 

(2007). "Experimental assessment of N 2 O background fluxes in grassland 

systems." Tellus B 59(3): 470-482. 

Ramaswamy, V., O. Boucher, J. Haigh, D. Hauglustaine, G. Myhre, T. Nakajima, G. 

Y. Shi and S. Solomon (2001). Radiative Forcing of Climate Change. Climate 

Change 2001: The Scientific Basic. Contribution of the Working Group I to the 

Third Assesement Report of the Intergovernmental Panel on Climate Change. J. 

T. Houghton, Y. Ding, D. J. Griggset al. Cambridge, United Kingdom and New 

York, USA: 881. 

Reichstein, M., E. Falge, D. Baldocchi, D. Papale, M. Aubinet, P. Berbigier, C. 

Bernhofer, N. Buchmann, T. Gilmanov, A. Granier, T. Grьnwald, K. 

Havrбnkovб, H. Ilvesniemi, D. Janous, A. Knohl, T. Laurila, A. Lohila, D. 

Loustau, G. Matteucci, T. Meyers, F. Miglietta, J.-M. Ourcival, J. Pumpanen, S. 

Rambal, E. Rotenberg, M. Sanz, J. Tenhunen, G. Seufert, F. Vaccari, T. Vesala, 

D. Yakir and R. Valentini (2005). "On the separation of net ecosystem 

exchange into assimilation and ecosystem respiration: review and improved 

algorithm." Global Change Biology 11(9): 1424-1439. 

Scanlon, T. M. and G. Kiely (2003). "Ecosystem-scale measurements of nitrous oxide 

fluxes for an intensely grazed, fertilized grassland." Geophysical Research 

Letters 30(16): 1852. 

70

References 

Skiba, U. and K. A. Smith (2000). "The control of nitrous oxide emissions from 

agricultural and natural soils." Chemosphere - Global Change Science 2(3-4): 

379-386. 

Smith, K. A. (2005). "The impact of agriculture and other land uses on emissions of 

methane and nitrous and nitric oxides." Journal of Integrative Environmental 

Sciences 2(2): 101 - 108. 

Steudler, P. A., J. M. Melillo, R. D. Bowden, M. S. Castro and A. E. Lugo (1991). 

"The Effects of Natural and Human Disturbances on Soil Nitrogen Dynamics 

and Trace Gas Fluxes in a Puerto Rican Wet Forest." Biotropica 23(4): 356- 

363. 

Teagasc (2008). Major and micro nutrient advice for productive agricultural crops. B. 

S. Coulter and S. Lalor, Teagasc. 

Ullah, S., R. Frasier, L. King, N. Picotte-Anderson and T. R. Moore (2008). "Potential 

fluxes of N 2 O and CH 4 from soils of three forest types in Eastern Canada." Soil 


von Arnold, K., M. Nilsson, B. Hånell, P. Weslien and L. Klemedtsson (2005). "Fluxes 

of CO 2 , CH 4 and N 2 O from drained organic soils in deciduous forests." Soil 


Voronovich, V. and G. Kiely (2007). "On the gap in the spectra of surface-layer 

atmospheric turbulence." Boundary-Layer Meteorology 122(1): 67-83. 

Whitehead, D. C. (2000). Nutrient Elements in Grassland: soil-plant-animal 

relationship. Oxon, CABI Publishing. 

Wrage, N., G. L. Velthof, M. L. van Beusichem and O. Oenema (2001). "Role of 

nitrifier denitrification in the production of nitrous oxide." Soil Biology and 

Biochemistry 33(12-13): 1723-1732. 

71

References 

Yashiro, Y., W. R. Kadir, T. Okuda and H. Koizumi (2008). "The effects of logging on 

soil greenhouse gas (CO 2 , CH 4 , N 2 O) flux in a tropical rain forest, Peninsular 

Malaysia." Agricultural and Forest Meteorology 148(5): 799-806. 

Zerva, A. and M. Mencuccini (2005). "Short-term effects of clearfelling on soil CO 2 , 

CH 4 , and N 2 O fluxes in a Sitka spruce plantation." Soil Biology and 

Biochemistry 37(11): 2025-2036. 

72

PhD Thesis, 2010 - University College Cork

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