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Accuracy of micrometeorological techniques for detecting a change in methane emissions from a herd of cattle


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Accuracy of micrometeorological techniques for detecting a change in methane emissions from a herd of cattle

Johannes Laubach

Landcare Research NZ

Mei Bai

University of Wollongong, [email protected]

Cesar S. Pinares-Patino

Agresearch Grasslands Research Centre

Frances A. Phillips

University of Wollongong, [email protected]

Travis A. Naylor

University of Wollongong, [email protected]

See next page for additional authors

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Publication Details

Laubach, J., Bai, M., Pinares-Patino, C. S., Phillips, F. A., Naylor, T. A., Molano, G., Cardenas Rocha, E. A. & Griffith, D. W. T. (2013).

Accuracy of micrometeorological techniques for detecting a change in methane emissions from a herd of cattle. Agricultural and Forest Meteorology, 176 50-63.


Accuracy of micrometeorological techniques for detecting a change in methane emissions from a herd of cattle


Micrometeorological techniques are effective in measuring methane (CH4) emission rates at the herd scale, but their suitability as verification tools for emissions mitigation depends on the uncertainty with which they can detect a treatment difference. An experiment was designed to test for a range of techniques whether they could detect a change in weekly mean emission rate from a herd of cattle, in response to a controlled change in feed supply. The cattle were kept in an enclosure and fed pasture baleage, of amounts increasing from one week to the next. Methane emission rates were measured at the herd scale by the following techniques: (1) an external tracer-ratio technique, releasing nitrous oxide (N2O) from canisters on the animals’ necks and measuring line-averaged CH4 and N2O mole fractions with Fourier-transform infra-red (FTIR) spectrometers deployed upwind and downwind of the cattle, (2) a mass-budget technique using vertical profiles of wind speed and CH4 mole fraction, (3) a dispersion model, applied separately to CH4 mole fraction data from the FTIR spectrometers, the vertical profile, and a laser system measuring along four paths surrounding the enclosure. For reference, enteric CH4 emissions were also measured at the animal scale on a daily basis, using an enteric tracer-ratio technique (with SF6 as the tracer). The animal-scale technique showed that mean CH4 emissions increased less than linearly with increasing feed intake. The herd-scale techniques showed that the emission rates followed a diurnal pattern, with the maximum about 2 h after the feed was offered. The herd-scale techniques could detect the weekly changes in emission levels, except that the two vertical-profile techniques (mass-budget technique and dispersion model applied to profile) failed to resolve the first step change. The weekly emission rates from the external tracer-ratio technique and the dispersion model, applied to data from either the two FTIR paths or the four laser paths, agreed within ±10%

with the enteric tracer-ratio technique. By contrast, the two vertical-profile techniques gave 33–68% higher weekly emission rates. It is shown with a sensitivity study that systematically uneven animal distribution within the enclosure could explain some of this discrepancy. Another cause for bias was the data yield of the vertical-profile techniques being higher at day-time than at night-time, thus giving more weight to times of larger emission rates. The techniques using line-averaged mole fractions were less sensitive to the exact locations of emission sources and less prone to data loss from unsuitable wind directions; these advantages outweighed the lack of a method to calibrate CH4 mole fractions in situ.


herd, emissions, methane, cattle, change, accuracy, detecting, techniques, micrometeorological, GeoQuest


Medicine and Health Sciences | Social and Behavioral Sciences

Publication Details

Laubach, J., Bai, M., Pinares-Patino, C. S., Phillips, F. A., Naylor, T. A., Molano, G., Cardenas Rocha, E. A. &

Griffith, D. W. T. (2013). Accuracy of micrometeorological techniques for detecting a change in methane emissions from a herd of cattle. Agricultural and Forest Meteorology, 176 50-63.



Johannes Laubach, Mei Bai, Cesar S. Pinares-Patino, Frances A. Phillips, Travis A. Naylor, German Molano, Edgar A. Cardenas Rocha, and David W. T Griffith


Accuracy of micrometeorological techniques for detecting a change in methane emissions from a herd of cattle

Johannes LaubachA,*, Mei BaiB,**, Cesar S. Pinares-PatiñoC, Frances A. PhillipsB, Travis A. NaylorB, German MolanoC, Edgar A. Cárdenas RochaC,D, David W. T.


ALandcare Research, P.O. Box 40, Lincoln 7640, New Zealand

BCentre for Atmospheric Chemistry, University of Wollongong, Wollongong NSW 2522, Australia

CAgResearch, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand

DFacultad de Medicina Veterinaria y Zootecnia, Universidad Nacional de Colombia, Carrera 45 No. 26-85, Bogotá, Colombia

*Corresponding author:

Tel.: +64-3-321 9999 Fax: +64-3-321 9998

email: [email protected]

**present address: CSIRO Animal, Food & Health Sciences, Townsville QLD 4811, Australia

Submitted to Agricultural and Forest Meteorology 15 November 2012 Revised version, submitted 23 February 2013


Abstract 1

Micrometeorological techniques are effective in measuring methane (CH4) emission 2

rates at the herd scale, but their suitability as verification tools for emissions mitigation 3

depends on the uncertainty with which they can detect a treatment difference. An 4

experiment was designed to test for a range of techniques whether they could detect a 5

change in weekly mean emission rate from a herd of cattle, in response to a controlled 6

change in feed supply. The cattle were kept in an enclosure and fed pasture baleage, of 7

amounts increasing from one week to the next. Methane emission rates were measured 8

at the herd scale by the following techniques: 1) an external tracer-ratio technique, 9

releasing nitrous oxide (N2O) from canisters on the animals’ necks and measuring line- 10

averaged CH4 and N2O mole fractions with Fourier-transform infra-red (FTIR) 11

spectrometers deployed upwind and downwind of the cattle, 2) a mass-budget 12

technique using vertical profiles of wind speed and CH4 mole fraction, 3) a dispersion 13

model, applied separately to CH4 mole fraction data from the FTIR spectrometers, the 14

vertical profile, and a laser system measuring along four paths surrounding the 15

enclosure. For reference, enteric CH4 emissions were also measured at the animal scale 16

on a daily basis, using an enteric tracer-ratio technique (with SF6 as the tracer). The 17

animal-scale technique showed that mean CH4 emissions increased less than linearly 18

with increasing feed intake. The herd-scale techniques showed that the emission rates 19

followed a diurnal pattern, with the maximum about two hours after the feed was 20

offered. The herd-scale techniques could detect the weekly changes in emission levels, 21

except that the two vertical-profile techniques (mass-budget technique and dispersion 22

model applied to profile) failed to resolve the first step change. The weekly emission 23

rates from the external tracer-ratio technique and the dispersion model, applied to data 24

from either the two FTIR paths or the four laser paths, agreed within ±10 % with the 25

enteric tracer-ratio technique. By contrast, the two vertical-profile techniques gave 33 26

to 68 % higher weekly emission rates. It is shown with a sensitivity study that 27

systematically uneven animal distribution within the enclosure could explain some of 28

this discrepancy. Another cause for bias was the data yield of the vertical-profile 29

techniques being higher at day-time than at night-time, thus giving more weight to 30

times of larger emission rates. The techniques using line-averaged mole fractions were 31

less sensitive to the exact locations of emission sources and less prone to data loss 32

from unsuitable wind directions; these advantages outweighed the lack of a method to 33

calibrate CH4 mole fractions in situ.

34 35


Keywords: Cattle CH4 emissions, Gas dispersion, Atmospheric surface layer, Tracer-ratio 36

techniques, Mass-budget technique, Backward-Lagrangian stochastic model 37


1. Introduction


Methane (CH4) emissions from ruminant livestock constitute about 30 % of New 40

Zealand's and 12 % of Australia's greenhouse gas emissions. For any future practice or 41

technology to mitigate these emissions, it must be verified at the “herd scale” (or 42

“paddock scale”), under representative farming conditions, that the expected emissions 43

reduction is achieved. In New Zealand (NZ) and Australia, cattle and sheep are farmed 44

outdoors year-round. To measure CH4 emissions outdoors, micrometeorological 45

techniques are effective at the herd scale (Laubach et al., 2008). These can potentially 46

verify small changes in emission rates, provided that the uncertainty with which such 47

changes are detected is accurately known. Here, we report an experiment measuring 48

the emissions from a herd of beef cattle that was designed to specify this uncertainty 49

for a suite of micrometeorological techniques. These include: a mass-budget technique 50

using vertical profiles of wind speed and CH4 mole fraction ([CH4]), a backward- 51

Lagrangian stochastic (BLS) dispersion model using the same [CH4] profiles, the same 52

BLS model using line-averaged [CH4] data gathered with two types of instruments, 53

and an external tracer-ratio technique, releasing nitrous oxide (N2O) co-located with 54

the CH4 emission sources and measuring line-averaged N2O and CH4 mole fractions 55

upwind and downwind of the sources. The last technique was classified by Harper et 56

al. (2011) as “non-micrometeorogical”, since it does not require any meteorological 57

information to compute emission rates; however, as its feasibility relies on atmospheric 58

transport, we consider it more appropriate to include it among the 59

“micrometeorological” techniques. With these, it also shares the spatial and temporal 60

scales at which it operates. To obtain non-micrometeorological reference values of 61

CH4 emissions on a daily basis, an enteric tracer-ratio technique was employed, 62

commonly known as the SF6 tracer-ratio technique. This technique operates at the 63

“animal scale”, i.e. individual animals. Details and references for all techniques are 64

given in later sections.


Laubach et al. (2008) already reported an experiment at the same site, with 66

similar animals, and using the same techniques except for the external tracer-ratio 67

technique. In that experiment, the animals were freely grazing in rectangular strips that 68

were changed daily. This represented usual farming practice in NZ but had two 69


disadvantages: the instrumentation had to be moved frequently to be in suitable 70

locations for capturing the emissions, and the feed intake of the animals could not be 71

measured. Knowing the feed intake is desirable because it is a major factor 72

determining CH4 emissions. The “methane yield” Ym, defined as the ratio of CH4 73

emissions per dry-matter intake (DMI) where both variables are expressed in units of 74

combustion energy, is recommended for inventory purposes (IPCC, 2006; Lassey, 75

2007). To overcome the two disadvantages in the present experiment, the cattle were 76

held in a grass-free area and were fed known quantities of pasture baleage. The 77

experiment was run for three weeks. Within each week, daily feed rations were held 78

constant, then increased for the following week in order to produce a measurable step 79

change in the herd’s CH4 emissions. The objective was to test for each technique 80

whether it was possible to detect this step change on the basis of weekly averages, 81

which in turn required quantification of the uncertainty of these averages. Factors 82

determining this uncertainty are not only measurement accuracy, but also for each 83

technique its data yield, i.e. the number of runs meeting specific quality criteria, and its 84

sensitivity to the spatial distribution of sources (which is always assumed 85

homogeneous across a prescribed area, except for the external tracer-ratio technique, 86

where no such assumption is needed).


A side issue, inadvertently discovered from consistency checks between the 88

different CH4 instruments, is a strong temperature dependence of the “GasFinder” CH4 89

laser, previously unreported in the micrometeorological literature. This result is 90

presented and empirically corrected for in the main text; the causes are discussed in the 91


92 93 94

2. Experimental design


The experiment was conducted in November 2008. The site (40.336° S, 175.465° E) 96

was located on the Aorangi Research Farm, ca. 20 km inland from the west coast of 97

the North Island of NZ, near the city of Palmerston North. It is ideally suited for 98

micrometeorological techniques and tracer dispersion studies because the surrounding 99

terrain is flat for several km in all directions and there is a predominant wind direction, 100

from W (which includes frequent afternoon sea breezes when synoptic winds are 101

weak). The cattle were managed in two groups, of 30 and 31 animals, respectively, 102

with identical treatments as described in 2.2.




2.1 Site preparation and setup geometry 105

A paddock area of about 200 m by 200 m had been sprayed with herbicide prior to the 106

experiment, so that the ground was initially bare; by the end, a thin cover of herbage 107

had grown back. In the NW quarter of this area, a rectangle of 80 m by 55 m was 108

fenced to contain the cattle herd. A 7 m tall mast to collect vertical profiles of wind 109

speed and CH4 mole fraction was erected at the midpoint of the E boundary of the 110

fenced area, and with additional fencing of a semicircle with 20 m radius, the cattle 111

were kept at a minimum distance of 20 m from the mast. The nominal source area 112

(rectangle minus semicircle) thus covered 3772 m2. This area was subdivided into two 113

equal-size enclosures (Fig. 1) to allow handling of the cattle in two separate groups and 114

to limit clustering of the animals at feeding time.


The surrounding terrain consisted mainly of flat paddocks, with no significant 116

flow obstacles to the W, S and E for at least 500 m. To the N, there was a water ditch 117

dropping 2 m below ground level at 50 m distance from the profile mast, and a 118

shelterbelt at ca. 150 m distance. The bare ground extended ca. 100 m from the mast to 119

the W, S and E, and 45 m to the N.


Two types of line-averaging optical sensors (described below) were employed 121

(Fig. 1). One was a CH4-specific laser system with four paths that were mounted 122

outside the cattle area, with one path along each side of the rectangle, at 5 to 10 m 123

distance from the fence. Path lengths were between 53 and 57 m, measured with 0.1 m 124

accuracy, and path heights above ground were 1.85, 1.37, 1.38 and 1.07 m (±0.02) m 125

for the W, S, E and N path, respectively. The other instrument type was a Fourier- 126

transform infra-red (FTIR) spectrometer, measuring mole fractions of multiple gas 127

species simultaneously along an open path. Two identical FTIRs were set up parallel to 128

the W and E sides of the rectangle, at about 5 and 12 m distance to W and E, 129

respectively. The path lengths were 88.9 and 89.5 m and the heights above ground 130

were 1.39 (±0.05) m.


Wind direction, atmospheric stability and velocity statistics (required as inputs 132

for the dispersion model) were measured with a sonic anemometer (81000V, RM 133

Young, Traverse City, Michigan, USA), mounted on top of a telescope mast, 3.85 m 134

above ground and 13 m NNW of the profile mast.

135 136


2.2 Animals and feed 137

The experiment involved 61 one-year-old Friesian-Hereford crossbred steers. They 138

were fed perennial ryegrass/white clover baleage that had been prepared on-farm, with 139

an approximate content of white clover of 20 % (DM basis). The chemical 140

composition of the feed was determined by near-infrared spectroscopy.


Prior to the start of measurements, the animals were acclimatised to the 142

management conditions over a period of 10 d by gradually increasing the baleage 143

fraction in the diet while decreasing herbage allowance. At the same time, the animals 144

were accustomed to wear the gas collection gear required for the SF6 tracer-ratio 145

technique. Starting on the 6th day of acclimatisation, baleage constituted 100 % of the 146

diet. The cattle were fed at three increasing feeding levels (low, medium and high) 147

over three consecutive periods of one week duration each. The first three days of each 148

week were considered as adjustment periods to the new feeding level, and the last four 149

days were used to conduct CH4 emissions measurements with the animal-scale 150

technique. Feeding levels were set with the intention of making 1.0, 1.5, and 2.0 times 151

maintenance energy requirements available to the steers during Weeks 1, 2 and 3, 152

respectively. The maintenance energy requirement was estimated following CSIRO 153

(2007). In order to allow for increasing fractions of feed wastage with increasing 154

feeding level, the feed amounts offered were set to 1.10, 1.73 and 2.60 times 155

maintenance requirements. The animals were weighed at the start of the experiment 156

and at the end of each one-week feeding period. Drinking water was freely available to 157

them, from troughs placed centrally in each enclosure.


The feed bales weighed around 450 kg. Every morning at 09:00 h the feed was 159

distributed on the ground inside the two enclosures, using a forage mixer feed-out 160

wagon equipped with a scale. The accuracy of the wagon’s scale was calibrated against 161

a load cell at the start of the experiment and found to agree within ±10 kg for a bale. In 162

each enclosure, the feed was laid out approximately in a “B” shape, which maximised 163

the areal spread of the feed while avoiding that the feed-out wagon had to cross the 164

supply pipes to the water troughs (Fig. 1).


Estimation of feed intake on the basis of metabolisable energy algorithms was 166

not considered reliable, because the 7 d long feeding period at each level was too short 167

to accurately account for liveweight change due to the potentially overwhelming 168

influence of variable gut fill when the animals were weighed. Alternatively, feed 169

intake estimates were to be based on the difference between feed offered and refused.


However, refusals were trampled and mixed with faeces and soil, and attempts to 171


collect representative samples proved impractical. Consequently, the proportion of 172

refused feed was estimated daily by visual assessment.

173 174

2.3 Profile mast instrumentation 175

The vertical profile of [CH4] was measured with an analyser using off-axis integrated 176

cavity output spectroscopy (DLT-100, Los Gatos Research, Mountain View, 177

California, USA). Air was drawn continuously from seven intakes via tubes into 178

ballast volumes. A datalogger-controlled valve-switching system sequentially selected 179

each of the intake lines for sampling by the analyser. Each switching cycle lasted 180

20 min, allocating 171 s of sampling time to each intake, of which the first 140 s were 181

discarded as flushing time to purge the sample cell of air from the previous intake.


Five of the intakes were mounted on the mast, at heights of 0.62, 1.22, 2.24, 4.13 and 183

7.18 m (±0.02) m. The other two were placed to the W and SE away from the cattle 184

area (Fig. 1), at 1.94 and 1.77 m height, respectively, so that for any wind direction one 185

of them would be suitably located to provide the local background mole fraction 186

(upwind of the herd).


Wind speed, relative humidity (RH) and temperature were measured at the same 188

five heights as [CH4]: wind speed by cup anemometers (A101M, Vector Instruments, 189

Rhyl, Clwyd, UK) with matched calibrations, humidity by capacitive RH sensors 190

(MP100A, Rotronic, Bassersdorf, Switzerland) and temperature by thermocouples 191

inside the same aspirated double radiation shields as the RH sensors.

192 193

2.4 Open-path instrumentation 194

The four-path CH4 laser system (GasFinder MC, Boreal Laser, Edmonton, Alberta, 195

Canada) consists of a central control unit, four remote heads (Fig. 2a) and four retro- 196

reflectors. The central unit houses the laser source, of wavelength 1653 nm, a sealed 197

reference cell filled with a known amount of CH4, an optical multiplexer and the 198

controlling and processing electronics. The wavelength is modulated across a narrow 199

band in order to sample the shape of the absorption line. The IR light is ducted via 200

fibre-optical cables to the remote heads, switching between them in a cycle of ca. 18 s 201

duration. The head emits the light along the open path towards the retro-reflector and 202

collects the returning light on a photodiode. The electrical output signal of the 203

photodiode is transmitted back to the central unit via a coaxial cable. There, the light 204


intensity measurements, as scanned across the waveband, are converted to CH4 mole 205

fractions with a regression algorithm that compares the measured line shape to that of 206

the reference cell (Boreal Laser, 2005). These mole fraction data were collected on a 207

laptop computer and subjected to post-processing to remove dubious data (guided by 208

quality flags and light level data provided by the instrument), apply correction factors 209

for signal dampening along the coaxial cable length, and form 20-min averages aligned 210

with the switching cycles of the vertical profile system.


The two identical open-path multi-gas FTIR spectrometer systems were 212

constructed at the University of Wollongong (Bai, 2010). The spectrometer is a 213

Matrix-M IRcube (Bruker Optik, Ettlingen, Germany). Light from an air-cooled SiC 214

infrared source is modulated by the interferometer and passes via a beam splitter (ZnSe 215

window) into a modified 10” Schmidt-Cassegrain telescope (LX200R, Meade 216

Instrument Corp., Irvine, California, USA). From there, the primary beam is 217

transmitted across the measurement path, returned by a 0.3 m diameter retro-reflector 218

array (PLX, Deer Park, New York, USA) and received by a HgCdTe detector (Infrared 219

Associates, Stuart, Florida, USA) which sits on the same tripod-mounted optical bench 220

as the source, interferometer and telescope (Fig. 2b). A Zener-diode thermometer (type 221

LM335) and a barometer (PTB110, Vaisala, Helsinki, Finland) are integrated in the 222

source/detector array to provide real-time air temperature and pressure data for the 223

analysis of the measured spectra. The spectrometer is controlled by software developed 224

at the University of Wollongong. The interferometer performs about 80 scans min−1 225

across the waveband of interest (700 – 5000 cm−1), which were in this experiment 226

averaged to 2-min spectra. From the spectra, the mole fractions of CH4, N2O, CO2, CO 227

and H2O are retrieved by a non-linear optimisation algorithm (MALT software), 228

developed by Griffith (1996). The algorithm uses absorption line strengths of these 229

gases as listed in the HITRAN database (Rothman et al., 2005) and constructs the 230

combination of mole fractions that provides the best match between the transmission 231

spectrum expected from this combination and the observed spectrum (Smith et al., 232

2011; Griffith et al., 2012). Only the CH4 and N2O mole fractions are used in this 233


234 235 236


3. Techniques to determine CH


emission rates


The various techniques to determine the CH4 emission rates are listed in Table 1. The 238

first technique (Section 3.1) was applied to individual animals, providing daily 239

averages. The others (Sections 3.2 to 3.4) all integrated over emissions from the herd, 240

as the emitted CH4 was transported and dispersed by the wind. They were applied 241

using 20-min averages of the CH4 mole fraction, relative to moist air, and of the 242

meteorological variables.

243 244

3.1 Enteric tracer-ratio technique (SF6) 245

The SF6 tracer-ratio technique (Johnson et al., 1994) was employed to estimate CH4


emissions from individual animals. For this purpose, on the first day of the pre-trial 247

acclimatisation period of 10 d, individually calibrated brass permeation tubes 248

containing the SF6 tracer were dosed per os into the reticulo-rumen of each 249

participating animal. For the last four days of each feeding level period, daily breath 250

and background air samples were collected from each steer, using a yoke fitted around 251

his neck (Fig. 3). Although the animals were out of the paddock for 2 to 3 h every 252

morning (time spent in transit to and from the management yards and for handling), 253

changing the sampling yoke took only about 1 min per animal, thus the collection 254

period was effectively 24 h. Samples were transported to the laboratory for analysis of 255

SF6 and CH4 mole fractions using a gas chromatograph (GC-2010, Shimadzu, Kyoto, 256

Japan), as described by Pinares-Patiño et al. (2007). Daily CH4 emissions from 257

individual animals were calculated from the ratio of mole fraction elevations of CH4


and SF6 (above the respective background-air mole fractions) and the known 259

permeation rate of SF6 from the permeation tubes (Johnson et al., 1994).

260 261

3.2 External tracer-ratio technique (N2O) 262

The basic approach of the N2O tracer-ratio technique is the same as for the SF6


technique: a tracer gas is released at a known rate co-located with the CH4 emission 264

from the animals, the mole fractions of both gases are measured post-emission in the 265

same volume, and then the ratio of the two mole fractions (after subtracting 266

atmospheric background from each) is equated with the ratio of their emission rates.


The main difference is that with the N2O technique the gases are not collected in a 268

container, but measured in situ, some distance downwind, by the open-path FTIR. The 269

path length is long enough to capture the emissions from many, if not all, animals 270


simultaneously, which makes the technique effective at the herd scale. Along with this 271

spatial integration comes high temporal resolution, not achievable with the animal- 272

scale technique.


Nitrous oxide was chosen because it is inert, non-toxic, commercially available 274

and its spectrum documented in HITRAN, which makes it easily measurable by the 275

FTIR in the same wavebands as CH4. Also, it could be released at rates much larger 276

than from any environmental sources (soil and animal excreta), so that the measured 277

downwind-upwind [N2O] differences were clearly attributable to the manufactured 278

release. In a previous experiment, Griffith et al. (2008) released the N2O from a 279

perforated pipe along the upwind fence line. Here, the release location was much more 280

closely matched to that of the CH4 by using pressurised canisters carried by the 281

animals near their mouths (Fig 3). These were 12 oz paintball gas canisters (Catalina 282

cylinders, Garden Grove, California, USA), which were filled with ca. 0.3 kg liquid 283

compressed N2O and fitted to the animals on a daily schedule, along with the SF6 284

collection yokes to which they were attached. Due to the long preparation time 285

required to fill the N2O canisters, only half of the cattle (15 in each group) were 286

equipped with them. This was unlikely to introduce additional error because animals 287

with and without release canister distributed themselves randomly across the same 288

area. Each canister was fitted with a tap and restricting orifice to regulate the release 289

rate to about 10 g h−1. The release rate’s time average was determined for each canister 290

by weighing it, before and after it was carried by the animal, and the release rate’s 291

temporal evolution was constructed by taking into account its systematic decrease with 292

canister pressure as well as its temperature dependence. Technical details of the 293

canister filling procedure and the computation of the N2O release rate are given in Bai 294

(2010). The CH4 emission rate was then calculated from the total N2O release rate and 295

the path-integrated CH4 and N2O mole fractions measured downwind from the herd.

296 297

3.3 Micrometeorological mass-budget technique 298

The mass-budget technique, sometimes also named integrated horizontal-flux 299

technique, was first applied to animal emissions by Harper et al. (1999) and Leuning et 300

al. (1999). Here, it was implemented using the vertical 5-point profiles of CH4 mole 301

fraction, measured with the Los Gatos analyser, and of wind speed, measured with the 302

cup anemometers. From these profiles and the background mole fraction, measured 303

upwind of the cattle, the integrated horizontal flux of CH4 was computed. To convert 304


this flux to an areal emission rate, it was divided by the distance across the cattle area, 305

from the profile mast in the upwind direction. Corrections for cross-wind variation of 306

this distance, for horizontal flux contributions above the top measurement height, and 307

for turbulent backflow were applied as described in Laubach and Kelliher (2004).

308 309

3.4 BLS technique 310

The principle of the backwards-Lagrangian stochastic (BLS) technique is to employ a 311

dispersion model to simulate trajectories of air parcels backwards in time from a 312

location where a gas concentration was measured, and to analyse statistically where 313

these trajectories intersect with a source volume (or area) in which the concentration of 314

the simulated air parcels would have been altered. For a given flow field (mean wind, 315

direction, and certain turbulent parameters), this trajectory-mapping delivers an 316

unambiguous linear relationship between measured concentration and emission rate.


The BLS model used here, developed by Flesch et al. (1995; 2004), is distributed 318

as a user-friendly software (WindTrax 2.0, www.thunderbeachscientific.com). The 319

model can accommodate concentration measurements at a point, such as realised by an 320

intake to a gas analyser, or along a line, such as the path of an optical sensor measuring 321

absorption. It is here applied separately to the three types of CH4-measuring 322

instruments: the closed-path analyser (vertical profile), the open-path laser system 323

(4 paths) and the open-path FTIR instruments (2 paths). While the three simulations 324

differ in their concentration input data, they assume identical CH4 source distributions 325

and identical flow fields. The CH4 source is defined as a ground-level area source with 326

the dimensions of the cattle enclosure. The flow field is specified, as in Laubach 327

(2010), by wind speed from the highest cup anemometer, the stability parameter and 328

the standard deviations of the three wind components from the sonic anemometer (at 329

3.85 m), and roughness length. Roughness length was not allowed to vary randomly 330

from run to run; rather, its evolution for the whole experiment was determined as a 331

function of time and wind direction. Over time, the roughness length increased slowly 332

as vegetation grew back, from 0.8 cm (bare soil) to 1.7 cm. The directional analysis 333

showed systematically larger values from the N sector than from the other directions, 334

consistent with the presence of ditch and shelterbelt to the N. The directional 335

dependence was fitted with a squared cosine function for directions within ±90 ° from 336

N, added to the time-dependent roughness length for the other sectors. The maximum 337

roughness length obtained for N winds was 9.1 cm.



339 340

4. Calibration checks and corrections to CH


mole fractions


4.1 Closed-path analyser 342

Every 6 h, sampling of vertical [CH4] profiles with the closed-path analyser (Los 343

Gatos) was interrupted for 20 min to perform an automated calibration check, using 344

two gas cylinders that provided air with near-zero and near-ambient CH4 mole 345

fractions. These checks showed that throughout the campaign, [CH4] was reproducible 346

within ±3 ppb standard deviation (±0.18 % of ambient). Testing for temperature 347

dependence, a linear regression slope of 0.07 ppb K−1 was found, which is a factor 12 348

smaller than reported by Tuzson et al. (2010) for the same type of instrument. With 349

R2 = 0.03, this temperature dependence was not significantly different from zero and 350

thus neglected. Instead, the observed differences between subsequent calibration 351

checks were used to correct the mole fraction data in post-processing, assuming a 352

linear drift in time. Within a 20-min run, this drift correction had negligible effect on 353

mole fraction differences between intakes, but it provided minor adjustments to the 354

diurnal courses.

355 356

4.2 Open-path FTIR instruments 357

For the open-path instruments, in-situ calibration checks were not possible. Instead, a 358

series of consistency checks was conducted, first between paths of the same system, 359

then between these and the closed-path instrument. For these checks, only the periods 360

of cattle absence were used, expecting that then all instruments would effectively 361

sample the local background CH4 mole fraction – not necessarily identical to 362

hemispheric background, but influenced only by sources (or sinks) far enough away 363

that the position differences between the compared instruments did not matter.


As the periods of cattle absence were in the morning, strongly stable 365

stratification from the residual nocturnal boundary could occur on some days, with 366

elevated CH4 concentrations that potentially showed a vertical structure. To be able to 367

identify the influence of stable versus well-mixed conditions, the [CH4] data from the 368

two FTIR instruments are compared by plotting their ratio as a function of the wind 369

speed at 7.18 m (Fig. 4). As a change in alignment can change the instrument 370

calibration, the data are split into two groups, before and after a realignment of the W 371


path that was necessary following disturbance to the instrument while diagnosing an 372

electronic failure. Before the realignment, the ratio was consistently near 1, except for 373

some runs of low wind speed which are considered as stably stratified and not well- 374

mixed. The mean (±SD) for wind speed > 2 m s−1 was 1.007 (±0.009, n = 67). After 375

the realignment, the ratio was 1.034 (±0.006, n = 30). The SD values indicate a random 376

error of order 5 to 10 ppb at ambient [CH4]. This estimate would include an error 377

contribution from the horizontal distance between the instruments; it is thus 378

compatible with several independent instrument precision checks that yielded 379

estimates between 2 and 4 ppb (Bai, 2010).


Similar tests were done for the N2O mole fractions, required for the external 381

tracer-ratio technique (not shown). These yielded W/E ratios of 1.02 (±0.01) before 382

and 1.03 (±0.01) after realignment, respectively. The SD values indicate a random 383

error of 2 ppb at ambient [N2O], which again – due to the error from the horizontal 384

separation – is compatible with independent precision checks, yielding 0.4 ppb (Bai, 385



To compare the two FTIR to the closed-path analyser, [CH4] from each of the 387

former was divided by [CH4] from the nearest intake of the latter, selecting the same 388

cattle-free periods as before. The nearest path for the W path FTIR was the background 389

intake at 1.94 m height, and for the E path, the second-lowest intake from the profile 390

mast, at 1.22 m height. Since the open-path and closed-path data differ in three 391

respects: height, averaging volume (“line” vs. “point”), and sampling duration (shorter 392

for closed-path due to the switching cycle), one should not expect perfect agreement.


However the ratios showed remarkable consistency, at about 0.97 for the E path and 394

about 0.98 and 1.01 for the W path before and after the realignment, respectively 395

(±0.01 for each ratio). The ratios were independent of temperature and humidity. Any 396

ratios less than 0.94 were associated with wind speed < 2 m s−1. This indicates that the 397

absolute mole fractions of the two FTIR instruments were 2 to 3 % too low, equivalent 398

to an absolute error of 40 to 60 ppb at background, and they did not drift.


After these checks, the mole fractions from the W path were corrected against 400

those from the E path, by factors determined as the slopes of period-wise linear 401

regressions (of runs without cattle presence). This ensured matched calibrations 402

between the two FTIRs, which are crucial for accurate determination of upwind- 403

downwind differences, and in turn, emission rates. The FTIR data were not corrected 404

against the closed-path data. Thus, it is expected that the 3 % difference between the E 405


FTIR and the closed-path analyser carries through to the emission rate computations 406

with the BLS technique.

407 408

4.3 Four-path laser system 409

For the open-path laser (GasFinder MC), the same consistency checks were carried out 410

as for the FTIR. In Fig. 5, consistency between paths is assessed, by showing the ratios 411

of [CH4] from the W, S and N path, respectively, to [CH4] from the E path, against 412

wind speed. For each ratio, the spread is considerably larger than for the W/E FTIR 413

ratio in Fig. 4, and wind speed does not affect the spread, indicating that the variability 414

reflects instrument precision, not true [CH4] variations. The means (±SD), for 126 runs 415

of cattle absence, were 1.015 (±0.039) for W/E, 0.963 (±0.038) for S/E, and 416

1.033 (±0.041) for N/E, respectively. The SD values indicate a random error of order 417

48 ppb, at ambient [CH4], for each path. This is compatible with the manufacturer’s 418

specification of 2 ppm m precision for the path-integrated mole fraction, equivalent to 419

36 ppb for a 55 m long path. The laser system is thus one magnitude less precise than 420

the FTIR instruments and the closed-path analyser.


In Fig. 5, the differences of the mean ratios from 1 cannot be explained by height 422

differences (the W path was the highest, N the lowest, and S and E were at equal 423

height). Like for the FTIR, the E path was selected as the reference path, and the [CH4] 424

data from the other three paths were corrected against it. This was done separately for 425

the three feeding-level periods, to ensure the [CH4] differences between paths were 426

bias-free within each such period.


As for the FTIR, mole fractions from the W and E paths of the laser system were 428

compared to those from the nearest intakes of the closed-path analyser. Both horizontal 429

and vertical distances between path and intake location were smaller than for the FTIR.


Therefore, it was considered acceptable to include runs with and without cattle 431

presence in the comparison. To ensure sufficient atmospheric mixing, only runs with 432

wind speed > 2 m s−1 were selected. The path/intake [CH4] ratios reveal a significant 433

temperature dependence (Fig. 6), with a slope of −9.4 x 10−3 K−1 for both. The ratio 434

equals 1 at 23 (±2) °C (equal to the reference temperature, 296 K, of the HITRAN 435

database – see Appendix), while at 0 °C the open-path laser would overestimate [CH4] 436

by about 20 %. It was further tested whether the [CH4] ratios depended on specific 437

humidity or pressure, with negative result. This test was repeated with the data binned 438

into 3 K wide temperature classes, to prevent that the temperature dependence would 439


overwhelm any trends with the other variables, but no dependence on humidity or 440

pressure could be identified. By contrast, when the data were binned into five classes 441

of specific humidity, or pressure, then within each class there was still a temperature 442

dependence, represented by a slope that agreed within ±30 % with that in Fig. 6. For 443

all subsequent analyses, the [CH4] data from the laser system are therefore linearly 444

corrected for temperature, using the slope from Fig. 6 as the adjustment factor.


To our knowledge, such a temperature dependence of the GasFinder has not been 446

reported in the micrometeorological literature before. Possible physical causes are 447

discussed in the Appendix.

448 449 450

5. Results and Discussion


5.1 Animal behaviour 452

On the majority of mornings, the cattle were taken off-site between 7:15 and 8:00 h, to 453

have their gas collection yokes changed. During their absence, the feed was laid out in 454

the enclosures. When the cattle returned, between 10:00 and 10:30 h, they began 455

feeding immediately, and used up the available rations within a few hours. The rest of 456

the day they spent mostly drinking, resting and ruminating, moving around less than 457

grazing animals typically would. It was clear from observations, of the cattle 458

themselves as well as the distribution of their excreta, that they stayed preferably in the 459

W parts of the enclosures, frequently bunching near the W fence line (and 460

occasionally, around the water troughs). This behaviour was very different from the 461

freely-grazing behaviour observed by Laubach and Kelliher (2004; 2005b) and 462

Laubach et al. (2008), when the paddock area was by and large evenly covered by the 463

cattle, for most of the time. The uneven spatial animal distribution in the present 464

experiment had implications for the emission rate determinations. These are discussed 465

in Section 5.5.

466 467

5.2 Feed intake and emission rates at the animal scale 468

On average, the baleage contained 399 (±26) g dry matter (DM) per kg wet weight. On 469

a DM basis, it contained 46.5 (±5.5) % neutral detergent fibre, 19.0 (±3.4) % crude 470

protein, 9.4 (±2.3) % soluble sugars, 3.3 (±0.5) % lipids and 13.8 (±3.0) % ash. The 471

estimated metabolisable energy content of the feed was 10.5 (±0.4) MJ (kg DM)−1. 472


The average liveweight (LW) of the cattle increased with level of feeding from 473

331 kg at low level to 352 kg at the high level (Table 2). On a DM basis, feed on offer 474

increased by 58 % from the low to the medium level of feeding and by 52 % from the 475

medium to the high level. Visual assessment of feed refused showed negligible 476

amounts (< 1 %) for Weeks 1 and 2. In Week 3, the soiled feed residues were 477

considerably larger and estimated at 10 %. The resulting estimates of dry matter intake 478

(DMI) were 4.3, 6.7 and 9.3 kg DM d−1 animal−1 for the low, medium and high feeding 479

level, respectively (Table 2), and hence the relative increases from one feeding level to 480

the next were 58 and 38 %, respectively.


The CH4 emission rates were obtained with the SF6 tracer-ratio technique, for the 482

last 4 d of each week. The first 3 d were considered as an adjustment period to the new 483

feeding level (as diet composition did not change and feeding level increased, rather 484

than decreased, a period of 3 d sufficed). The average emission rates were 485

70.8 (±13.5), 89.7 (±11.1) and 119.1 (±16.4) g CH4 d−1 animal−1 for Weeks 1, 2 and 3, 486

respectively (Table 2), where the numbers are means (± SD) of the 61 animals. Even in 487

Week 3, when DMI was relatively generous, the CH4 emission rate was low in 488

comparison to those observed previously for freely-grazing steers at the same location 489

(Laubach et al., 2008). The standard deviations in Table 2 convert to standard errors of 490

the mean ≤ 2 g CH4 d−1 animal−1. We assume the weekly mean CH4 emission rates to 491

be “true” within this limit and use them as reference values for the other techniques.


The accuracy of the SF6 technique for estimating mean emissions of CH4 has been 493

proved by comparison to animal chamber measurements, both for sheep (Hammond et 494

al., 2009) and cattle (Grainger et al., 2007).


The CH4 emission rate increased with increase in feeding level, by 27 % from 496

the low to the medium feeding level and by 33 % from medium to high. Similarly, the 497

estimates of CH4 emissions expressed per unit of LW increased with increasing 498

feeding levels, by 24% between low and medium feeding levels and by 28% between 499

medium and high feeding levels. These increases were significant (P < 0.01), but less 500

than a proportional increase with DMI. Hence, the estimated CH4 yield per unit of feed 501

intake decreased, by 19 % from the low to the medium feeding level and by 4 % from 502

medium to high (Table 2). Decreasing CH4 yields at increasing feeding levels were 503

already reported by Blaxter and Clapperton (1965), and have been attributed to a faster 504

rate of passage of feed through the alimentary tract (Benchaar et al., 2001; Pinares- 505

Patiño et al., 2003).



The CH4 emissions per feed intake are unusually low, compared to the mean for 507

ryegrass-fed cattle in NZ, of 19.1 (±3.70) g CH4 (kg DMI)−1 (Hammond et al., 2009).


The observed CH4 yields, of 4.6, 3.7 and 3.6 % of gross energy intake (GEI), are closer 509

to the IPCC default value for concentrate-fed cattle, 3.0 %, than to the default value for 510

grazing cattle, 6.5 % (IPCC, 2006, p. 10-30). This is likely to be due to the baleage 511

used in this study, in contrast to fresh grass being the usual cattle diet in New Zealand.


It is well established (McDonald et al., 1991; Huhtanen and Jaakkola, 1993; González 513

et al., 2007) that ensiling reduces the ruminal degradability of dry matter. A computer 514

simulation by Benchaar et al. (2001) took this effect into account, and also that CH4 515

production decreases as pH and acetate/propionate ratio decrease. It predicted that CH4 516

emission from feeding lucerne silage would be lower than that from hay (3.7 vs. 5.4 % 517

of GEI). In light of this result, the observed CH4 yields appear plausible. Yet, a deeper 518

discussion of this finding is beyond the scope of this study.

519 520

5.3 Emission rates at the herd scale 521

For any of the herd-scale techniques, the run-to-run variations of the obtained CH4 522

emission rate, Qc, were considerable. This was the compounded effect of several 523

sources of variability: true emission rate changes in response to the animals’ digestion 524

processes, variability of source locations relative to the instruments as the animals 525

moved around, influence of variations in wind speed and direction on the effective 526

footprint of the various techniques, influence of wind speed and direction on the 527

magnitude of the concentration differences to be resolved, and random instrument 528

error (precision). Data availability also differed between techniques. This was partly 529

due to scheduled calibration checks and occasional malfunctions, e.g. dew on optical 530

components, failure of electronic components (resolved by replacement). In addition, 531

wind direction affected availability of the different techniques in different ways. The 532

techniques involving the vertical profile mast (mass-budget and BLS-profile) required 533

the mast to be downwind of at least a few steers, so gave meaningful emission rates 534

only for directions between 210 and 330 °. The techniques involving the two FTIR 535

instruments required one of them to be up- and the other downwind, so data were 536

accepted only if wind direction was within ±50 ° of either W or E. By contrast, the 537

four-path laser system could be used for all wind directions. Periods of low wind speed 538

were excluded for all techniques, when friction velocity was less than 0.1 m s−1. 539


Given the different operational constraints for each technique, and the overall 540

goal to assess the suitability of each to detect a change between weekly emission rates, 541

the results were first compared on the basis of weekly diurnal courses. To construct 542

these, for each week and technique the 20-min emission rates were sorted by time of 543

day, into 1-h wide bins, and then bin-averaged. The results are discussed in the 544

following subsections.

545 546

5.3.1 Diurnal pattern of emissions 547

Despite obvious discrepancies between techniques, a few features of the emission 548

pattern appear robust (Fig. 7). From about 10:00 h, when the cattle entered their 549

enclosures, Qc increased steeply until it reached a maximum typically around 13:00 h.


In Weeks 1 and 2, Qc began declining after that, according to all techniques, while in 551

Week 3 the techniques disagreed as to whether Qc further increased or decreased until 552

18:00 h. This pattern suggests that the maximum CH4 emissions typically occurred 553

within 2 h of the time of maximum feeding activity. Feeding always started soon after 554

10:00 h and in Weeks 1 and 2 was practically finished by noon, while it was stretched 555

out longer in Week 3, when food amounts on offer exceeded the animals’


requirements. Similar phase relationships between feeding time and maximum- 557

emission time were observed for feedlot cattle (Loh et al., 2008; Gao et al., 2011) and 558

sheep in test chambers (Lassey et al., 2011). From late afternoon throughout the night, 559

Qc generally decreased, reaching the lowest levels from about 7:00 to 9:00 h, before 560

the new day’s feed ration was offered.


With all herd-scale techniques, Qc from 10:00 to about 22:00 h (±3 h) was 562

usually larger than the daily average obtained from the SF6 tracer-ratio technique, and 563

during the other half of the diurnal cycle, Qc was smaller than the average. This 564

reflected that most of the feeding and ruminating occurred during the daylight hours.


Similar activity patterns are common for free-ranging animals.

566 567

5.3.2 Profile-based techniques 568

The mass-budget and BLS technique that used the vertical [CH4] profile from the 569

closed-path analyser detected virtually the same temporal pattern of emission rates 570

(Fig. 7). On a run-to-run basis, the two techniques agreed typically within 10 %, as 571

was found in other experiments (Laubach and Kelliher, 2005a; Gao et al., 2009;


Laubach, 2010).



From one week to the next, the daytime observations (10:00 to 18:00 h) clearly 574

showed increasing Qc. For the night-time hours, this cannot be stated. On many days 575

the wind direction turned from W in the afternoon, via N in the evening, to NE at 576

night, and then wind speed usually dropped, with friction velocity falling below the 577

acceptance threshold. Since the profile-based techniques relied on westerly wind 578

directions, night-time data availability was poor, and the available data suffered from 579

low replication rates. This is particularly evident in Week 1 (Fig. 7, left panel), where 580

lack of points for some hours, and a lack of error bars for others, indicated that none or 581

only one run per bin, respectively, was available.


Consistently for all three weeks, and almost all times of day, these techniques 583

gave higher Qc than the other techniques. The likely cause for this is that for most of 584

the time, the animals were not spread evenly across the enclosures. Rather, they 585

preferred to stay near the W fence line. Consequently, for the wind directions most 586

frequent and best-suited for the profile-based techniques, the actual animal density in 587

the effective source area tended to be larger than the nominal mean animal density 588

across the enclosures, which was used for converting emission rates per area to 589

emission rates per animal. Hence, the latter were frequently overestimated. This point 590

is further elaborated in Section 5.5.

591 592

5.3.3 BLS with open-path instruments 593

During the nights of Week 1, the laser system showed the highest data availability of 594

all herd-scale techniques, while no data were available from the FTIR either due to 595

unsuitable wind direction or, during one night, failure of the instrument W of the 596

cattle. During day-time in Week 1, and throughout Weeks 2 and 3, there was generally 597

reasonable agreement in the temporal pattern of Qc retrieved by BLS from the FTIR 598

and from the laser system. The laser system gave larger variability throughout, 599

indicated both by the error bars in Fig. 7 and the variations from hour to hour. This 600

may appear counter-intuitive, given that the laser system consisted of four paths almost 601

completely surrounding the emission sources, so it should have provided the most 602

representative coverage of the emissions plume for any wind direction. However, the 603

more erratic behaviour of Qc from the laser system can be explained by its poorer 604

measurement precision.


The BLS results from both open-path systems showed an increase of Qc from 606

one week to the next. Each week, the hourly averages from the laser system were 607


spread roughly from 0.3 times to 1.7 times the average Qc from the SF6 tracer-ratio 608

technique. The corresponding FTIR data spanned roughly between night-time minima 609

(when available) of 0.5 times Qc and day-time maxima of 1.5 times Qc from the SF6 610


611 612

5.3.4 External tracer-ratio technique 613

During Week 1, Qc from the N2O tracer-ratio technique agreed closely with that from 614

BLS using the same [CH4] input data from the FTIR (Fig. 7). During Week 2, the N2O 615

technique gave systematically lower Qc than BLS, and for many hourly bins the lowest 616

of all herd-scale techniques. The same is true at day-time in Week 3, but not at night.


In effect, this technique gave the smallest variations with time of day, tracking the 618

average Qc from the SF6 technique more closely than the other herd-scale techniques.


This can be explained by the fact that the tracer ratio is independent of the flow field 620

parameters, hence these parameters do not contribute to its error. The sampling errors 621

of the external tracer-ratio technique are those of the mole fractions and the mean N2O 622

release rate, while for BLS they are those of the mole fractions and a number of wind 623

and turbulence parameters.

624 625

5.4 Weekly mean emission rates 626

To obtain mean CH4 emission rates for each feeding level, with each technique, the 627

mean diurnal courses were averaged. This gave more representative estimates than 628

simply averaging all available runs, which would have weighted day-time more 629

strongly than night-time (because of the more frequent occurrence of unsuitable calm 630

periods at night). In some instances, especially in Week 1, some night-time hours were 631

not covered by the data, which made some residual bias towards day-time inevitable.


Random errors of the weekly means were obtained by propagating the standard errors 633

of the mean for the hourly bins (root-mean-squares estimate). Fig. 8 displays the 634

results, discussed in the following two subsections.

635 636

5.4.1 Comparison of absolute mean emission rates 637

For all three weeks, the techniques using vertical profile data gave the largest mean 638

emission rates. In Week 1, these were 68 and 56 % higher than Qc from the SF6 tracer- 639

ratio technique, for mass-budget and BLS, respectively. In Weeks 2 and 3, they 640


exceeded the SF6 technique consistently by 35 (±2) %. As noted in 5.3.2, the likely 641

main cause for these biases was the systematically uneven animal distribution, 642

resulting in a discrepancy between the actual animal density (in the effective source 643

area of these techniques) and the nominal mean animal density. Another contributing 644

factor was probably the low data yield at night, which could have led to potentially 645

erratic results for some night-time hours. In Week 1, when several night-time hours 646

had no data coverage at all, that would also have led to bias in the diurnal average.


The BLS technique with the four-path laser gave weekly mean Qc that were 648

18 % lower than from the SF6 technique in Week 1, and 8 and 6 % higher (not 649

significantly different) in Weeks 2 and 3, respectively. The low average in Week 1 650

appears to be caused mostly by low hourly values between 13:00 and 15:00, which do 651

not fit with the diurnal patterns observed in the other weeks and by the other 652

techniques (Fig. 7). Some of the contributing runs with low Qc had unstable 653

stratification and SE to S winds; it is possible that the N laser path, at only 1.07 m 654

height, was too close to the ground to accurately sample the emissions plume in 655

convective conditions.


The BLS technique with FTIR data exceeded Qc from the SF6 technique by 25 657

and 21 % in Weeks 1 and 2, and agreed within < 1 % (no significant difference) in 658

Week 3. The N2O tracer-ratio technique exceeded Qc from the SF6 technique by 18 % 659

in Week 1, by 3 % (not significant) in Week 2, and fell short of it by 10 % in Week 3 660

(different at the 95 % confidence level). For both techniques, the overestimate in 661

Week 1 is explained by the lack of night-time data, causing the weekly mean to over- 662

represent the times of feeding activity. For the BLS technique in Week 2, there is no 663

obvious cause for an overestimate. Valid FTIR data were obtained for either W or E 664

winds; the fraction of W winds was 90, 80 and 74 % for Week 1, 2 and 3, respectively, 665

so any effects related to wind direction are unlikely to explain differences between 666

weeks in the BLS technique’s performance. For the N2O tracer-ratio technique in 667

Week 3, it can be seen in Fig. 7 that the day-time emission rates were substantially 668

lower than those recorded by the other techniques. A possible cause for low emission 669

rate estimates is an underestimate of the N2O release rate, obtained from weighing of 670

the canisters and using the time and temperature dependence determined by Bai 671

(2010). However, the release rates during Week 3 showed very consistent diurnal 672

courses. It thus remains unclear whether the N2O tracer-ratio technique underestimated 673

the day-time emissions for this week. If it did, that would have caused the weekly 674

average to be an underestimate.



With the exceptions as discussed, it appears that all techniques using open-path 676

[CH4] measurements delivered weekly mean Qc that agreed with the enteric tracer- 677

ratio technique within 10 %, which is the commonly assumed magnitude of 678

uncertainty for micrometeorological flux measurement techniques. It should be noted 679

that the herd-scale techniques include CH4 emissions from the rectum, while the SF6 680

tracer-ratio technique does not. Comparisons of the SF6 technique to animal-chamber 681

measurements by McGinn et al. (2006) and Grainger et al. (2007) indicated lower 682

emission estimates for the SF6 technique of 4 % and 6 %, respectively, which could in 683

part be attributed to rectal emissions missed by this technique. Accounting for a small 684

underestimate by the SF6 technique would not change the finding that, for weekly 685

averages, the three open-path techniques agreed well with it. For the two vertical- 686

profile techniques, the differences to the SF6 technique would be somewhat reduced.

687 688

5.4.2 Suitability to resolve a change in mean emission rate 689

According to the SF6 tracer-ratio technique, the mean CH4 emission rate increased by 690

27 % from Week 1 to 2, and by 33 % from Week 2 to 3. The mass-budget technique 691

failed to detect the first change, but identified an increase of the right magnitude 692

(34 %) for the second. The BLS technique using vertical profile data recorded CH4


emission increases for both feed level changes, of 10 % and 34 % respectively, but the 694

change from Week 1 to 2 was not significant. For both these techniques, the failure to 695

detect the first step change must be attributed to sparse data coverage and large 696

variability during night-time hours, since Fig. 7 shows that the day-time emission rates 697

increased for both techniques.


The other techniques (N2O tracer-ratio and BLS with FTIR or four-path laser) 699

detected both changes in weekly emission rates at > 99 % confidence levels. The N2O 700

tracer-ratio technique recorded the lowest week-to-week increases of these techniques, 701

of 11 and 16 %, respectively, which were just under half of the increases shown by the 702

SF6 technique. For the first step change, this can be explained by the overestimate in 703

Week 1 due to a lack of night-time data. For the second step change, this is 704

computationally caused by low day-time emission rates in Week 3, yet the ultimate 705

cause for these is unclear.


Since the 95 % confidence intervals are about twice and 99 % confidence 707

intervals roughly 3 times as large as the standard errors in Fig. 8, one may infer that (in 708

all instances where data availability was sufficient) all techniques would probably 709


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