and ecosystem emissions
Performance of CH 4 and CO 2 eddy covariance measurements over a grazed pasture
Raphael Felber 2015 Diss. ETH No. 22831
BRIDGING THE GAP BETWEEN ANIMAL AND
ECOSYSTEM EMISSIONS: PERFORMANCE OF CH
4AND CO
2EDDY COVARIANCE MEASUREMENTS OVER A
GRAZED PASTURE
A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH
(Dr. sc. ETH Zurich)
presented by RAPHAEL FELBER
MSc ETH Environmental Sc., ETH Zurich
born on 20.03.1981 citizen of Egerkingen (SO)
accepted on the recommendation of Prof. Dr. Michael Kreuzer
Dr. Christof Ammann Prof. Dr. Nina Buchmann
2015
Greenhouse gas (GHG) emissions from agriculture are responsible for 14 % of total global GHG emissions and represent the largest contribution of anthropogenic non-CO2 emis- sions. Methane (CH4) from enteric fermentation by ruminants (cattle, goats, sheep, etc.) contribute the most to agricultural GHG emissions, followed by nitrous oxide (N2O) emis- sions from manure management and synthetic fertilizer use. While being a source for GHG emissions, agriculture also has the potential to mitigate global warming by weakening the increase of the atmospheric CO2 concentration through the sequestration of carbon in the soil. Grassland management by grazing is considered as one of the most cost-effective mitigation options. However, emission and mitigation estimates from the agricultural sector have been associated with large uncertainties of up to 150 %.
Most frequently, CH4 emissions from animals are derived from individual animal mea- surements during relatively short-term experiments (several days) and are not free of interference on the animal (e.g., the confinement of animals in chambers). By contrast, the exchange of CO2 and N2O of ecosystems is commonly measured by area-related meth- ods (soil chambers or micrometeorological flux approaches), whereby the eddy covariance (EC) method is commonly used for CO2 exchange measurements over ecosystems. Due to advances in laser technology for CH4 and N2O measurements, the application of EC mea- surements for these gases has also become feasible. The EC method offers non-intrusive measurements that integrate over a larger area (’footprint’), and provides advantages to investigate long-term in-situ exchange of GHGs from agricultural ecosystems. However, meaningful EC measurements are dependent on meteorological conditions (wind direction, stability of the atmosphere, etc.), which influence the footprint, as well as on spatial and temporal homogeneity. In grazing systems, EC measurements are especially challenging due to the uneven distribution of animals contributing to the flux.
This thesis investigates the applicability of EC measurements on pasture. It focuses on CH4 and CO2 fluxes because these gases on one hand are directly emitted by the animals and on the other hand occur as exchange fluxes from soil/vegetation processes. Data used in the thesis cover a measurement period of one year including a 209-day grazing season,
flux interpretation.
Cow position information allowed a reliable distinction between measurement intervals with and without cow contributions. CH4 fluxes measured when cows were present in the footprint were typically in the range of 100 to 1200 nmol m−2s−1, whereas CH4 fluxes without cow contributions were much smaller (around 4 nmol m−2s−1). However, cow contributions to CO2 fluxes (up to around 10 µmol m−2s−1) were in the same order of magnitude as CO2fluxes measured during situations without cows present in the footprint.
Thus the determination of cow contributions from measured fluxes was more difficult for CO2than for CH4. A common flux partitioning method was used to model CO2 emissions from soil/vegetation processes, and cow respiration was derived from the residuals of modelled soil/vegetation fluxes to measured fluxes for periods with cows in the footprint.
Using the monitored cow positions in combination with an analytical footprint model, cow fluxes were converted into average emissions per animal. Average animal CH4 emis- sions of 423±24 g CH4head−1d−1 and CO2 emissions of 4.6±1.6 kg C head−1d−1 for the grazing season were found. These values agree well with previously reported respiration chamber measurements of cows of similar performance and live weight. However, derived shorter-term (days to months) values showed considerable uncertainty that was attributed to the strong variability of the cows’ presence in the footprint. On annual time scales the applied setup and meteorological conditions averaged out this variability and reduced the uncertainty. Thus the performed EC measurements over the investigated pasture are re- garded as suitable for assessing longer-term ecosystem GHG budgets that are necessary to improve national inventories, but may not be appropriate to resolve small differences in animal CH4 emissions during short-term experiments, for example applied to deter- mine emission reductions from different feeding situations like supplementary feeding to pasture grass.
Using the CH4 and CO2 flux measurements in combination with other management re- lated carbon fluxes, the 1-year carbon budget for two different system boundaries (includ- ing and excluding cows) was determined. Both approaches led to similar results revealing no significant carbon storage change in the pasture system, partly due to the considerable uncertainty range. Considering the GHG budget (CH4 and N2O in CO2-equivalents), the CH4 emissions from the animals dominated the total emissions, but the carbon budget showed a much larger uncertainty, despite its in-situ determination. The carbon budget uncertainty could be reduced to some degree by better constraining the carbon fluxes related to animal intake and respiration, which can be achieved by the combination of long-term field observations with measurements on the individual animals.
Die Landwirtschaft ist für 14 % der globalen Emissionen von Treibhausgasen (THG) ver- antwortlich, und sie ist der grösste anthropogene Emittent an Methan (CH4) und Lachgas (N2O). CH4 aus der Fermentation im Magen von Wiederkäuern (Rindvieh, Ziegen, Schafe, etc.) macht den grössten Anteil an landwirtschaftlichen THG-Emissionen aus. An zweiter Stelle folgt N2O aus Gülle und aus biologischen Bodenprozessen nach Einsatz von Dün- gemitteln. Neben ihrem Beitrag als THG-Quelle hat die Landwirtschaft aber auch das Potenzial, den anthropogenen THG-Effekt zu mindern, indem Kohlenstoff in den Böden eingelagert und so der Anstieg der atmosphärischen CO2-Konzentration gebremst wird.
Graslandbewirtschaftung mit Weidehaltung wird als eine kosteneffektive Möglichkeit zur Milderung des Klimawandels angesehen. Dabei ist jedoch zu beachten, dass sowohl die Emissions- als auch die Milderungsabschätzungen zur Zeit noch mit sehr grossen Unsi- cherheiten behaftet sind.
Bisher folgen die Emissions- bzw. Austauschmessungen von den in der Landwirtschaft wichtigen THG unterschiedlichen Ansätzen: Experimente zur Quantifizierung von CH4- Emissionen von Tieren finden auf Einzeltier-Basis statt und sind aufgrund ihrer Kom- plexität auf einige Tage beschränkt. Die Messungen werden in Respirationskammern und somit in einer künstlichen Umgebung unter potentieller Beeinflussung des Tieres durchge- führt. Im Gegensatz dazu wird der CO2-Austausch von Ökosystemen üblicherweise mit der
’Eddy Kovarianz’ (EC) Methode gemessen. Neuerungen in der Lasertechnik und die Ent- wicklung von sensitiven und schnellen Messgeräten ermöglichen nun die Anwendung der EC Methode auch für CH4 und N2O Messungen. Die Methode ermöglicht eine beeinflus- sungsfreie Messungen des THG-Austauschs einer bestimmten Fläche (dem sogenannten
’Footprint’) über eine längere Zeitspanne. Lage und Grösse des Footprints sind dyna- misch und hängen von meteorologischen Bedingungen ab. Für aussagekräftige Resultate zum Austausch eines bestimmten Ökosystems wird deswegen eine gewisse räumliche und zeitliche Homogenität vorausgesetzt. Bei beweideten Systemen wird die Messung des Spu- rengasaustausches durch die anwesenden Tiere (und deren variable räumlich und zeitliche Verteilung) verkompliziert.
von den Tieren emittiert als auch zwischen Wiese und Atmosphäre ausgetauscht werden.
Für die einjährige Messperiode (inkl. 209 Tage Weidesaison) wurden 20 Milchkühe in einem Umtriebsweide-System gehalten. Für eine detaillierte Interpretation der gemessenen Flüsse wurden die Positionen (GPS) und die Fress-Aktivität (Kausensoren) der Kühe aufgezeichnet.
Die Positionsdaten der Kühe erlaubten eine klare Trennung zwischen Austauschflüssen mit und ohne Kuhbeitrag. Wenn sich Kühe im Footprint aufhielten, wurden CH4-Flüsse zwischen 100 und 1200 nmol m−2s−1 gemessen, während Flüsse ohne Kühe im Footprint viel geringere Werte um 4 nmol m−2s−1 zeigten. Beim CO2 war der Einfluss der Kühe weniger klar ersichtlich, da der Kuhbeitrag in derselben Grössenordnung wie der CO2- Austausch des Boden-Vegetationssystems war. Die Bestimmung des Kuh-Anteils in den gemessenen Flüssen war für CO2 deshalb schwieriger als für CH4. Für die Berechnung des Kuh-Anteils, wurde der CO2-Fluss des Boden-Vegetationssystems basierend auf den Daten ohne Kuh-Einfluss modelliert und vom gemessenen Fluss abgezogen.
Flächenbezogene Kuh-Flussanteile konnten mit Hilfe der Kuhpositionen und einem analytischen Footprint Modell in tierbezogene Emissionen umgerechnet werden. Die resul- tierende mittlere CH4-Emission von 423±24 g CH4Tier−1d−1 wie auch die mittlere CO2- Emission von 4.6±1.6 kg C Tier−1d−1 stimmen gut mit publizierten Respirationskammer- Messungen vergleichbarere Kühe (Milchleistung, Gewicht) überein. Wegen der beträchtli- chen Streuung der flächenbezogenen Kuh-Flussanteile konnten für die tierbezogenen Emis- sionen nur Mittelwerte oder mittlere Tagesgänge über längere Zeitspannen mit der nötigen Genauigkeit bestimmt werden. Für kürzere Messkampagnen, wie zum Beispiel Fütterungs- versuche auf einer Weide, scheint die Methode zu grosse Unsicherheiten zu haben.
Unter Einbezug aller bewirtschaftungsbedingten Kohlenstoff-Flüsse wurden die CH4- und CO2-Messungen zur Abschätzung der Kohlenstoff-Änderung im Boden verwendet.
Dabei wurde mit zwei verschiedenen Systemgrenzen gearbeitet (mit und ohne Einbe- zug der Kühe), welche zu vergleichbaren Resultaten für das Messjahr führten. Sie waren mit grossen Unsicherheiten behafteten und zeigten deshalb keine signifikante Änderung des Kohlenstoff-Speichers im Boden an. Dieses Ergebnis wurde in einer THG-Bilanz in Bezug zu den anderen THG (CH4 und N2O, als CO2-Äquivalente) gesetzt. Der höch- ste Anteil im THG-Budget machten die CH4-Emission der Tiere aus, während aber der Kohlenstoff-Austausch eine viel grössere Unsicherheit aufwies. Diese Unsicherheit, könnte durch kombinierte Messungen des Kohlenstoff-Umsatzes (insbes. Nahrungsaufnahme und Atmung) an den Einzeltieren reduziert werden.
Summary iii
Zusammenfassung v
1 General introduction 1
1.1 Anthropogenic greenhouse gases . . . 1
1.2 Agricultural sources and sinks of greenhouse gases . . . 3
1.3 Measurement methods for agricultural greenhouse gas exchange . . . 4
1.3.1 Individual animal measurements . . . 4
1.3.2 Ecosystem measurements . . . 5
1.4 Objectives of the doctoral thesis . . . 6
2 The eddy covariance method and the application in this project 9 2.1 Methodological basics . . . 9
2.1.1 Flux determination . . . 9
2.1.2 Flux footprint concept . . . 13
2.2 Measurement setup . . . 16
2.2.1 Eddy covariance system . . . 16
2.2.2 Animal position tracking . . . 17
2.2.3 Mastication monitoring . . . 20
2.3 Partitioning of measured fluxes . . . 21
2.3.1 Effect of cows on concentrations and fluxes . . . 21
2.3.2 Partitioning principle . . . 22
2.3.3 Partitioning of CH4 fluxes . . . 23
2.3.4 Partitioning of CO2 fluxes . . . 24
2.3.4.1 Nighttime fluxes . . . 25
2.3.4.2 Daytime fluxes . . . 26
2.3.4.3 Cow respiration . . . 27
2.4 Cow activity on the pasture . . . 28
2.4.1 Resolving cow activity in space and time . . . 28
2.4.2 Combining cow activity and CH4 emission measurements . . . 30
3.1 Introduction . . . 32
3.2 Material and methods . . . 33
3.2.1 Study site and grazing management . . . 33
3.2.2 Eddy covariance measurements . . . 35
3.2.2.1 Instruments and setup . . . 35
3.2.2.2 Flux calculation . . . 36
3.2.2.3 Detection limit and flux quality selection . . . 38
3.2.3 GPS method for deriving animal CH4 emission . . . 40
3.2.3.1 Animal position tracking . . . 40
3.2.3.2 Footprint calculations . . . 41
3.2.3.3 Calculation of average cow emission . . . 42
3.2.4 PAD method for deriving animal CH4 emission . . . 43
3.2.4.1 Footprint calculation for paddocks . . . 43
3.2.4.2 Determination of average cow emission . . . 44
3.2.5 FIELD method for deriving animal CH4 emission without position information . . . 44
3.3 Results . . . 44
3.3.1 Methane fluxes with and without cows . . . 44
3.3.2 Footprints and cow influence . . . 46
3.3.2.1 Roughness length . . . 46
3.3.2.2 Footprint weights of cows and paddocks . . . 47
3.3.3 Methane emissions per cow . . . 48
3.3.3.1 Overall statistics . . . 48
3.3.3.2 Diurnal variations . . . 49
3.4 Discussion . . . 49
3.4.1 Flux data availability and selection . . . 49
3.4.2 Source distance effect and footprint uncertainty . . . 51
3.4.3 Comparison to published respiration chamber results . . . 52
3.4.4 Systematic and random-like variations of cow emission . . . 53
3.4.5 Relevance of cow position information . . . 54
3.5 Conclusions . . . 56
4 Discerning the cows from the pasture: Quantifying and partitioning the NEE of a grazed pasture using animal position data 59 4.1 Introduction . . . 60
4.2 Material and methods . . . 62
4.2.1 Study site and herd management . . . 62
4.2.2 Instruments and setup . . . 63
4.2.2.1 Flux measurements . . . 63
4.2.2.2 Animal position tracking . . . 64
4.2.2.3 Ancillary measurements . . . 64
4.2.3 Flux calculation and selection . . . 65
4.2.4 NEE gap filling, partitioning, and uncertainty . . . 65
4.3.1 Environmental conditions and grazing management . . . 69
4.3.2 NEE with and without cow influence . . . 70
4.3.3 Partitioning into R and GPP . . . 73
4.3.4 Effect of grazing on vegetation height and GPP . . . 74
4.3.5 Animal respiration . . . 77
4.3.5.1 Emission per cow . . . 77
4.3.5.2 Field-scale cow respiration flux . . . 78
4.3.6 Implications for NEE evaluation of grazed pastures . . . 79
4.4 Conclusions . . . 80
5 Determination of the carbon budget of a pasture: Effect of system bound- aries and flux uncertainties 83 5.1 Introduction . . . 84
5.2 Material and methods . . . 85
5.2.1 Study site . . . 85
5.2.2 Carbon budget concept . . . 86
5.2.3 Determination of area-related fluxes . . . 88
5.2.3.1 CO2 fluxes . . . 88
5.2.3.2 CH4 fluxes . . . 88
5.2.3.3 Fertilizer application . . . 89
5.2.4 Determination of animal-related fluxes . . . 89
5.2.4.1 Products . . . 89
5.2.4.2 Feed intake . . . 90
5.2.4.3 Excreta . . . 91
5.2.5 Greenhouse gas budget . . . 91
5.3 Results and discussion . . . 92
5.3.1 Carbon budget of the dairy cows . . . 92
5.3.2 Carbon budget of the pasture system . . . 93
5.3.3 Applicability of the NECB approaches . . . 96
5.3.4 Greenhouse gas budget of the dairy cow pasture . . . 97
5.4 Conclusions . . . 98
6 General conclusions 101 6.1 Synthesis . . . 101
6.2 Outlook . . . 103
References xi
List of abbreviations and symbols xxi
Acknowledgements xxvii
Curriculum vitæ xxix
1
General introduction
1.1 Anthropogenic greenhouse gases
Life on Earth in the present form is enabled by the atmosphere. The atmosphere protects life from harmful incoming radiation, is responsible for a warming effect (greenhouse effect) and also reduces temperature extremes between day and night (Thornes et al., 2010).
Without the warming effect, induced by re-radiation of outgoing longwave radiation, Earth’s surface temperature would be at −18◦C with unfavorable conditions for today’s life. The capture of the outgoing radiation by the so-called greenhouse gases (GHGs) increases the average surface temperature to 15◦C. The primary GHGs are water vapor (H2O), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Human activities have been responsible for substantially increasing concentrations of CO2, CH4 and N2O since pre-industrial time, leading to an additional warming effect (IPCC, 2014). Whereas burning of fossil fuels and emissions through deforestation are the main human induced sources for increasing CO2 concentrations, the increased food demand by growing Earth’s population is responsible for increased CH4 and N2O con- centrations (Crutzen, 2002). The contribution of the different gases to climate change is expressed as global warming potential (GWP) relative to CO2 (CO2-equivalent). GWPs are calculated for different time horizons because they depend on the atmospheric lifetime and the radiative forcing (i.e., the efficiency of a gas to act as GHG). For example, on a 100 year time horizon the GWPs of CH4and N2O are 25 CO2-eq. and 298 CO2-eq., respec- tively (Solomon et al., 2007). Thus one gram of CH4 has a 25 times stronger effect on the climate than one gram of CO2. Global climatic models project up to 4◦C higher global mean surface temperatures in the next century (Solomon et al., 2007) causing various risks and potential damage on natural and human systems (e.g., sea levels rise, frequency and severity of extreme weather situations, loss of biodiversity, regional change in agri- cultural productivity, etc.). These consequences affect human well-being, and reductions in anthropogenic GHG emissions are needed to mitigate these effects of global warming (IPCC, 2014).
(b) (a)
Figure 1.1: (a)Contributions by sectors to total anthropogenic greenhouse gas emission (source: IPCC, 2014, p. 88). (b)Contributions by sectors and different sources within the sectors (ri: rice cultivations, ru: ruminants, ro: nitrogen run-off, ag: agriculture, c: coal mining, ng: natural gas, oil, and industry) of anthropogenic CH4 and N2O (source: Montzka et al., 2011, modified).
The top three emitters of GHGs are China, USA and the European Union, which are together responsible for about 45 % of total anthropogenic GHG emissions (WRI, 2014).
Figure 1.1a indicates the contributions of different sectors to the total anthropogenic GHG emissions reported in the latest Intergovermental Panel on Climate Change (IPCC) report. The electricity and heat production sector is the major contributor, followed by the industry and agriculture. Anthropogenic CO2 emissions account for 76 % (65 % from fossil fuel combustions and 11 % from forest and land use) of the total anthropogenic GHG emissions, followed by CH4and N2O with 16 % and 6 %, respectively (IPCC, 2014). Fossil fuel burning is the largest human source for CO2 emissions accounting for 87 % of total CO2 emissions, followed by land-use change and the industrial sector accounting for 14 % and 9 % of total anthropogenic CO2 sources (Le Quéré et al., 2013). The major source for non-CO2 GHG emissions is the agricultural sector emitting the largest amounts of CH4
and N2O (Fig. 1.1b). CH4 emissions from ruminants constitutes the largest share of these emissions.
The IPCC method to estimate GHG emissions is based on up-scaling approaches using standard emission factors (IPCC, 2006). For example the number of dairy cows is multiplied with a default CH4 rate of dairy cows (IPCC Tier 1 approach). However, to reduce the uncertainty related to default emission factors, countries are encouraged to determine country specific and individual source specific emission factors (Tier 2) using more detailed (input) data (e.g., emission factors estimated from gross energy intake and CH4 conversion factors for specific livestock categories). Sophisticated models (Tier 3) using even more detailed information, e.g., diet composition or seasonal variation in livestock numbers, can further improve country specific emission estimates. Thus emission measurements are needed for the development and validation of such models.
1.2 Agricultural sources and sinks of greenhouse gases
IPCC reports a contribution of 14 % to total GHG emissions from the agriculture sector (IPCC, 2014). However, estimates that include all direct (i.e., from soils and livestock) and indirect emissions (i.e., from fossil fuel use, production of agrochemicals, and conversion of land to agriculture) range from 17 to 32 % (Bellarby et al., 2008). Total direct annual agricultural GHG emissions (5.4 Gt CO2-eq. in 2011) are at the highest level in history and are projected to increase further (Tubiello et al., 2015). Direct agricultural GHGs consists of CH4 and N2O. With a share of 40 %, the emissions from enteric fermentation is the greatest contributor to the total agricultural GHG release, followed by manure left on the pasture (16 %), use of synthetic fertilizer (13 %), and rice cultivation. The major source of CH4 emissions is enteric fermentation from ruminating animals, but also manure management and rice cultivation are sources of CH4 emissions. N2O is emitted from soil processes induced from synthetic fertilizer use and manure left on or applied to soils as well as from manure management (Tubiello et al., 2014). Due to high uncertainty levels of input data and estimation methodology uncertainty these emission estimates are associated with uncertainties of up to 150 % (Tubiello et al., 2015).
In Switzerland, the agricultural sector accounts for about 12 % of national GHG emis- sions (5’949 kt CO2-eq. in 2013). Changes in cattle population and in the use of mineral fertilizer led to a decrease in CH4 and N2O emission over the last two decades (FOEN, 2014). On the national scale, Switzerland’s agriculture is responsible for 85 % of the CH4 and for 80 % of the N2O emissions (BAFU, 2015). The major contributor of Swiss agricultural GHGs (54 %) is enteric fermentation from livestock (FOEN, 2014).
Attempts have been made to reduce CH4 emissions in livestock by feeding options (Beauchemin et al., 2008) as well as by the selection of breeds with genetically low CH4 production (Münger and Kreuzer, 2008). However, according to IPCC (2014) one of the most cost-effective mitigation options for agriculture is grassland management by grazing, i.e., pastures systems. The key mechanism is enhanced soil carbon sequestration in grasslands (Lal, 2004; Allard et al., 2007; Soussana et al., 2010) which potentially counteracts the other GHG emissions.
Figure 1.2 illustrates the GHG fluxes of a pasture system. Whereas the soil can be a sink or source for CH4 (through anaerobic decomposition of organic matter in the soil or oxidation of atmospheric CH4, Topp and Pattey, 1997), the grazing animals emit CH4 due to enteric fermentation processes in the rumen, and to lesser extent from feces (Flessa et al., 1996). N2O is emitted by nitrification and denitrification processes, which depend on the availability of nitrogen (from animal excreta or from fertilizer) as well as environmental factors, mainly soil moisture content (Flechard et al., 2007). CO2 is exchanged with the soil and vegetation as well as exhaled by the animals (Soussana et al., 2004). Although the exchange of CO2 is quite large, it is only the change in the soil carbon pool (∆SOC/∆t), which has a potential mitigation effect (Ammann et al., 2007).
For assessing carbon storage change in the soil, all relevant carbon in- and export fluxes need to be considered, i.e., gaseous carbon exchange in form of CO2 and CH4, carbon
input from applied slurry, carbon removed in animal products (in milk or meat) and in harvest. To assess the total net effect of a pasture system on climate change the mitigation potential of carbon sequestration needs to be balanced against the emissions of the other GHG gases (i.e., CH4 and N2O). Thus experimental studies should include all relevant GHGs.
Figure 1.2: Greenhouse gas fluxes of a pasture. To offset the contribution to global warming from net CH4 and N2O emissions, the stock of carbon in the soil needs to be increased.
1.3 Measurement methods for agricultural greenhouse gas exchange
The assessment of CH4emissions from ruminants is commonly based on individual shorter- term animal measurements and results are expressed as amount CH4 per animal and time (Sect. 1.3.1), whereas the CO2and N2O exchange of (agricultural) ecosystems is commonly assessed by surface area related measurements (in units of mass per area and time) by chamber or micrometeorological approaches over longer time periods (Sect. 1.3.2).
1.3.1 Individual animal measurements
Commonly, measurements of individual animal emissions are conducted in open-circuit respiration chambers; however, for some decades tracer techniques have also been used to measure individual animal emissions in-situ on the pasture. The overviews of the two techniques given here follow the reviews of Johnson and Johnson (1995) and Storm et al.
(2012).
Open-circuit chamber measurements derive emissions by determination of the mass balance between chamber import and export, i.e., total air flow through the chamber and
the concentration difference between the inflow and outflow. Results from respiration chamber measurements are considered to be the most accurate and precise means of measuring CH4 and CO2 emissions from animals. Animals usually spend two to five days in the constraint environment of a respiration chamber and limited animal movements leads to lower energy expenditure. Thus the validity of results obtained from respiration chambers have been queried for their representation of emissions on pasture where the cattle behave more naturally (Johnson et al., 1994). Respiration chamber measurements serve as a basis for inventory emission factors and are also used to study the effects of diet and fodder supplements on the amount of emitted CH4 (Hindrichsen et al., 2006b,a;
Münger and Kreuzer, 2006, 2008).
A method to derive in-situ emissions from grazing animals is the sulfur hexafluoride (SF6) tracer technique (Johnson et al., 1994). The basic principle is that two gases dis- perse identically from the rumen into the animal environment and that the animal’s CH4
emission rate can be estimated from the concentration ratio of CH4/SF6 if the release rate of the SF6 source is known. A permeation tube that contains a small amount of SF6 (∼6 g), is placed in the rumen of the animal. Breath samples are collected through a cap- illary tube placed close to the mouth and nostrils of the animal and sample air is collected in evacuated canisters mounted at the back or around the neck of the animal and is later analyzed in the laboratory. Typical sample intervals correspond to one feeding cycle of one day (e.g., Johnson et al., 1994; Pinares-Patiño et al., 2007; Laubach et al., 2014) but also higher sample frequencies have been shown to yield reliable data to investigate the temporal pattern of CH4 emissions (Lassey et al., 2011; Tekippe et al., 2011). Besides using a strong GHG as tracer, this approach misses CH4 emissions through the rectum.
1.3.2 Ecosystem measurements
For several decades the exchange of CO2 from ecosystem has been measured by the eddy covariance (EC) technique (details about the theory and the application are given in Chap. 2). The EC technique is a micrometeorological measurement approach deriving spatially integrated fluxes over a certain area at relatively high temporal resolution (e.g., 10 to 60 min) from a single point at a measurement tower and is thus a non-intrusive method (Baldocchi et al., 1988; Foken et al., 2012a). The EC technique is most suitable for long-term investigations and various research programs (e.g., in Europe: Greengrass, CarboEurope, NitroEurope, GHG-Europe) were initiated in the last decades to inves- tigate trace gas exchange over grassland systems using the EC technique. They aimed to calculate GHG budgets on the European scale and also to quantify the interaction between carbon (C) and nitrogen (N) cycling.
Ecosystem exchange of CH4 and N2O have been most frequently measured by soil chamber techniques, because the application of the EC approach to measure these gases was hampered by technical limitations. In the last decade technical advances in tunable diode laser spectrometry have led to the development of new instruments suitable for EC measurement of CH4 and N2O (Peltola et al., 2013). This enables the opportunity for long-term measurements of all three GHGs together with the same technique. Especially
for pasture systems, where the use of chambers is difficult due to animal interference, the EC technique offers a suitable way to measure CH4 and N2O emissions of the grazed system including the possibility for non-intrusive CH4 measurements of animal emissions under real grazing situations.
There are already some studies, which measured CO2exchange over pastures using the EC method, but the emissions of animals played a minor role in the interpretation of the gas exchange; the cow contribution was regarded as small compared to the soil respiration (e.g., Jérôme et al., 2014). For example Gilmanov et al. (2007) used data from 20 European sites of which 12 were at least partially grazed to determine ecosystem respiration, but did not especially account for the contribution of the animals. Several studies (e.g., Nieveen et al., 2005; Jaksic et al., 2006; Nagy et al., 2007; Soussana et al., 2007; Gilmanov et al., 2007) assumed to fully include animal respiration in a representative way, whereas others cautioned against EC data when grazing animals were present (e.g., Zeeman et al., 2010;
Baldocchi et al., 2012) or even discarded such data (Skinner, 2008). In contrast to CO2, CH4 emissions from ruminating animals are potentially much larger than the emissions from the soil and thus should be detectable by the EC method. However, no systematic assessment of EC measurements for the application over pasture have been performed up to now.
1.4 Objectives of the doctoral thesis
Pasture systems represent a combination of individual animal emissions and area-related emissions normally measured with different techniques. The goal of this thesis was to mea- sure and characterize both types of emissions using one technique; the area-integrating eddy covariance technique. The suitability of eddy covariance measurements for in-situ CH4 and CO2 flux measurement over a pasture with cows under realistic grazing situa- tions was tested and the effect of cow emissions contributing to the measured flux was investigated. The following questions were addressed:
• How representative are eddy covariance flux measurements over a grazed grassland?
Do they adequately include the emissions of the animals?
• Is it possible to disentangle measured fluxes into animal-related fluxes and soil/vege- tation-related fluxes?
• Are eddy covariance measurements suitable to quantify in-situ average animal emis- sions under typical grazing management practice and can they be used to validate up-scaling of individual animal measurements to the ecosystem-scale?
• What is the carbon budget of the investigated pasture and to what extent can the carbon sequestration compensate for the CH4 and N2O emissions of the grazing system?
The thesis is structured as follows: Chapter 2 introduces the eddy covariance theory and describes the application on the investigated pasture including information about the associated animal measurements for a comprehensive flux interpretation. Informa- tion on how measured CH4 and CO2 fluxes could be partitioned into different underlying processes and details regarding the activity and distribution of the cows on the pasture are presented. Chapter 3 and 4 consist of two manuscripts describing the application of CH4 and CO2 eddy covariance measurements to assess exchange fluxes over a pasture.
The first manuscript deals with the CH4 measurements and presents a method to de- rive animal emissions from eddy covariance measurements. The second deals with CO2
measurements and the effect of cow respiration on annual net ecosystem CO2 exchange.
A third manuscript (Chap. 5) presents the carbon budget and the GHG balance of the pasture. The thesis concludes with general conclusions and an outlook (Chap. 6).
2
The eddy covariance method and the application in this project
2.1 Methodological basics
2.1.1 Flux determination
The following section gives an overview about the EC method and is based on information found in Ammann (1998), Sintermann (2011), and Aubinet et al. (2012b).
A flux (F) of a quantity through a plane is determined by the product of the transport velocity (vn) normal to the plane and the trace gas concentration (c):
F =vn·c (2.1)
F represents an ensemble average over many spatial and temporal realizations. Under stationary conditions (i.e., mean and variance of a time series do not change with time and follow no trend) and over a homogeneous surface the ensemble average is equal to the respective temporal or spatial average. The EC approach (Fig. 2.1) takes advantage of this and determines the vertical exchange F through a virtual plane at the measurement height (z) above ground by measuring the time averaged product of the vertical wind speed (w) and the concentration (c) at a single point location as:
Fz =w c (2.2)
The driver of the vertical flux is atmospheric turbulence: eddies of different sizes transport the quantity up- and downward independently of the mean wind flow. The fluctuating signals of w and c are measured for a certain time interval and decomposed by the so called Reynolds decomposition into a mean (indicated by the overbar) and turbulent part (indicated by the prime), e.g., c=c+c′. Equation (2.2) then yields:
F =w c+w c′+w′c+w′c′ (2.3)
mean wind (u)
measurement height (z)
� , �
Figure 2.1: Schematic principle of eddy covariance measurements. The flux at the measurement height is derived by the covariance (w′c′) of measured wind speed (basically vertical wind w) and trace gas concentration (c) (Source: Sintermann, 2011, modified).
By assuming only vertical turbulent air motion (i.e., w = 0) and following Reynolds averaging rules Eq. (2.3) reduces to the covariance of w and c:
F =w′c′ (2.4)
If there is no vertical flux divergence between the measurement height and the surface, the measured flux directly corresponds to the surface-atmosphere exchange. For a quantity with no source or sink processes in the air and under stationary conditions this condition is complied and the EC flux represents the exchange flux at the surface.
Practically, to meet the above described requirements to use Eq. (2.4), the wind vectors (u, v, w) are rotated so that u points into the mean wind direction and w as well as v become zero indicating a new reference coordinate frame. This can be achieved by double rotation: the wind vectors are rotated that u points in the main wind direction forcing v = 0, followed by a rotation around the x-axis to achieve w = 0. The vertical rotation indicates how strong the virtual plane is tipped against the streamline and can be used to exclude cases with non-horizontal wind fields (Rebmann et al., 2012).
To apply Eq. (2.4) for trace gas fluxes, the measurements ofwandcneed to be taken at the same point and need to be fast enough to resolve the variability induced by the atmo-
spheric turbulence. The largest fraction of turbulent transport occurs between frequencies of 0.01 to 1 Hz, thus accurate measurements of wind speed and trace gas concentration at 10 Hz are recommended to resolve also smallest contributions of turbulence. Intervals of 30 to 60 min have been shown to cover most eddies contributing to the exchange flux (Foken et al., 2006) and thus allow measurements at relatively high time resolution.
Figure 2.2a and b show time series at 10 Hz forwand CO2mixing ratio measured over a 30 min interval. Thewcomponent is already rotated to fulfill the requirements of Eq. (2.4), i.e.,w= 0. The CO2 mixing ratio was measured by a closed-path instrument (Sect. 2.2.1).
Closed-path instruments require an inlet tube through which the air sample is pulled into the measurement cell. This leads to a time lag between the required simultaneous measurement of wind components and trace gas concentration, which needs to be corrected before Eq. (2.4) can be applied. A common way to derive the flux is to analyze the cross-covariance function (Fig. 2.2c). It is derived by shifting one time series against the other and calculating the covariance for each shift time step. The extreme of this function represents the flux and the effective lag time. In this example a negative flux of
−19 µmol m−2s−1 with a lag time of 1.6 s is derived. The peak height described by the cross-covariance function depends on the strength of the flux. Small fluxes (i.e., low peaks) are often hidden in the variability of the cross-covariance function and the determination of the maximum cannot be achieved. In such situation the flux needs to be derived at the before-hand determined lag.
Eddy covariance fluxes need to be corrected because practical instrumentation can never fully meet the requirements of the underlying flux theory (Aubinet et al., 2012b).
Besides instrument specific corrections, the main sources for errors in EC flux calculations arises from spectral losses in the measured signal (high- and low-frequency loss) and due to density fluctuations in the trace gas concentration induced from heat and water vapor fluctuations.
Low-frequency damping that leads to an underestimation of a flux arises from too short sampling intervals. The so-called ogive test (e.g., Desjardins et al., 1989) can be used to test if a flux has its extreme within the adopted measurement interval. If not, a longer averaging interval needs to be considered for the calculation.
High-frequency signal loss leads to an underestimation of the flux and occurs due to inadequate sensor frequency response, line averaging (i.e., the quantity is not measure at a single point but in a certain volume over a line), sensor separation, and in closed- path systems due to air transportation through the inlet tube. Thus the flux attenuation is setup dependent and needs to be determined for each flux separately. For systems requiring inlet tubes, the signal attenuation also depends on the cleanness of the tube walls and on atmospheric conditions (stability, wind speed, etc.).
Errors in the flux due to fluctuations in the trace gas density, which are induced by fluc- tuations in temperature and humidity, can be corrected by the so-called WPL correction (named after the three authors of the paper describing the correction; Webb, Pearman and Leuning, 1980). Whereas this correction is essential for open-path sensors, it is of minor importance for closed-path systems (setup dependent). Temperature fluctuations
−1012 w [m s−1 ]
w (a)
380400420
CO2[ppm]
0 10 20 30
Time [min]
c (b)
−20−100
CO2Flux [µmolm−2 s−1 ]
−40 −20 0 20 40
Lag time τ [s]
(c)
Figure 2.2: Time series (10 Hz) of (a)vertical wind speedw and(b)CO2 mixing ratio. The vertical lines indicate the mean over 30 min. The wtime series is rotated to achievew= 0. (c) The maximum of the cross-covariance function corresponds to the flux and the offset of the peak to time 0 indicates the lag time between the two quantities.
are reduced to less than 1 % if an inlet tube of several meter length is used. To circumvent corrections due to water vapor fluctuations the air can be dried before the measurement, or dry trace gas mixing ratios can be calculated from simultaneous measurements of water vapor and wet trace gas mixing ratios.
Eddy covariance measurements provide flux data at typically 30 min time resolution.
Investigations on annual time scales but also at intermediate time resolution (monthly or daily) can be achieved by EC measurements. However technical failures (power failures, sensor calibration issues, etc.) and environmental conditions (like rain, ice, fog, or insects mainly critical for open-path sensors) lead to gaps in flux data sets. Additionally, under some atmospheric conditions the underlying theory (assumption of stationarity, fully es- tablished turbulence, etc.) is violated. Because there is no strict limit to decide whether the EC theory is violated (e.g., when is a fully turbulent regime established) Foken et al.
(2004) suggested to classify measured fluxes using a quality flag system. Depending on the research question, fluxes can be selected according to their quality (quality selec- tion) against the expense of more gaps. For fundamental research (e.g., the development of parameterizations) it is recommended to use high quality fluxes (hereafter referred
to as ’best’ quality), whereas for continuously running systems aiming to assess annual exchange, lower qualities are often better than gap-filled data (Foken et al., 2004).
2.1.2 Flux footprint concept
Eddy covariance measurements represent spatially integrated fluxes from an area that extends upwind of the tower, the so-called flux ’footprint’ (FP) area; broadly speaking this is the area which is ’seen’ by the flux measurement at the sensor location (Fig. 2.3).
The FP function mathematically describes the relative contribution of each location to the measured flux (Schmid, 2002). The relationship between the source distribution and the vertical flux above the origin of a coordination system and no contributions from downwind is defined as (adapted from, Schmid, 1994):
F(0,0, z) =
∞
Z
−∞
∞
Z
0
S(x, y,0)ϕ(x, y, z)dx dy (2.5) where,F is the measured flux at heightzin the origin of the coordination system,S(x, y,0) is the surface source flux field, and ϕ(x, y, z) the FP weighting function.
The FP function can be derived by analytical or stochastic Lagrangian approaches, or large eddy simulation (Kljun et al., 2003). Large eddy simulation models directly calculate the three-dimensional, time dependent turbulence motions, and only parame- terize sub-grid scale processes (Sullivan et al., 1994; Rannik et al., 2012), with the cost of very high computational power. Lagrangian models calculate the diffusion of particle by stochastic differential equations, whereas analytical models use analytical solutions of the advection-dispersion equation. The latter are the fastest in computational sense but have the strongest restrictions regarding their use under different meteorological conditions.
An overview of existing models and their mathematical functions can be found in Schmid (2002).
In this thesis the analytical FP model of Kormann and Meixner (2001) was used.
Figure 2.3 illustrates the distribution of the FP weighting function (ϕ; Eq. 3.1) in 3-D as well as a projection on the x-y-plane. The equation of ϕ(x, y) is given in Sect. 3.2.3.2.
The 3-D surface describes the contribution of each surface location to the measured flux, i.e., the weight of each point in the x-y-plane for the spatial averaging of the measured flux. The contour lines in the 2-D-view indicate isopleths that comprise 20 %, 40 %, 60 %, and 80 % (from inside outwards) of the total FP weight. The FP weight is symmetric around the x-axis (mean wind direction). It has its maximum at some distance upstream of the tower and falls off asymptotically at all sites. The orientation of the FP depends on the wind direction whereas the extent depends on turbulence characteristics (atmospheric stability and roughness of the surface) and the height of the sensor (Leclerc et al., 1997).
Rougher surfaces produce more turbulence, which supports mixing, and thus shorter FP are observed than over smoother surfaces. The FP theoretically has an infinite spatial extent (cf. Eq. 2.5). However for practical reasons it is often restricted to the area where the footprint function drops to 1 % of its maximum (Neftel et al., 2008). The FP (in
2-D) has an ovoid shape and a length typically about 100 times the measurement height (Dabberdt et al., 1993). As the extent of the FP changes under different turbulence situations, the position of the maximum changes its position on the x-axis. The location of the maximum typically ranges between a few times the measurement height for unstable atmospheric conditions (daytime) and a few dozen times for stable atmospheric conditions (nighttime).
Figure 2.3: Schematic representation of a flux footprint, i.e., the area that is ’seen’ by the flux measure- ment. The footprint describes the relative contribution of each location to the measured flux. Its weight is indicated in 3-D and projected to 2-D where the isopleths comprise 20, 40, 60, and 80 % (from inside outwards) of the total footprint weight. The light blue arrows indicate the flux through the plane at the measurement height, the dark blue arrow indicates the mean wind direction.
Under ideal spatial condition (i.e., infinite large homogeneous source distribution) around the tower the flux is independent of the wind direction because it is by definition equal in all directions (Schmid, 2002). However, to cover a large variety of ecosystems, measurement sites of the FLUXNET community have also been established in less homo- geneous areas, for example with variable land cover types (e.g., agricultural fields with different crops). In such systems, FP analysis is needed for quality assessment of mea- sured fluxes and to test their spatial representativeness (Rannik et al., 2012). Footprint model results are used to reveal the compositions of fluxes from different areas and the information is used to characterize the variability in the flux time series caused by chang- ing ’views’ of the sensor. The results of FP analysis can be used to filter flux data with large contributions from areas which are not of interest. However as shown by Neftel et al.
(2008) and Ammann et al. (2010) FP results can also be used to correct fluxes originating from limited source areas.
If an EC system is installed in an agricultural region the emission area of interest is often of limited extent and the FP can exceed it in size (Fig. 2.4). This leads to an underestimation of the area source strength by the measured fluxes, which can be regarded as diluted. Under the assumption that only the area of interest (gray area in Fig. 2.4)
emits the measured gas species, the FP contribution needs to be evaluated by integration of the FP function over this area. The true source strength is then yielded by dividing the measured flux through the integrated FP weight over the investigated area (i.e., solving Eq. (2.5) for S). Although there exist a simple tool to calculate FP fractions of areas around a tower (ART-footprint tool, http://www.agroscope.admin.ch/art-footprint-tool) only few studies have applied this correction (e.g., Tuzson et al., 2010; Sintermann et al., 2011).
−50 0 50 100 150 200
[m]
050100150
[m]
20%
40%
60%
80%
75%
25%
Figure 2.4: Schematic picture of a footprint covering more than the area of interest (gray field). The area of interest contributes to 75 % to the flux and the area outside to 25 % (white). The blue arrow indicates the wind direction, the blue circles are isopleths comprising 20, 40, 60, and 80 % of the total footprint weight. The blue triangle indicates the EC measurement tower.
Equation(3.1) describes the contribution of a single point source to the measured flux and can therefore be applied to the coordinates of the source, if the exact location is known, to derive the source strength. With more than one point source in the FP, their source strength can still be quantified, if it is assumed to be equal for all point sources.
Usually the exact location of the source(s) is however unknown (Dengel et al., 2011;
Baldocchi et al., 2012). Assuming an even (or random) distribution of many equal point sources over a defined area, the situation may be treated like a limited area source as described above.
Obviously, FP weights (either area or point source contributions) can be used to allocate fluxes to specific situations, e.g., situations with cows in the FP (Sect. 2.3 and Chap. 3).
2.2 Measurement setup
In addition to the information given in the manuscripts (Chap. 3–5), this and the following sections give more details on the realized measurements and on the methods applied in this thesis.
Figure 2.5 shows the experimental concept of this project: Cows are used as CH4 point sources. The movement of the cows leads to spatial and temporal inhomogeneous distributions of the point sources, thus cows are suited to test various different situations.
Animal positions are tracked by means of GPS devices (Sect. 2.2.2) and the FP contribu- tion of the cows for each 30 min interval can be calculated by applying the FP function (Eq. 3.1) on the cow positions.
Figure 2.5: Concept of eddy covariance measurement over a pasture. Position of cows (GPS) are combined with the flux footprint to determine the cow contribution.
2.2.1 Eddy covariance system
These animal FP contributions allowed a detailed allocation of fluxes into intervals with and without animal contribution, as well as the conversion of area-based fluxes into animal-related CH4 and CO2 emissions (for details see Chap. 3 and 4). Additionally information of the cows feeding behavior (Sect. 2.2.3) can be used for flux interpretation.
An overview of the EC setup is shown in Fig. 2.6a. The EC tower was placed in the center of the pasture, which was subdivided into six paddocks of equal size (see Fig. 3.1). The tower was protected from animal interference by an electrical fence. The system consisted of an ultra-sonic anemometer (Solent HS-50, Gill Instruments Ltd., UK;
Fig. 2.6b-1), an open-path CO2 and water vapor (H2O) gas analyzer (LI-7500, LI-COR Inc., US; Fig. 2.6b-2), and a closed-path fast greenhouse gas analyzer (FGGA; Los Gatos
Research Inc., US), housed in a temperature controlled trailer 20 m away from the EC tower, measuring CH4, CO2, and H2O. The sonic anemometer and the LI-7500 were placed as close as possible together at a height of 2 m above the ground. To enhance the roll-off of water droplets from the window (bottom) of the LI-7500 the devices was tilted by circa 45◦ from the vertical. For the concentration measurements by the FGGA, air samples were pulled by a vacuum pump (XDS35i Scroll Pump, Edwards Ltd., UK;
flow rate circa 45 SLPM) through a 30 m long PVC tube (8 mm ID). The inlet of the tube was placed slightly below the center of the sonic at a horizontal distance of 20 cm (Fig. 2.6b-3). Two particle filters with liquid water traps (AF30 and AFM30, SMC Corp., JP) were included in the sample line. The 5 µm air-filter (AF30), installed 1 m away from the inlet, avoided contamination of the tube walls. The micro air-filter (AFM30; 0.3 µm) additionally cleaned the air at the analyzer inlet. Sample frequency of all instruments was 10 Hz and the data stream was synchronized by a customized LabView (LabView 2009, National Instruments, US) program. A LabView visualization of the measurements and an online flux calculation allowed to control of the EC system by remote access.
Figure 2.6: (a)The eddy covariance measurement setup with the EC tower on the right hand side and the trailer housing the closed-path instrument on the left. The instruments are protected from animal interference by a two-wire fence. (b) Close-up picture of the main part of the EC system: (1) the ultra-sonic anemometer, (2) the open-path LI-7500, and (3) the inlet of the FGGA tube.
2.2.2 Animal position tracking
Animal position tracking was achieved with low cost commercial hiking GPS devices (BT- Q1000XT, Qstarz Ltd., TW; Fig. 2.7a). These GPS receivers use satellites of the Navstar global positioning system (GPS) and calculate the position by range-finding triangula- tion; the distance between the satellite and the GPS receiver is calculate from the signal propagation time (i.e., the time a radio signal needs to travel from the satellite to the GPS) multiplied by the speed of sound. The receiver is located on the intersection of the distances from at least three satellites (Bajaj et al., 2002).
Errors in the position determination have different sources (Bajaj et al., 2002):
• Atmospheric effects: Water vapor in the upper troposphere and ionic effects in the ionosphere slow down the signal and lead to propagation delay.
• Multipath error: The signal is not directly received but is bounced from a surface before reaching the receiver.
• Satellite geometry: Most precise positions are achieved when satellites are spread evenly across the sky. Unfavorable conditions occur when satellites are either all clustered or all distributed near the horizon.
• Percentage of sky visible: Obstacles which obscure a part of the sky reduce the number of satellites which can be used for the position calculation.
Atmospheric effects can be corrected by the principle of differential GPS (DGPS). The signal received from a satellite by two relatively close receivers experience the same aug- mentation during traveling. If the exact position of a reference receiver is known the signal of the other can be corrected for the difference between projected and actual position (Ba- jaj et al., 2002). A more sophisticated approach but with the same principle is provided by Satellite-Based Augmentation System (SBAS). Signal errors are calculated by accurately- located reference stations deployed across an entire continent. For Europe the European Geostationary Navigation Overlay Service (EGNOS: http://www.egnos-portal.eu) pro- vides differential correction data for dedicated GPS receiver. The accuracy, depending also on the device’s hardware, can then be improved down to meter accuracy (Witte and Wilson, 2005; Bolstad et al., 2005).
Figure 2.7: (a) Details of the GPS device and modified battery pack. (b) RumiWatch halter with noseband sensor (black part over the cow’s nose) and GPS box at the top of the cow’s head.
According to the manufacturer the included rechargeable battery lasts for 42 h. To reduce the frequency of needed battery recharging, the built-in battery was removed and the device connected to a modified battery pack (3×3.6 V lithium batteries; Fig. 2.7a)
which extended power supply up to ten days. This modification allowed logging and data storage at a frequency of 0.2 Hz for seven days before the battery had to be changed and the data downloaded. The battery pack and the logger were placed in a robust and watertight polycarbonate enclosure (Bopla Gehäuse Systeme GmbH, DE). The enclosure was attached with rivets to a cow halter (Fig. 2.7b) which includes a noseband sensor to measure jaw movements of the cow (Sect. 2.2.3). To prevent moisture condensation, desiccation bags with silica (Blue gel, Dry & Save GmbH, CH) were placed inside the enclosure.
As described above, the accuracy of GPS position measurements relies on the percent- age of the sky visible to the receiver, which can be constraint by buildings or topography, and is therefore site specific. The accuracy of the GPS device was therefore assessed at the particular site by a fixed point test with six devices placed directly side by side for five days logging data at 5 s intervals. This test revealed that each device shows individual variability in time and some systematic deviation from the overall mean point (Fig. 2.8).
The accuracy of each device was calculated as the 95 % quantile of deviations (red circle).
It was device dependent and ranged from 1.9 to 4.3 m, which lies in the range specified by the manufacture (2.5 m with EGNOS correction and 3 m without correction, respectively).
Figure 2.8: Two example results of the GPS device side-by-side fixed point accuracy test during five consecutive days. Each device showed an individual variability of the measured position with time, not correlated to other devices. The yellow line indicates data points of a one hour interval for which both devices used maximum number of visible satellites. The central red point indicates the average position over time and all devices. The distance comprising 95 % of all data points is indicated by the red circle.
The yellow line depicts the data recorded during one hour with the same number of satellites (total number of visible satellites) used for position determination, indicating that device internal corrections are responsible for the variation in measured locations.
Because of this effect, the application of a customized differential correction with one device placed at a fix known position at the pasture and the calculation of deviations from this fix point to apply the same correction to the cow GPS devices had to be dismissed.
Visual inspection of cow tracks revealed that some devices recorded unreliable positions which could not be removed by signal quality criteria (position dilution of precision). The positions radially jumped around (Fig. 2.9a) and some points even lay far outside the paddock being grazed. Tracks that appeared to jump around excessively (found by visual
inspection of the tracks) were excluded. An example of accepted GPS data is shown in Fig. 2.9b. During this interval of two hours, the cows were moving most of the time and visited almost the entire paddock.
−100 −50 0 50
[m]
−100−50050
[m]
(a)
−100 −50 0 50
[m]
(b)
Figure 2.9: Examples of cow tracks recorded by GPS: (a) Comparison of an accepted GPS track (yellow) and of bad GPS recordings (red). (b)Cow tracks of 20 cows during two hours of grazing. The blue triangle indicates the EC measurement tower.
2.2.3 Mastication monitoring
The feeding behavior (eating, ruminating, and idling activities) of each cow was measured by the RumiWatch (RW) health monitoring system (Itin+Hoch GmbH, CH). The RW system is a cow halter with a noseband sensor (Fig. 2.7b), which measures the jaw move- ment of the cow (Nydegger et al., 2010). The noseband sensor consists of a plant oil filled tube placed over the cow’s nose and a built-in pressure sensor records the pressure signal at 10 Hz. Moving of the mouth induces a sinusoidal pressure signal that is characteristic for an activity. During eating the pressure signal is much more irregular with changing amplitudes (Fig. 2.10a). During ruminating (Fig. 2.10b) the peaks of the pressure signal are more consistent and interrupted when cud is swallowed/brought back to the mouth.
The RW system was recently validated by comparing the RW data output to visually recorded activities of dairy cows (Zehner et al., 2012).
Unfortunately the data coverage of the activity data was less than 40 % due to system failures and inappropriate mounting. To extract useful data from the inappropriately mounted RW units, the data needed to be visually inspected. Files which showed no grazing during times on pasture or more than 6 h without ruminating activity were re- jected. Finally, data was only used for further analysis if more than 50 % of the RW units recorded reliable data resulting in 23 rotation phases (i.e., time to fully graze a paddock) with activity data.
Figure 2.10: RumiWatch noseband pressure signal during(a)eating and(b)ruminating activity.
2.3 Partitioning of measured fluxes
2.3.1 Effect of cows on concentrations and fluxes
The influence of cows on concentration and flux measurements is illustrated in Fig. 2.11.
It shows 30 min intervals of different grazing situations and the corresponding measured CH4 and CO2 concentration time series as well as the obtained fluxes. The figures rep- resent situations with good turbulent conditions (according to Foken et al., 2004) for EC measurements.
CH4 concentration measurements show large peaks (i.e., higher variability) when cows are present in the FP (Fig. 2.11c and d) compared to situations with no cows on the pasture (Fig. 2.11a) or no cows in the FP (Fig. 2.11b). Such variable behavior of CH4
concentration measurements in the presence of ruminating animals was already reported by Baldocchi et al. (2012). The influence of the cows on concentrations and fluxes is stronger if they are closer to the EC tower. Derived CH4 fluxes for situations with cows are one to two orders of magnitude higher than without cows in the FP.
In contrast, the variability of CO2 concentrations differs less between situations where cows are or are not present in the FP. During daytime the assimilation of CO2 by the vegetation is the dominant process. CO2 concentration patterns usually show drops from an upper level (rudimentary visible in Fig. 2.11a and b), and CO2 uptake fluxes of up to
−30 µmol m−2s−1 can be measured. CO2 uptake fluxes in the case with cows in the FP (Fig. 2.11c and d) are lower (i.e., are less negative) than without cows in the FP, since they also include fluxes induced by the cow respiration, which reduce the net uptake flux of the system. The amplitude of the CO2 uptake flux is determined by the vegetation state and by the amount of photosynthetic active radiation which can be reduced by clouds (Falge et al., 2001). Without additional data it is thus impossible to allocate the reduction in the CO2 flux when cows are present to the cows alone. We found that the contribution of the cows to the flux is in the same order of magnitude as the fluxes from the pasture alone (see Sect. 2.3.4.3).
