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A micrometeorological technique for detecting small differences in methane emissions from two groups of cattle
Atmospheric Environment 98 (2014) 599e606
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
A micrometeorological technique for detecting small differences in methane emissions from two groups of cattle ~ o b, 2, Johannes Laubach a, *, Samantha P.P. Grover a, 1, Cesar S. Pinares-Patin German Molano b a b
Landcare Research, PO Box 69040, Lincoln 7640, New Zealand AgResearch, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand
h i g h l i g h t s A new micrometeorological approach for treatment-control comparisons was developed. With this approach we compared methane emissions from two groups of grazing cattle. A relative group difference in emissions of order 10% was detected (P ¼ 0.01). This result was corroborated with a non-micrometeorological tracer-ratio technique.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 June 2014 Received in revised form 11 September 2014 Accepted 12 September 2014 Available online 16 September 2014
Potential approaches for reducing enteric methane (CH4) emissions from cattle will require verification of their efficacy at the paddock scale. We designed a micrometeorological approach to compare emissions from two groups of grazing cattle. The approach consists of measuring line-averaged CH4 mole fractions upwind and downwind of each group and using a backward-Lagrangian stochastic model to compute CH4 emission rates from the observed mole fractions, in combination with turbulence statistics measured by a sonic anemometer. With careful screening for suitable wind conditions, a difference of 10% in group emission rates could be detected. This result was corroborated by simultaneous measurements of daily CH4 emissions from each animal with the sulfur hexafluoride (SF6) tracer-ratio technique. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Backward-Lagrangian stochastic model Cattle CH4 emissions Gas dispersion Atmospheric surface layer Emissions mitigation
1. Introduction In countries with large numbers of ruminant livestock, methane (CH4) emissions from these animals contribute significantly to the total greenhouse gas emissions of these countries. For example, in 2012 enteric CH4 accounted for 31.5% of New Zealand's total greenhouse gas emissions (MfE, 2014). Micrometeorological techniques have contributed to determining emission rates of CH4 from grazing animals (Judd et al., 1999; Laubach and Kelliher, 2004, 2005; McGinn et al., 2011), feedlots (Loh et al., 2008; Gao et al.,
* Corresponding author. E-mail address: [email protected] (J. Laubach). 1 Present address: Department of Agricultural Sciences, La Trobe University, 5 Ring Rd, Bundoora, VIC 3086, Australia. 2 Present address: CSIRO Black Mountain Laboratories, Clunies Ross Street, Acton, ACT 2601, Australia. http://dx.doi.org/10.1016/j.atmosenv.2014.09.036 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
2011), manure heaps (Sommer et al., 2004), manure storage tanks (Park et al., 2010; VanderZaag et al., 2011), biodigesters (Flesch et al., 2011) and whole farms (Leytem et al., 2011; McGinn and Beauchemin, 2012) thereby providing valuable information upon which emission factors for national greenhouse gas inventories can be based. Merits and constraints of various micrometeorological techniques in this regard have been reviewed by Denmead (2008) and Harper et al. (2011). It is less well established what role these techniques could potentially play in verifying the efficacy of mitigation approaches. Then, it is not the absolute emission rate that is of primary interest; rather, it is differences in emission rates as a consequence of different treatments, management practices, or selections of animals that would need to be accurately determined. This task is commonly undertaken by emissions measurements at the ‘animal scale’, in controlled conditions such as in calorimetric chambers (e.g. McGinn et al., 2004). Such experiments can be designed to
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directly compare specific treatments, and thus appear most suitable to identify promising mitigation approaches. However, once proof of concept for a mitigation treatment has been achieved, it must still be verified at the ‘herd scale’, or ‘paddock scale’, under representative farming conditions, that the expected emissions reduction is achieved there, too. This is particularly challenging with animals grazing outdoors, as is widespread year-round practice e.g. in New Zealand and Australia. It was Pattey et al. (2006) who originally suggested that micrometeorological techniques could be designed for ‘treatment versus control’ experiments. McGinn et al. (2009) pioneered this idea, using the backward-Lagrangian stochastic (BLS) technique (Flesch et al., 1995) to detect a significant difference in CH4 emission rates between two groups of 10 cattle each that were fed diets differing in grain and forage ingredients. An overall emissions difference was not stated by the authors, but the data shown suggest that it was of the order of 20e30%. In that experiment, the groups of cattle on different diets were each enclosed in a rectangular pen and the pen surrounded by a four-path laser system to measure CH4 mole fractions. Each animal carried a Global Positioning System transmitter, so that the BLS flow model could be provided with accurate point-source locations in space and time. For the whole study period of 54 d, concurrent CH4 emission measurements on each animal were made with the SF6 tracer-ratio technique (Johnson et al., 1994), and used as a reference to assess the performance of the BLS technique. While this experiment provided a valuable proof of principle, it will only rarely be possible to devote such a large amount of effort and resources to a single treatmentcontrol comparison. Laubach et al. (2013) tried a different approach to test whether a number of different micrometeorological techniques were capable of detecting treatment effects. They measured CH4 emissions from a group of cattle that were provided a forage diet at amounts increasing from one week to the next. The micrometeorological techniques successfully detected changes in weekly CH4 emission rates of the order of 30%, in response to the feed intake changes. While, in targeted research trials with specific treatments, CH4 emission reductions of that magnitude have sometimes been observed, practical application of such treatments may only become available with many more years of research (Eckard et al., 2010). For the foreseeable future, practical mitigation steps may come from small improvements of farm management practices, e.g. with respect to feed intake, and are likely to have relatively small effects. To demonstrate that a certain management practice makes a detectable difference in CH4 emissions therefore poses a considerable measurement challenge. Tackling this challenge, the present study was aimed at improving the approach of McGinn et al. (2009) and applying it to grazing animals. The BLS technique was employed to measure CH4 emissions from two groups of cattle simultaneously, in a ‘treatment versus control’ set-up. The objective was to test whether a difference in mean group emissions of the order of 10% could be detected. The treatment to create a difference of this magnitude consisted in the spraying of oil onto the grass prior to grazing, since lipid additions to the diet are known to decrease the CH4 emissions of ruminants (Grainger and Beauchemin, 2011). One step to optimise the measurement technique was to use a single high-precision gas analyser to determine the emissions from both cattle groups, which removed the need for intercomparison of instruments. This idea was the same as in McMillan et al. (2014), who measured nitrous oxide emissions from a row of differentlyfertilised paddocks, but the design details were different. Here, perforated pipes were used to provide line-integrated air samples to the CH4 analyser. This was done because Laubach et al. (2013) found that approaches using line-averaged mole-fraction
measurements performed better than approaches using point mole-fraction measurements, due to both a larger range of admissible wind directions and smaller run-to-run variations in obtained emission rates. They suggested as ‘the ideal herd-scale technique one that combines the strengths of the accurate closedpath analyser with the strengths of a path-averaging approach’. The study reported here realised this idea. The intake pipes were symmetrically arranged upwind and downwind of the cattle groups, in order to obtain group emission rates as well as their difference. The combination of the BLS technique with perforated intake pipes was first employed by Loh et al. (2009). Their objective was to detect CO2 and CH4 escaping from underground storage. Using the same approach to detect emission differences between two differently-treated groups of animals is a novel application. 2. Materials and methods 2.1. Experimental design, animals, and treatments The experiment was conducted at Aorangi Research Farm (40.336 S, 175.465 E), near Palmerston North, New Zealand, from 27 September to 14 October 2011. The site is ideally suited for micrometeorological techniques because the surrounding terrain is flat for several kilometres in all directions. Permanent fence lines on the farm are approximately aligned with the main compass directions. Temporary fences for this experiment were lined up parallel to the former, and references in the following to the compass directions are relative to the ‘farm-North’ defined by the main fences. Two groups of 30 cattle each were selected, with equal mean liveweight. The cattle were one-year-old Hereford Friesian steers. In a flat uniform paddock dominated by ryegrass (Lolium perenne), 32 rectangular strips were fenced, each 40 m by 25 m in size (Fig. 1). Paired strips were allocated to the two groups on a daily basis, such that one group was always 65 m north of the other. First, for 6 days (Period 1) no treatment was applied, to test whether the emissions from the two groups were indistinguishable. For the following 10 days (Period 2), the grazing strip for the N group (treatment group) was sprayed with canola oil at a rate of 120 L ha1. This was expected to cause a reduction in CH4 emissions compared with the S group, which did not receive any oil (control group). Grass height at the start of the experiment was 0.25 (±0.05) cm, growing to 0.45 (±0.05) cm at the end, providing biomass well in excess of dietary requirements. For three days in Period 1 and four days in Period 2, individual CH4 emissions over 24 h from all steers were measured with the SF6 tracer-ratio technique (Johnson et al., 1994). This served as a reference method to independently check on a daily basis whether there was a difference in group emissions. Group dry-matter intakes (DMI) were estimated from herbage mass measured daily with a plate meter, before and after grazing. Individuals' DMI were estimated in Period 2, based on faecal outputs and in vitro feed digestibility. Faecal output was estimated using titanium dioxide ~ o et al., 2008). The (TiO2) as external faecal marker (Pinares-Patin cattle were rounded up and moved away into crushes in order to administer the faecal marker and change the collection gear for the SF6 tracer-ratio technique. These tasks were usually performed between 0800 and 1100 h. 2.2. Measurements of CH4 mole fractions and meteorological variables Mole fractions of CH4 in air were measured as line averages, along four lines. These were placed parallel to the W and E fences of the two rectangles that were grazed simultaneously (Fig. 1). Each
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WeE pair of measurement lines enclosed two rectangles between them, which were grazed on successive days. Each measurement line was realised by a 44.2 m long perforated alkathene pipe, mounted 0.7 m above ground. Air was drawn continuously into the pipe, at a flow rate of 2.1 (±0.1) L min1. The perforation holes were arranged in two rows of 23 holes each, on opposing sides of the pipe (i.e. pairs of opposing holes were 2 m apart). The hole diameter (~1 mm) was much smaller than the inner diameter of the pipe (~25 mm), to make the flow resistance along the pipe negligible compared with the resistance of the holes and thereby minimise flow rate differences between the holes.3 In this respect, the design was similar to that of Denmead et al. (1998). By virtue of their large volume (~22 L), the pipes also acted as ballast volumes, providing an integration time of ~10 min for the CH4 measurements. The air from each intake pipe travelled via a 150 m long polyethylene tube of 4 mm inner diameter either, via the pump, to waste, or when selected for sampling, into a CH4 analyser (DLT-100, Los Gatos Research, Mountain View, California, USA). Sequential selection of the intakes, via a switching manifold, was controlled by a datalogger (21X, Campbell Scientific, Logan, Utah, USA). Each switching cycle was 20 min long, divided equally between the four intakes. Of the 300 s allocated to each intake, the first 150 s were used to purge the sample cell of air from the previous intake, and only the CH4 mole fraction data from the last 150 s were averaged. Automated calibration checks of the CH4 analyser, against two known mole fractions, were performed every 6 h, interrupting sampling for 20 min. Differences between subsequent checks were used to correct the mole fraction data in post-processing, assuming a linear drift (Laubach et al., 2013). Every second day, the air intake lines were relocated to positions W and E of the two pairs of rectangles to be grazed over the next two days. The times of relocation coincided with the times of oilspraying and the times of cattle absence. Wind speed and direction, atmospheric stability and velocity statistics (required inputs for the dispersion model) were measured with a sonic anemometer (81000V, RM Young, Traverse City, Michigan, USA), at 2.20 m above ground and ~50 m E of the easternmost grazing rectangles. The grass height around this instrument was similar to the grass height inside the rectangles before being grazed. Temperature and pressure, required in the conversion from CH4 mole fraction to absolute gas density, were measured near the sonic anemometer: temperature at 0.64 m height with a double-shielded thermocouple, and pressure at 0.05 m with an analogue barometer (PTB101B, Vaisala, Helsinki, Finland).
2.3. Computation of emission rates Wind and turbulence variables were computed for 20-min runs, matching the switching cycle of the CH4 mole fraction measurements. For each run, the mole fraction measured W of a group of cattle was subtracted from the mole fraction measured E of this group and normalised by the number of cattle in the group, n (the actual number was occasionally one or two animals short of the intended number, due to illness or handling problems), to give the mole fraction increase caused by a group's emissions, DEW:
3 With pipe dimensions as stated, and using the Hagen-Poiseuille equation for resistance to laminar pipe flow, the pressure difference along the pipe for a flow rate of 2.1 L min1 is 0.022 Pa. This has negligible effect, compared to the orifice resistance (since the combined area of the 46 holes is only ~7% of the pipe's crosssectional area).
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. DEW;N ¼ D½CH4 EW;N nN
(1a)
. DEW;S ¼ D½CH4 EW;S nS
(1b)
where subscripts “N” and “S” indicate the Northern (oil-treated) and Southern (control) group, respectively. A measure independent of the absolute mole fractions is the relative normalised mole fraction difference, dDEW:
dDEW ¼ DEW;N DEW;S < DEW >
(2)
where is the mean of DEW, N and DEW, S. To establish whether a significant difference between treatment and control existed, it would in principle suffice to investigate the statistical properties of dDEW, requiring neither meteorological data nor micrometeorological theory. However, the meteorological data, in particular the flow variables, allow to improve the analysis in two respects. Firstly, the observed downwindeupwind differences, DEW, can be converted to emission rates. These can then be compared to emission rate estimates obtained with other techniques (here, the SF6 tracer-ratio technique), as well as those observed in other experiments. Also, runs can then be weighted according to their contributions to the total emissions (instead of being weighted equally, regardless of emission rate, as is the case when only the DEW values are known). Secondly, wind and turbulence data allow the definition of objective rejection criteria for unsuitable flow conditions. The collective CH4 emissions from each group of cattle were computed with a backward-Lagrangian stochastic model, executed with the software WindTrax (Version 2.0.8.6, www. thunderbeachscientific.com). The BLS model is described in detail by Flesch et al. (2004). On the software's map, the grazed rectangles were specified as the CH4 emission sources, and the positions of the air intake pipes as those of ‘line concentration sensors’. For each 20min run, the flow field was prescribed by variables obtained with the sonic anemometer, namely mean wind speed, wind direction, stability parameter (Obukhov length), and the ratios of the standard deviations of the three wind components to friction velocity. Heights of the sonic anemometer and the air intakes were reduced by a fixed displacement height of 0.2 m, to account for the effect of the tall grass on the wind profile. The roughness length was not allowed to vary from run to run, for reasons detailed in Laubach (2010); instead it was set to a constant value of 0.025 m. In reality, both displacement height and roughness would have increased over time; ignoring this trend causes small errors in the computed absolute emission rates, but not in their relative differences, which are the main focus of this study. For each 20-min run, the software computed the trajectories of 50,000 air parcels, backwards from a single point at air intake height to a distance of 200 m. The obtained distribution of points where these air parcels encountered ground level (the ‘touchdown’ distribution) was cloned for 23 points along each ‘line concentration sensor’, representing the positions of the air intake holes. The simulation was run for paired grazing rectangles and all four intake pipes simultaneously. Computation outputs were the areaaveraged emission rates for the two grazing rectangles and the background mole fraction of CH4. The area-averaged emission rates were then converted into emission strengths per animal, QN and QS, respectively, for the N and S group (units: g CH4 d1 animal1). Since the flow conditions over the N and S grazing rectangles are assumed identical, QN is related to DEW, N with the same proportionality factor as QS to DEW, S.
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The fourth and final step was to screen for large relative differences between the CH4 mole fractions from the two upwind paths, as these might indicate either systematically-changing wind conditions within the run (instationary flow), or the presence of other CH4 sources near the upwind paths, such as sheep in neighbouring paddocks. Accordingly, suspicious runs were removed either if wind direction was substantially changing, or if the presence of animals in adjacent paddocks had been recorded in the field diary. 2.5. Statistical analyses Two statistical approaches were used to test whether the CH4 emission rates from the two groups of cattle, computed with the BLS model, were different. Both approaches were applied separately to Period 1, when no difference was expected, and to Period 2, when the oil sprayed onto the grass for the N group was expected to cause a difference in group emissions. In the first approach, the differences in CH4 emissions between the groups, for each valid 20-min run, were the input data for a linear mixed-effects model, using days as the random effect (as a surrogate for replicate plots). This analysis was carried out using the generalised-least-squares (gls) regression procedure (Pinheiro et al., 2014) in the R statistical computing environment (version 2.15.2). In the second approach, for each day, the available emission rates for each group were averaged (in order to again use days as ‘replicates’), and then a paired Student's t-test was applied to compare the daily mean emission rates between groups. As an independent check of whether a significant treatment effect was present, the daily CH4 emission rates obtained for individual animals, with the SF6 tracer-ratio technique, were tested for group effects with ANOVA. Fig. 1. Schematic of the experimental set-up. Upper panel: Relative locations of fenced areas. Each day, one pair of rectangles (with same letter) was grazed, in alphabetical order. Within a pair, the N rectangle received oil-spraying treatment (during Period 2 only, see text) and the S rectangle served as the control. Lower panel: Subset of fenced areas with instrumentation, here as on Day 3 when rectangles labelled ‘C’ were to be grazed (followed by grazing of rectangles labelled ‘D’ on the next day). Every second day, the air intake lines were relocated to positions W and E of the two pairs of rectangles to be grazed on the next two days.
2.4. Data filtering Four steps of filtering were applied. Firstly, runs with a friction velocity below a set threshold were excluded, as is recommended practice (Flesch et al., 1995). The threshold was set to 0.12 m s1, a value previously found suitable for this site (Laubach et al., 2008). Secondly, data analysis was restricted to wind directions within ±40 of either E or W. This removed all runs in which there was ambiguity about which intakes were upwind and which downwind of the animals, as well as runs with cross-contamination of a downwind air intake by the emissions plume from the other, farther, grazing rectangle. For the remaining runs, the fractions of simulated touchdowns occurring within the source area (‘TD fractions’) were investigated. They ranged from 0.41 to 1.00. Values close to 1 were common when the grazing rectangles were adjacent to the downwind air intake pipes (such as in Fig. 1, bottom panel, for winds from W), while the smallest TD fractions occurred when the grazing rectangles were adjacent to the upwind air intake pipes and wind direction was more than 30 off E or W. Visual assessment of the touchdown distributions on the WindTrax map showed that in runs with small TD fractions the touchdowns also tended to be concentrated in one half of the grazing rectangle, which means CH4 emissions from cattle in the other half contributed little to the air sampled from the downwind intake. Therefore, the third filtering step was to exclude runs in which the TD fraction was less than 0.8.
3. Results 3.1. Data yield and methane emission rates On a typical day, the cattle spent about 21 h in their assigned grazing rectangle, so the maximum possible yield of 20-min runs was about 63 per day. Only about 30% of runs passed the four filtering criteria, resulting in a total of 105 runs for Period 1 (no oil) and 217 runs in Period 2 (oil in N). On the second day, weak wind conditions led to a complete lack of valid runs, hence only five daily means were available for Period 1. For 644 valid WeE differences of measured CH4 mole fractions, D[CH4]EW, the range was 2.2e2078 ppb, with a mean of 230 ppb and a median of 152 ppb. The CH4 analyser's precision, if defined as 3 times the 1-s repeatability of 3 ppb (Laubach et al., 2013), was thus generally well-suited to resolve D[CH4]EW, with a median relative error of 6%. All but three values (0.5%) exceeded 9 ppb. Basic statistics of the CH4 emission rates, for each group and period, are shown in Table 1. The mean emission rates were between 142 and 162 g CH4 d1 animal1. In both periods, median and mean emissions from the S group were somewhat higher than from the N group; see next section for significance tests. The standard errors in Table 1 are about 5% of the mean Q for Period 1 and 3% of the mean Q for Period 2. A large part of the variability contributing to the standard error came from the diurnal pattern of emissions (Fig. 2). The Q values displayed in this figure were obtained from binning all valid 20-min runs by hour of day. For all groups and periods, Q rose from 1100 h (on most days, this was the time when grazing began) until 1500 h, and Q decreased from 0400 until 0700 h (presumably due to reduced digestive activity when the cattle were sleeping). Because of this diurnal pattern, the betweengroup difference is smaller than the within-day variability.
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3.2. Group differences in emissions The most immediate measure to assess group differences on a run-to-run basis is dDEW (Eq. (2)). For Period 1 (no treatment) one would expect the probability distribution of this variable to be centred at zero, provided there were no other factors predisposing one group of cattle to higher CH4 emissions than the other. For Period 2, if the oil applied to the grass for the N group was effective in reducing emissions, one would expect the distribution to be biased towards negative values. Both expectations are met by the observations (Fig. 3), where the median dDEW for Periods 1 and 2, respectively, are 0.024 and 0.112. If the result of Period 1 is interpreted as pre-existing bias, then the difference between the two values gives a measure of the net effect, suggesting that the oil spraying caused a net reduction of CH4 emissions from the N group by 13.6%. However, both distributions in Fig. 3 appear wide, with significant outliers. Moreover, all runs are weighted equally in this approach, regardless of absolute emission rates, which could introduce bias if the emissions reduction effect itself had a diurnal pattern, or was non-linearly correlated to the emission rate. Therefore, further statistical tests were applied either to the absolute group differences in emission rate (gls regression) or to the absolute daily means of emission rate (paired t-test). The results from both tests are very similar (Table 2). For Period 1, both tests indicate that the CH4 emissions from the S group were 6 (±8.5) g CH4 d1 animal1 (not significantly) higher than from the N group, consistent with the means in Table 1. By contrast, for Period 2 both tests show significant differences (P 0.01), indicating that the oil-treated group emitted 14 or 15 (±5) g CH4 d1 animal1 less than the control group. If one considers the result of Period 1, even though not significantly different from zero, as the most likely baseline and subtracts it from the result of Period 2, then the net reduction due to oil is obtained as 9 (±7) g CH4 d1 animal1.
Fig. 2. Mean diurnal courses (valid runs averaged for each hour) of CH4 emission rates from the two groups of cattle. Upper panel: Period 1 (no oil), lower panel: Period 2 (oil in N). In Period 2, the cattle left the grazing strip at around 0800 h each day and returned to the next grazing strip at around 1100 h. Error bars indicate standard error of the mean; lack of error bar means only one run was available.
3.3. Results from the animal-scale technique Table 3 summarises the daily CH4 emissions from the individual animals, measured with the SF6 tracer-ratio technique. In Period 1, the N and S group means, respectively, were 4.3 and 5.5 g CH4 d1 animal1 less than those from the micrometeorological technique (Table 1), i.e. in relative terms they were 3.0% and 3.7% less for N and S, respectively. The difference between the groups was 4.7 (±4.7) g CH4 d1 animal1 (not significant). In Period 2, the mean emission rates from the SF6 technique were also less than those from the micrometeorological technique, by 10.2 and 5.8 g CH4 d1 animal1 (6.9% and 3.6%) for N and S, respectively. They indicated that the oil-treated group emitted 17.5 (±5.0) g CH4 d1 animal1 less than the control group. Subtracting the mean group difference of Period 1 from that of Period 2, the net reduction due to oil is obtained as 12.8 (±4.9) g CH4 d1 animal1. The oil-treated group had a significantly higher dry-matter intake (10% higher, P ¼ 0.02), of 8.29 (±1.43) kg d1 animal1, compared with 7.51 (±0.90) kg d1 animal1 for the control group. Consequently, the mean CH4 yield (emissions per DMI) of the oilTable 1 Data yield, median and mean CH4 emission rates for each group of cattle and experimental period, obtained with the micrometeorological technique. Period
Number of runs
Group
Median Q (g d1 animal1)
Mean Q (±SEM) (g d1 animal1)
1 e No oil
105
2 e Oil in N
217
N S N (treatment) S (control)
135.6 141.2 149.5 158.6
142.1 148.0 148.8 161.9
(7.1) (7.3) (4.4) (4.8)
Fig. 3. Histogram of the relative differences between the two groups of cattle, of the measured downwindeupwind differences of CH4 mole fraction (Eq. (2)). Wide rear bars: Period 1 (no oil treatment applied), 105 runs, median 0.024. Narrow front bars: Period 2 (oil applied to N group), 217 runs, median 0.112.
treated group was obtained as about 0.8 of that of the control group, with both the micrometeorological and the SF6 technique. 4. Discussion 4.1. Emission rates and data yield The mean CH4 emission rates, of about 150 g d1 animal1, were similar to those in other experiments with grazing beef cattle (Laubach et al., 2008; McGinn et al., 2011). The diurnal emission
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pattern (Fig. 2) showed increases in CH4 emission rates from morning to early afternoon, and decreases at night, similar to those found by McGinn et al. (2011). Presumably, these lagged increases and decreases in feeding activity by a few hours. Such time lags have been observed in feedlots (Loh et al., 2008; Gao et al., 2011) and feedlot-resembling experimental set-ups (McGinn et al., 2009; Laubach et al., 2013). The data yield, with about 30% of periods considered valid, may appear low but is not unusual for a micrometeorological experiment that needs to be restricted to steady winds and selected wind direction sectors in order to unambiguously define ‘upwind’ and ‘downwind’ direction from the emission sources. For example, Flesch et al. (2005) demonstrated, for an experiment in which ammonia emissions from a swine farm were measured with the BLS technique, how a sequence of filtering steps also left them with about 1/3 of ‘good’ runs. In the present experiment, data yield was optimised by the WeE symmetry of the set-up, allowing the roles of upwind and downwind intakes to be reversed. Rejection of N and S wind sectors and calm periods had to be strict, to avoid crosscontamination of the emissions plume from one group by that from the other. Using a data selection threshold for friction velocity generally favours daytime runs over nocturnal and early-morning runs. In the present experiment, selection by wind direction also favoured daytime runs (due to the common development of a sea breeze from the W). With the diurnal pattern of CH4 emissions being largest in the afternoon and smallest in the early morning (Fig. 2), it is likely that the mean emission rates from the micrometeorological technique were systematically overestimated. Comparison of the mean emissions in Table 1 to those from Table 3 supports this suspicion, as for each group and period the mean Q from the micrometeorological technique exceeded the corresponding mean Q from the SF6 tracer-ratio technique by 4e10 g CH4 d1 animal1, equivalent to 3e7%. For both groups, the mean emissions in Period 2 appear a few percent higher than in Period 1 (Table 1). Again, this could be an artefact of the data filtering, if Period 1 data included more runs of relatively low emissions (shortly after cattle began grazing, or during the night) than Period 2. However, the results from the SF6 technique (Table 3) also indicate an increase in emissions from Period 1 to Period 2. Moreover, feed intake (mean group DMI) increased from Period 1 to Period 2 (data not shown), hence the observed increase in CH4 emission rates was probably real.
Table 3 Group means (±standard errors), group mean differences, and probability P for the no-difference hypothesis, for the daily emission rates from individual animals measured with the SF6 tracer-ratio technique.
4.2. Sampling uncertainties
4.3. Group emissions differences and statistical significance
The uncertainty of the mean emission rates (Table 1) includes the effect of the diurnal variation (Fig. 2), as well as other sampling uncertainties. Considering run-wise relative differences (Eq. (2)) removes the diurnal variation. However, these differences are
Given the noisy character of the distribution of CH4 emission rates, as well as their relative differences, it seemed prudent to assess the significance of absolute group differences with two methods, and apply each of them in a way that days of the experiment were treated as the replicates. The results from the two methods corroborate each other (Table 2). Both methods indicate that there was no significant difference in group emissions in Period 1, when the groups of cattle were treated equally. Both methods further agree that there was a significant difference in Period 2, when oil was added to the diet of the N cattle group. They also agree well on the magnitude of the difference, of about 10% in relative terms. The validity of this figure, as well as its statistical significance, is further supported by the good agreement with the results from the SF6 tracer-ratio technique (Table 3). It is therefore concluded that the micrometeorological technique used here is capable of resolving group differences of 10%. There is some suggestion from the results of Period 1, even though not significant, that CH4 emissions from the N group were
Table 2 Statistical test results for the differences in CH4 emission rates between N and S group (oil treatment vs. control) for the micrometeorological technique. Values in parentheses are standard errors. Period
Paired Student's t-test for daily Q means
Generalised least-squaresregression for run-wise Q differences Intercept value Degrees of (g d1 animal1) freedom
1 e No oil 5.72 (8.57) 2 e Oil 14.26 (5.15) in N
100 207
P
Mean QN QS Degrees (g d1 animal1) of freedom
0.51 5.59 (8.50) 0.006 14.83 (4.60)
4 9
P
0.55 0.010
Period
N group (g d1 animal1)
S group (g d1 animal1)
Mean QN e QS (g d1 animal1)
P
1 e No oil 2 e Oil in N
137.8 (3.5) 138.6 (3.2)
142.5 (3.2) 156.1 (3.9)
4.7 (4.7) 17.5 (5.0)
0.25 0.002
subject to considerable run-to-run variability (Fig. 3), for a number of reasons. The first is the description of the spatial source distribution as a uniform area (the grazing rectangle). In reality, the spatial distribution of cattle relative to the downwind air intake pipe, at the time of air-sampling, could differ considerably from the corresponding spatial distribution of cattle in the other grazing rectangle. While the lush uniform grass on offer and the high stocking density ensured that there were no preferential areas for the cattle's feeding activity (evidenced by generally uniform reduction of grass height when the cattle left a grazing strip), it was possible at any time that a group of cattle collectively moved in response to external events, such as the passing of vehicles or farm staff. Secondly, the assumption that flow conditions were equal for both groups is not strictly correct, because the air intake pipes were sampled sequentially. Time-averaged flow variables in the atmospheric surface layer are inherently noisy. Hence, changes in wind speed, wind direction or turbulence statistics from the sampling time for one intake to the sampling time for the next could contribute to random error of the run-wise group differences in emission rates. In principle, one could reduce this error contribution by explicitly computing the flow statistics for shorter periods, matched to the air sampling time from each downwind intake. However, this is not straightforward, as flow rate and mixing effects along the whole length of the air intake lines into the CH4 analyser would need to be known or modelled, and the upwind CH4 mole fractions (sampled at different times) may be subject to temporal changes, too. Therefore, no such attempt was made. Other sources of error for the group differences include variability in feed intake between animals and variability in the rate of passage of feed through the alimentary tract between animals; however, the random effect of these errors had been minimised by large group size, unlimited grass supply, and selection of animals of similar breed, age and weight.
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slightly lower than from the S group already before the oil treatment started. If the observed group difference in Period 2 is reduced by that observed in Period 1, then the relative effect of the oil-spraying on CH4 emissions is obtained as 6% reduction with the micrometeorological technique and as 9% reduction with the animal-scale technique. The observed emissions reduction is compatible with the expectation of an oil effect of the order of 10e20%, as based on the literature reviewed by Grainger and Beauchemin (2011). The observed effect of the oil treatment on CH4 yield (daily CH4 emissions normalised by DMI) was 20% reduction. This result is relevant for understanding the digestion process of cattle and designing emissions mitigation strategies.
flow conditions in which it is expected to perform with reasonable accuracy. An additional filter to exclude strongly stable or unstable stratification, as recommended by Flesch et al. (2004), was not necessary in this experiment, since such conditions were already removed by the other filtering steps. Rigorous filtering always reduces random variability (by removing extreme values), at the potential cost of reduced representativeness of the accepted data samples for the whole sampling period (due to the reduced data yield and possible filtering bias). In an experiment where the primary goal was to obtain group differences, reduced random variability was of higher importance than overall representativeness of the samples.
4.4. Critical design features
4.5. Potential improvements and applications
Detecting a treatment effect of the order of 10% is often a challenging task, particularly in field experiments and in real-world farm management situations. The results presented here show that an optimised micrometeorological technique is capable of meeting this challenge. It should be stressed that this success rests on a number of carefully implemented design features. These features are to: 1) place treatment and control into identical flow conditions, 2) sample their emissions almost simultaneously with identical high-precision equipment, 3) rigorously filter out times of unsuitable wind conditions, and 4) apply relevant statistical tests not to the mean group emissions, but to the distribution of runwise group differences. The last can rarely be realised in micrometeorological experiments. It is made possible only by combination with the first three features, discussed in the following. Identical flow conditions were achieved by selecting terrain that was flat and free of obstacles, using equal set-up geometry for both groups, and handling both groups simultaneously. (Equality of other environmental parameters, such as temperature, is implied, too.) Identical flow conditions ensure that errors in the measurement of the meteorological parameters are equal for treatment and control. Hence, while such errors contribute to the absolute error of Q itself, they cancel out in the group difference. This desirable feature constitutes a major improvement over the approach of Laubach et al. (2013), where different treatments were applied to the same group of animals but in successive weeks. Flow conditions in that experiment varied considerably between weeks (treatments), and contributed to errors in mean emission rates, potentially in three ways: by measurement error, by BLS model error, and by filtering bias due to differences in data yield. In the present experiment, these error sources were irrelevant. Simultaneous sampling of gas emissions was only approximated, by a 5-min switching sequence for the four intake pipes, in which the downwind intakes were sampled during the second and third time slots within a 20-min run. The guiding assumption was that emission rates and flow conditions over a 5-min air collection period would not be too different from their means over the entire run. The disadvantage of instationarity effects from sequential switching was outweighed by the advantages of using a single instrument to measure CH4 (no need for intercomparisons), and that instrument being of high precision. Laubach et al. (2013) showed the closed-path CH4 analyser used here to be one magnitude more precise than an open-path laser system of the kind used by McGinn et al. (2009). This high precision contributed crucially to the ability of resolving a group emissions difference of 10% from only 10 ‘replicates’ (days). Rigorous filtering of data (by friction velocity, wind direction, TD fraction and relative agreement of upwind CH4 mole fractions) was necessary to remove runs with cross-contamination between groups, as well as to ensure that the BLS model was applied only to
The micrometeorological technique presented here is available for testing the efficacy of emissions mitigation treatments under field conditions, provided the expected treatment effect is at least 10%. It will be challenging to use the technique for proving the significance of even smaller effects, because under field conditions it is almost impossible to control all relevant environmental factors, and it is difficult to further reduce the sources of random error discussed in Section 4.2. Perhaps the most promising avenue for improvement could be to develop a design that achieves truly simultaneous sampling of all gas intakes and flow parameters, while maintaining the precision of the individual mole-fraction measurements. Apart from that, increasing the number of replications (sampling days) is a possibility to reduce the statistical detection threshold, at increased operational cost. The technique's application is not restricted to animal groups. Other kinds of emission sources (e.g. fields of suitable sizes, manure storage tanks) could also be compared, pair-wise or even in larger longitudinal arrays, such as in McMillan et al. (2014), provided that it was possible to match their flow environments.
5. Conclusions Both techniques used in the reported experiment, the micrometeorological technique (analysed here in detail) and the SF6 tracer-ratio technique, were capable of detecting a CH4 emissions difference of 10% between two groups of grazing cattle. For the micrometeorological technique, this is only the second rigorous attempt of this kind. It constitutes an improvement over the first, by McGinn et al. (2009), in two respects: the detected difference was smaller than theirs, and it was resolved from a dataset with far fewer ‘replicates’ (days), 10 instead of 54. This success rests on a combination of: careful design using line-averaged mole fractions, a high-precision CH4 analyser, and strict data screening for steady wind conditions. Advantages of the micrometeorological technique are that it does not affect the animals and is less labour-intensive in the field than the SF6 technique.
Acknowledgements This research was undertaken with CRI Core Funding from the Ministry of Business, Innovation and Employment. We thank Steve Lees, Derek Birch, Colin Faiers and Francisco Franco (AgResearch) and Tony McSeveny, Peter Berben and Arezoo Taghizadeh-Toosi (Landcare Research) for their contributions in the field. Ross Martin and Gary LaRosa (NIWA, Wellington) carried out the GC analyses for the SF6 tracer-ratio technique. Guy Forrester (Landcare Research) helped with the statistical analyses.
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techniques for detecting a change in methane emissions from a herd of cattle. Agric. For. Meteorol. 176, 50e63. Leytem, A.B., Dungan, R.S., Bjorneberg, D.L., Koehn, A.C., 2011. Emissions of ammonia, methane, carbon dioxide, and nitrous oxide from dairy cattle housing and manure management systems. J. Environ. Qual. 40, 1383e1394. Loh, Z., Chen, D., Bai, M., Naylor, T., Griffith, D., Hill, J., Denmead, T., McGinn, S., Edis, R., 2008. Measurement of greenhouse gas emissions from Australian feedlot beef production using open-path spectroscopy and atmospheric dispersion modelling. Aust. J. Exp. Agric. 48, 244e247. Loh, Z., Leuning, R., Zegelin, S., Etheridge, D., Bai, Mei, Naylor, T., Griffith, D., 2009. Testing Lagrangian atmospheric dispersion modelling to monitor CO2 and CH4 leakage from geosequestration. Atmos. Environ. 43, 2602e2611. McGinn, S.M., Beauchemin, K.A., 2012. Dairy farm methane emissions using a dispersion model. J. Environ. Qual. 41, 73e79. McGinn, S.M., Beauchemin, K.A., Coates, T., Colombatto, D., 2004. Methane emissions from beef cattle: effects of monensin, sunflower oil, enzymes, yeast, and fumaric acid. J. Anim. Sci. 82, 3346e3356. McGinn, S.M., Beauchemin, K.A., Flesch, T.K., Coates, T., 2009. Performance of a dispersion model to estimate methane loss from cattle in pens. J. Environ. Qual. 38, 1796e1802. McGinn, S.M., Turner, D., Tomkins, N., Charmley, E., Bishop-Hurley, G., Chen, D., 2011. Methane emissions from grazing cattle using point-source dispersion. J. Environ. Qual. 40, 22e27. McMillan, A.M.S., Harvey, M.J., Martin, R.J., Bromley, A.M., Evans, M.J., Mukherjee, S., Laubach, J., 2014. The detectability of nitrous oxide mitigation efficacy in intensively grazed pastures using a multiple-plot micrometeorological technique. Atmos. Meas. Tech. 7, 1169e1184. MfE, 2014. New Zealand's Greenhouse Gas Inventory 1990e2012. Ministry for the Environment, Wellington, New Zealand. Park, K.-H., Wagner-Riddle, C., Gordon, R.J., 2010. Comparing methane fluxes from stored liquid manure using micrometeorological mass balance and floating chamber methods. Agric. For. Meteorol. 150, 175e181. Pattey, E., Edwards, G., Strachan, I.B., Desjardins, R.L., Kaharabata, S., WagnerRiddle, C., 2006. Towards standards for measuring greenhouse gas fluxes from agricultural fields using instrumented towers. Can. J. Soil Sci. 86, 373e400. ~ o, C.S., Molano, G., Smith, A., Clark, H., 2008. Methane emissions from Pinares-Patin dairy cattle divergently selected for bloat susceptibility. Aust. J. Exp. Agric. 48, 234e239. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., the R Development Core Team, 2014. Package ‘nlme’: Linear and Nonlinear Mixed Effects Models. R package Version 3.1-117, p. 113. Available at: http://cran.r-project.org/web/packages/nlme/nlme. pdf (verified 12 May 2014). Sommer, S.G., McGinn, S.M., Hao, X., Larney, F.J., 2004. Techniques for measuring gas emissions from a composting stockpile of cattle manure. Atmos. Environ. 38, 4643e4652. VanderZaag, A.C., Wagner-Riddle, C., Park, K.-H., Gordon, R.J., 2011. Methane emissions from stored liquid dairy manure in a cold climate. Anim. Feed Sci. Technol. 166e167, 581e589.
Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
A micrometeorological technique for detecting small differences in methane emissions from two groups of cattle ~ o b, 2, Johannes Laubach a, *, Samantha P.P. Grover a, 1, Cesar S. Pinares-Patin German Molano b a b
Landcare Research, PO Box 69040, Lincoln 7640, New Zealand AgResearch, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand
h i g h l i g h t s A new micrometeorological approach for treatment-control comparisons was developed. With this approach we compared methane emissions from two groups of grazing cattle. A relative group difference in emissions of order 10% was detected (P ¼ 0.01). This result was corroborated with a non-micrometeorological tracer-ratio technique.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 June 2014 Received in revised form 11 September 2014 Accepted 12 September 2014 Available online 16 September 2014
Potential approaches for reducing enteric methane (CH4) emissions from cattle will require verification of their efficacy at the paddock scale. We designed a micrometeorological approach to compare emissions from two groups of grazing cattle. The approach consists of measuring line-averaged CH4 mole fractions upwind and downwind of each group and using a backward-Lagrangian stochastic model to compute CH4 emission rates from the observed mole fractions, in combination with turbulence statistics measured by a sonic anemometer. With careful screening for suitable wind conditions, a difference of 10% in group emission rates could be detected. This result was corroborated by simultaneous measurements of daily CH4 emissions from each animal with the sulfur hexafluoride (SF6) tracer-ratio technique. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Backward-Lagrangian stochastic model Cattle CH4 emissions Gas dispersion Atmospheric surface layer Emissions mitigation
1. Introduction In countries with large numbers of ruminant livestock, methane (CH4) emissions from these animals contribute significantly to the total greenhouse gas emissions of these countries. For example, in 2012 enteric CH4 accounted for 31.5% of New Zealand's total greenhouse gas emissions (MfE, 2014). Micrometeorological techniques have contributed to determining emission rates of CH4 from grazing animals (Judd et al., 1999; Laubach and Kelliher, 2004, 2005; McGinn et al., 2011), feedlots (Loh et al., 2008; Gao et al.,
* Corresponding author. E-mail address: [email protected] (J. Laubach). 1 Present address: Department of Agricultural Sciences, La Trobe University, 5 Ring Rd, Bundoora, VIC 3086, Australia. 2 Present address: CSIRO Black Mountain Laboratories, Clunies Ross Street, Acton, ACT 2601, Australia. http://dx.doi.org/10.1016/j.atmosenv.2014.09.036 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
2011), manure heaps (Sommer et al., 2004), manure storage tanks (Park et al., 2010; VanderZaag et al., 2011), biodigesters (Flesch et al., 2011) and whole farms (Leytem et al., 2011; McGinn and Beauchemin, 2012) thereby providing valuable information upon which emission factors for national greenhouse gas inventories can be based. Merits and constraints of various micrometeorological techniques in this regard have been reviewed by Denmead (2008) and Harper et al. (2011). It is less well established what role these techniques could potentially play in verifying the efficacy of mitigation approaches. Then, it is not the absolute emission rate that is of primary interest; rather, it is differences in emission rates as a consequence of different treatments, management practices, or selections of animals that would need to be accurately determined. This task is commonly undertaken by emissions measurements at the ‘animal scale’, in controlled conditions such as in calorimetric chambers (e.g. McGinn et al., 2004). Such experiments can be designed to
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directly compare specific treatments, and thus appear most suitable to identify promising mitigation approaches. However, once proof of concept for a mitigation treatment has been achieved, it must still be verified at the ‘herd scale’, or ‘paddock scale’, under representative farming conditions, that the expected emissions reduction is achieved there, too. This is particularly challenging with animals grazing outdoors, as is widespread year-round practice e.g. in New Zealand and Australia. It was Pattey et al. (2006) who originally suggested that micrometeorological techniques could be designed for ‘treatment versus control’ experiments. McGinn et al. (2009) pioneered this idea, using the backward-Lagrangian stochastic (BLS) technique (Flesch et al., 1995) to detect a significant difference in CH4 emission rates between two groups of 10 cattle each that were fed diets differing in grain and forage ingredients. An overall emissions difference was not stated by the authors, but the data shown suggest that it was of the order of 20e30%. In that experiment, the groups of cattle on different diets were each enclosed in a rectangular pen and the pen surrounded by a four-path laser system to measure CH4 mole fractions. Each animal carried a Global Positioning System transmitter, so that the BLS flow model could be provided with accurate point-source locations in space and time. For the whole study period of 54 d, concurrent CH4 emission measurements on each animal were made with the SF6 tracer-ratio technique (Johnson et al., 1994), and used as a reference to assess the performance of the BLS technique. While this experiment provided a valuable proof of principle, it will only rarely be possible to devote such a large amount of effort and resources to a single treatmentcontrol comparison. Laubach et al. (2013) tried a different approach to test whether a number of different micrometeorological techniques were capable of detecting treatment effects. They measured CH4 emissions from a group of cattle that were provided a forage diet at amounts increasing from one week to the next. The micrometeorological techniques successfully detected changes in weekly CH4 emission rates of the order of 30%, in response to the feed intake changes. While, in targeted research trials with specific treatments, CH4 emission reductions of that magnitude have sometimes been observed, practical application of such treatments may only become available with many more years of research (Eckard et al., 2010). For the foreseeable future, practical mitigation steps may come from small improvements of farm management practices, e.g. with respect to feed intake, and are likely to have relatively small effects. To demonstrate that a certain management practice makes a detectable difference in CH4 emissions therefore poses a considerable measurement challenge. Tackling this challenge, the present study was aimed at improving the approach of McGinn et al. (2009) and applying it to grazing animals. The BLS technique was employed to measure CH4 emissions from two groups of cattle simultaneously, in a ‘treatment versus control’ set-up. The objective was to test whether a difference in mean group emissions of the order of 10% could be detected. The treatment to create a difference of this magnitude consisted in the spraying of oil onto the grass prior to grazing, since lipid additions to the diet are known to decrease the CH4 emissions of ruminants (Grainger and Beauchemin, 2011). One step to optimise the measurement technique was to use a single high-precision gas analyser to determine the emissions from both cattle groups, which removed the need for intercomparison of instruments. This idea was the same as in McMillan et al. (2014), who measured nitrous oxide emissions from a row of differentlyfertilised paddocks, but the design details were different. Here, perforated pipes were used to provide line-integrated air samples to the CH4 analyser. This was done because Laubach et al. (2013) found that approaches using line-averaged mole-fraction
measurements performed better than approaches using point mole-fraction measurements, due to both a larger range of admissible wind directions and smaller run-to-run variations in obtained emission rates. They suggested as ‘the ideal herd-scale technique one that combines the strengths of the accurate closedpath analyser with the strengths of a path-averaging approach’. The study reported here realised this idea. The intake pipes were symmetrically arranged upwind and downwind of the cattle groups, in order to obtain group emission rates as well as their difference. The combination of the BLS technique with perforated intake pipes was first employed by Loh et al. (2009). Their objective was to detect CO2 and CH4 escaping from underground storage. Using the same approach to detect emission differences between two differently-treated groups of animals is a novel application. 2. Materials and methods 2.1. Experimental design, animals, and treatments The experiment was conducted at Aorangi Research Farm (40.336 S, 175.465 E), near Palmerston North, New Zealand, from 27 September to 14 October 2011. The site is ideally suited for micrometeorological techniques because the surrounding terrain is flat for several kilometres in all directions. Permanent fence lines on the farm are approximately aligned with the main compass directions. Temporary fences for this experiment were lined up parallel to the former, and references in the following to the compass directions are relative to the ‘farm-North’ defined by the main fences. Two groups of 30 cattle each were selected, with equal mean liveweight. The cattle were one-year-old Hereford Friesian steers. In a flat uniform paddock dominated by ryegrass (Lolium perenne), 32 rectangular strips were fenced, each 40 m by 25 m in size (Fig. 1). Paired strips were allocated to the two groups on a daily basis, such that one group was always 65 m north of the other. First, for 6 days (Period 1) no treatment was applied, to test whether the emissions from the two groups were indistinguishable. For the following 10 days (Period 2), the grazing strip for the N group (treatment group) was sprayed with canola oil at a rate of 120 L ha1. This was expected to cause a reduction in CH4 emissions compared with the S group, which did not receive any oil (control group). Grass height at the start of the experiment was 0.25 (±0.05) cm, growing to 0.45 (±0.05) cm at the end, providing biomass well in excess of dietary requirements. For three days in Period 1 and four days in Period 2, individual CH4 emissions over 24 h from all steers were measured with the SF6 tracer-ratio technique (Johnson et al., 1994). This served as a reference method to independently check on a daily basis whether there was a difference in group emissions. Group dry-matter intakes (DMI) were estimated from herbage mass measured daily with a plate meter, before and after grazing. Individuals' DMI were estimated in Period 2, based on faecal outputs and in vitro feed digestibility. Faecal output was estimated using titanium dioxide ~ o et al., 2008). The (TiO2) as external faecal marker (Pinares-Patin cattle were rounded up and moved away into crushes in order to administer the faecal marker and change the collection gear for the SF6 tracer-ratio technique. These tasks were usually performed between 0800 and 1100 h. 2.2. Measurements of CH4 mole fractions and meteorological variables Mole fractions of CH4 in air were measured as line averages, along four lines. These were placed parallel to the W and E fences of the two rectangles that were grazed simultaneously (Fig. 1). Each
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WeE pair of measurement lines enclosed two rectangles between them, which were grazed on successive days. Each measurement line was realised by a 44.2 m long perforated alkathene pipe, mounted 0.7 m above ground. Air was drawn continuously into the pipe, at a flow rate of 2.1 (±0.1) L min1. The perforation holes were arranged in two rows of 23 holes each, on opposing sides of the pipe (i.e. pairs of opposing holes were 2 m apart). The hole diameter (~1 mm) was much smaller than the inner diameter of the pipe (~25 mm), to make the flow resistance along the pipe negligible compared with the resistance of the holes and thereby minimise flow rate differences between the holes.3 In this respect, the design was similar to that of Denmead et al. (1998). By virtue of their large volume (~22 L), the pipes also acted as ballast volumes, providing an integration time of ~10 min for the CH4 measurements. The air from each intake pipe travelled via a 150 m long polyethylene tube of 4 mm inner diameter either, via the pump, to waste, or when selected for sampling, into a CH4 analyser (DLT-100, Los Gatos Research, Mountain View, California, USA). Sequential selection of the intakes, via a switching manifold, was controlled by a datalogger (21X, Campbell Scientific, Logan, Utah, USA). Each switching cycle was 20 min long, divided equally between the four intakes. Of the 300 s allocated to each intake, the first 150 s were used to purge the sample cell of air from the previous intake, and only the CH4 mole fraction data from the last 150 s were averaged. Automated calibration checks of the CH4 analyser, against two known mole fractions, were performed every 6 h, interrupting sampling for 20 min. Differences between subsequent checks were used to correct the mole fraction data in post-processing, assuming a linear drift (Laubach et al., 2013). Every second day, the air intake lines were relocated to positions W and E of the two pairs of rectangles to be grazed over the next two days. The times of relocation coincided with the times of oilspraying and the times of cattle absence. Wind speed and direction, atmospheric stability and velocity statistics (required inputs for the dispersion model) were measured with a sonic anemometer (81000V, RM Young, Traverse City, Michigan, USA), at 2.20 m above ground and ~50 m E of the easternmost grazing rectangles. The grass height around this instrument was similar to the grass height inside the rectangles before being grazed. Temperature and pressure, required in the conversion from CH4 mole fraction to absolute gas density, were measured near the sonic anemometer: temperature at 0.64 m height with a double-shielded thermocouple, and pressure at 0.05 m with an analogue barometer (PTB101B, Vaisala, Helsinki, Finland).
2.3. Computation of emission rates Wind and turbulence variables were computed for 20-min runs, matching the switching cycle of the CH4 mole fraction measurements. For each run, the mole fraction measured W of a group of cattle was subtracted from the mole fraction measured E of this group and normalised by the number of cattle in the group, n (the actual number was occasionally one or two animals short of the intended number, due to illness or handling problems), to give the mole fraction increase caused by a group's emissions, DEW:
3 With pipe dimensions as stated, and using the Hagen-Poiseuille equation for resistance to laminar pipe flow, the pressure difference along the pipe for a flow rate of 2.1 L min1 is 0.022 Pa. This has negligible effect, compared to the orifice resistance (since the combined area of the 46 holes is only ~7% of the pipe's crosssectional area).
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. DEW;N ¼ D½CH4 EW;N nN
(1a)
. DEW;S ¼ D½CH4 EW;S nS
(1b)
where subscripts “N” and “S” indicate the Northern (oil-treated) and Southern (control) group, respectively. A measure independent of the absolute mole fractions is the relative normalised mole fraction difference, dDEW:
dDEW ¼ DEW;N DEW;S < DEW >
(2)
where
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The fourth and final step was to screen for large relative differences between the CH4 mole fractions from the two upwind paths, as these might indicate either systematically-changing wind conditions within the run (instationary flow), or the presence of other CH4 sources near the upwind paths, such as sheep in neighbouring paddocks. Accordingly, suspicious runs were removed either if wind direction was substantially changing, or if the presence of animals in adjacent paddocks had been recorded in the field diary. 2.5. Statistical analyses Two statistical approaches were used to test whether the CH4 emission rates from the two groups of cattle, computed with the BLS model, were different. Both approaches were applied separately to Period 1, when no difference was expected, and to Period 2, when the oil sprayed onto the grass for the N group was expected to cause a difference in group emissions. In the first approach, the differences in CH4 emissions between the groups, for each valid 20-min run, were the input data for a linear mixed-effects model, using days as the random effect (as a surrogate for replicate plots). This analysis was carried out using the generalised-least-squares (gls) regression procedure (Pinheiro et al., 2014) in the R statistical computing environment (version 2.15.2). In the second approach, for each day, the available emission rates for each group were averaged (in order to again use days as ‘replicates’), and then a paired Student's t-test was applied to compare the daily mean emission rates between groups. As an independent check of whether a significant treatment effect was present, the daily CH4 emission rates obtained for individual animals, with the SF6 tracer-ratio technique, were tested for group effects with ANOVA. Fig. 1. Schematic of the experimental set-up. Upper panel: Relative locations of fenced areas. Each day, one pair of rectangles (with same letter) was grazed, in alphabetical order. Within a pair, the N rectangle received oil-spraying treatment (during Period 2 only, see text) and the S rectangle served as the control. Lower panel: Subset of fenced areas with instrumentation, here as on Day 3 when rectangles labelled ‘C’ were to be grazed (followed by grazing of rectangles labelled ‘D’ on the next day). Every second day, the air intake lines were relocated to positions W and E of the two pairs of rectangles to be grazed on the next two days.
2.4. Data filtering Four steps of filtering were applied. Firstly, runs with a friction velocity below a set threshold were excluded, as is recommended practice (Flesch et al., 1995). The threshold was set to 0.12 m s1, a value previously found suitable for this site (Laubach et al., 2008). Secondly, data analysis was restricted to wind directions within ±40 of either E or W. This removed all runs in which there was ambiguity about which intakes were upwind and which downwind of the animals, as well as runs with cross-contamination of a downwind air intake by the emissions plume from the other, farther, grazing rectangle. For the remaining runs, the fractions of simulated touchdowns occurring within the source area (‘TD fractions’) were investigated. They ranged from 0.41 to 1.00. Values close to 1 were common when the grazing rectangles were adjacent to the downwind air intake pipes (such as in Fig. 1, bottom panel, for winds from W), while the smallest TD fractions occurred when the grazing rectangles were adjacent to the upwind air intake pipes and wind direction was more than 30 off E or W. Visual assessment of the touchdown distributions on the WindTrax map showed that in runs with small TD fractions the touchdowns also tended to be concentrated in one half of the grazing rectangle, which means CH4 emissions from cattle in the other half contributed little to the air sampled from the downwind intake. Therefore, the third filtering step was to exclude runs in which the TD fraction was less than 0.8.
3. Results 3.1. Data yield and methane emission rates On a typical day, the cattle spent about 21 h in their assigned grazing rectangle, so the maximum possible yield of 20-min runs was about 63 per day. Only about 30% of runs passed the four filtering criteria, resulting in a total of 105 runs for Period 1 (no oil) and 217 runs in Period 2 (oil in N). On the second day, weak wind conditions led to a complete lack of valid runs, hence only five daily means were available for Period 1. For 644 valid WeE differences of measured CH4 mole fractions, D[CH4]EW, the range was 2.2e2078 ppb, with a mean of 230 ppb and a median of 152 ppb. The CH4 analyser's precision, if defined as 3 times the 1-s repeatability of 3 ppb (Laubach et al., 2013), was thus generally well-suited to resolve D[CH4]EW, with a median relative error of 6%. All but three values (0.5%) exceeded 9 ppb. Basic statistics of the CH4 emission rates, for each group and period, are shown in Table 1. The mean emission rates were between 142 and 162 g CH4 d1 animal1. In both periods, median and mean emissions from the S group were somewhat higher than from the N group; see next section for significance tests. The standard errors in Table 1 are about 5% of the mean Q for Period 1 and 3% of the mean Q for Period 2. A large part of the variability contributing to the standard error came from the diurnal pattern of emissions (Fig. 2). The Q values displayed in this figure were obtained from binning all valid 20-min runs by hour of day. For all groups and periods, Q rose from 1100 h (on most days, this was the time when grazing began) until 1500 h, and Q decreased from 0400 until 0700 h (presumably due to reduced digestive activity when the cattle were sleeping). Because of this diurnal pattern, the betweengroup difference is smaller than the within-day variability.
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3.2. Group differences in emissions The most immediate measure to assess group differences on a run-to-run basis is dDEW (Eq. (2)). For Period 1 (no treatment) one would expect the probability distribution of this variable to be centred at zero, provided there were no other factors predisposing one group of cattle to higher CH4 emissions than the other. For Period 2, if the oil applied to the grass for the N group was effective in reducing emissions, one would expect the distribution to be biased towards negative values. Both expectations are met by the observations (Fig. 3), where the median dDEW for Periods 1 and 2, respectively, are 0.024 and 0.112. If the result of Period 1 is interpreted as pre-existing bias, then the difference between the two values gives a measure of the net effect, suggesting that the oil spraying caused a net reduction of CH4 emissions from the N group by 13.6%. However, both distributions in Fig. 3 appear wide, with significant outliers. Moreover, all runs are weighted equally in this approach, regardless of absolute emission rates, which could introduce bias if the emissions reduction effect itself had a diurnal pattern, or was non-linearly correlated to the emission rate. Therefore, further statistical tests were applied either to the absolute group differences in emission rate (gls regression) or to the absolute daily means of emission rate (paired t-test). The results from both tests are very similar (Table 2). For Period 1, both tests indicate that the CH4 emissions from the S group were 6 (±8.5) g CH4 d1 animal1 (not significantly) higher than from the N group, consistent with the means in Table 1. By contrast, for Period 2 both tests show significant differences (P 0.01), indicating that the oil-treated group emitted 14 or 15 (±5) g CH4 d1 animal1 less than the control group. If one considers the result of Period 1, even though not significantly different from zero, as the most likely baseline and subtracts it from the result of Period 2, then the net reduction due to oil is obtained as 9 (±7) g CH4 d1 animal1.
Fig. 2. Mean diurnal courses (valid runs averaged for each hour) of CH4 emission rates from the two groups of cattle. Upper panel: Period 1 (no oil), lower panel: Period 2 (oil in N). In Period 2, the cattle left the grazing strip at around 0800 h each day and returned to the next grazing strip at around 1100 h. Error bars indicate standard error of the mean; lack of error bar means only one run was available.
3.3. Results from the animal-scale technique Table 3 summarises the daily CH4 emissions from the individual animals, measured with the SF6 tracer-ratio technique. In Period 1, the N and S group means, respectively, were 4.3 and 5.5 g CH4 d1 animal1 less than those from the micrometeorological technique (Table 1), i.e. in relative terms they were 3.0% and 3.7% less for N and S, respectively. The difference between the groups was 4.7 (±4.7) g CH4 d1 animal1 (not significant). In Period 2, the mean emission rates from the SF6 technique were also less than those from the micrometeorological technique, by 10.2 and 5.8 g CH4 d1 animal1 (6.9% and 3.6%) for N and S, respectively. They indicated that the oil-treated group emitted 17.5 (±5.0) g CH4 d1 animal1 less than the control group. Subtracting the mean group difference of Period 1 from that of Period 2, the net reduction due to oil is obtained as 12.8 (±4.9) g CH4 d1 animal1. The oil-treated group had a significantly higher dry-matter intake (10% higher, P ¼ 0.02), of 8.29 (±1.43) kg d1 animal1, compared with 7.51 (±0.90) kg d1 animal1 for the control group. Consequently, the mean CH4 yield (emissions per DMI) of the oilTable 1 Data yield, median and mean CH4 emission rates for each group of cattle and experimental period, obtained with the micrometeorological technique. Period
Number of runs
Group
Median Q (g d1 animal1)
Mean Q (±SEM) (g d1 animal1)
1 e No oil
105
2 e Oil in N
217
N S N (treatment) S (control)
135.6 141.2 149.5 158.6
142.1 148.0 148.8 161.9
(7.1) (7.3) (4.4) (4.8)
Fig. 3. Histogram of the relative differences between the two groups of cattle, of the measured downwindeupwind differences of CH4 mole fraction (Eq. (2)). Wide rear bars: Period 1 (no oil treatment applied), 105 runs, median 0.024. Narrow front bars: Period 2 (oil applied to N group), 217 runs, median 0.112.
treated group was obtained as about 0.8 of that of the control group, with both the micrometeorological and the SF6 technique. 4. Discussion 4.1. Emission rates and data yield The mean CH4 emission rates, of about 150 g d1 animal1, were similar to those in other experiments with grazing beef cattle (Laubach et al., 2008; McGinn et al., 2011). The diurnal emission
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pattern (Fig. 2) showed increases in CH4 emission rates from morning to early afternoon, and decreases at night, similar to those found by McGinn et al. (2011). Presumably, these lagged increases and decreases in feeding activity by a few hours. Such time lags have been observed in feedlots (Loh et al., 2008; Gao et al., 2011) and feedlot-resembling experimental set-ups (McGinn et al., 2009; Laubach et al., 2013). The data yield, with about 30% of periods considered valid, may appear low but is not unusual for a micrometeorological experiment that needs to be restricted to steady winds and selected wind direction sectors in order to unambiguously define ‘upwind’ and ‘downwind’ direction from the emission sources. For example, Flesch et al. (2005) demonstrated, for an experiment in which ammonia emissions from a swine farm were measured with the BLS technique, how a sequence of filtering steps also left them with about 1/3 of ‘good’ runs. In the present experiment, data yield was optimised by the WeE symmetry of the set-up, allowing the roles of upwind and downwind intakes to be reversed. Rejection of N and S wind sectors and calm periods had to be strict, to avoid crosscontamination of the emissions plume from one group by that from the other. Using a data selection threshold for friction velocity generally favours daytime runs over nocturnal and early-morning runs. In the present experiment, selection by wind direction also favoured daytime runs (due to the common development of a sea breeze from the W). With the diurnal pattern of CH4 emissions being largest in the afternoon and smallest in the early morning (Fig. 2), it is likely that the mean emission rates from the micrometeorological technique were systematically overestimated. Comparison of the mean emissions in Table 1 to those from Table 3 supports this suspicion, as for each group and period the mean Q from the micrometeorological technique exceeded the corresponding mean Q from the SF6 tracer-ratio technique by 4e10 g CH4 d1 animal1, equivalent to 3e7%. For both groups, the mean emissions in Period 2 appear a few percent higher than in Period 1 (Table 1). Again, this could be an artefact of the data filtering, if Period 1 data included more runs of relatively low emissions (shortly after cattle began grazing, or during the night) than Period 2. However, the results from the SF6 technique (Table 3) also indicate an increase in emissions from Period 1 to Period 2. Moreover, feed intake (mean group DMI) increased from Period 1 to Period 2 (data not shown), hence the observed increase in CH4 emission rates was probably real.
Table 3 Group means (±standard errors), group mean differences, and probability P for the no-difference hypothesis, for the daily emission rates from individual animals measured with the SF6 tracer-ratio technique.
4.2. Sampling uncertainties
4.3. Group emissions differences and statistical significance
The uncertainty of the mean emission rates (Table 1) includes the effect of the diurnal variation (Fig. 2), as well as other sampling uncertainties. Considering run-wise relative differences (Eq. (2)) removes the diurnal variation. However, these differences are
Given the noisy character of the distribution of CH4 emission rates, as well as their relative differences, it seemed prudent to assess the significance of absolute group differences with two methods, and apply each of them in a way that days of the experiment were treated as the replicates. The results from the two methods corroborate each other (Table 2). Both methods indicate that there was no significant difference in group emissions in Period 1, when the groups of cattle were treated equally. Both methods further agree that there was a significant difference in Period 2, when oil was added to the diet of the N cattle group. They also agree well on the magnitude of the difference, of about 10% in relative terms. The validity of this figure, as well as its statistical significance, is further supported by the good agreement with the results from the SF6 tracer-ratio technique (Table 3). It is therefore concluded that the micrometeorological technique used here is capable of resolving group differences of 10%. There is some suggestion from the results of Period 1, even though not significant, that CH4 emissions from the N group were
Table 2 Statistical test results for the differences in CH4 emission rates between N and S group (oil treatment vs. control) for the micrometeorological technique. Values in parentheses are standard errors. Period
Paired Student's t-test for daily Q means
Generalised least-squaresregression for run-wise Q differences Intercept value Degrees of (g d1 animal1) freedom
1 e No oil 5.72 (8.57) 2 e Oil 14.26 (5.15) in N
100 207
P
Mean QN QS Degrees (g d1 animal1) of freedom
0.51 5.59 (8.50) 0.006 14.83 (4.60)
4 9
P
0.55 0.010
Period
N group (g d1 animal1)
S group (g d1 animal1)
Mean QN e QS (g d1 animal1)
P
1 e No oil 2 e Oil in N
137.8 (3.5) 138.6 (3.2)
142.5 (3.2) 156.1 (3.9)
4.7 (4.7) 17.5 (5.0)
0.25 0.002
subject to considerable run-to-run variability (Fig. 3), for a number of reasons. The first is the description of the spatial source distribution as a uniform area (the grazing rectangle). In reality, the spatial distribution of cattle relative to the downwind air intake pipe, at the time of air-sampling, could differ considerably from the corresponding spatial distribution of cattle in the other grazing rectangle. While the lush uniform grass on offer and the high stocking density ensured that there were no preferential areas for the cattle's feeding activity (evidenced by generally uniform reduction of grass height when the cattle left a grazing strip), it was possible at any time that a group of cattle collectively moved in response to external events, such as the passing of vehicles or farm staff. Secondly, the assumption that flow conditions were equal for both groups is not strictly correct, because the air intake pipes were sampled sequentially. Time-averaged flow variables in the atmospheric surface layer are inherently noisy. Hence, changes in wind speed, wind direction or turbulence statistics from the sampling time for one intake to the sampling time for the next could contribute to random error of the run-wise group differences in emission rates. In principle, one could reduce this error contribution by explicitly computing the flow statistics for shorter periods, matched to the air sampling time from each downwind intake. However, this is not straightforward, as flow rate and mixing effects along the whole length of the air intake lines into the CH4 analyser would need to be known or modelled, and the upwind CH4 mole fractions (sampled at different times) may be subject to temporal changes, too. Therefore, no such attempt was made. Other sources of error for the group differences include variability in feed intake between animals and variability in the rate of passage of feed through the alimentary tract between animals; however, the random effect of these errors had been minimised by large group size, unlimited grass supply, and selection of animals of similar breed, age and weight.
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slightly lower than from the S group already before the oil treatment started. If the observed group difference in Period 2 is reduced by that observed in Period 1, then the relative effect of the oil-spraying on CH4 emissions is obtained as 6% reduction with the micrometeorological technique and as 9% reduction with the animal-scale technique. The observed emissions reduction is compatible with the expectation of an oil effect of the order of 10e20%, as based on the literature reviewed by Grainger and Beauchemin (2011). The observed effect of the oil treatment on CH4 yield (daily CH4 emissions normalised by DMI) was 20% reduction. This result is relevant for understanding the digestion process of cattle and designing emissions mitigation strategies.
flow conditions in which it is expected to perform with reasonable accuracy. An additional filter to exclude strongly stable or unstable stratification, as recommended by Flesch et al. (2004), was not necessary in this experiment, since such conditions were already removed by the other filtering steps. Rigorous filtering always reduces random variability (by removing extreme values), at the potential cost of reduced representativeness of the accepted data samples for the whole sampling period (due to the reduced data yield and possible filtering bias). In an experiment where the primary goal was to obtain group differences, reduced random variability was of higher importance than overall representativeness of the samples.
4.4. Critical design features
4.5. Potential improvements and applications
Detecting a treatment effect of the order of 10% is often a challenging task, particularly in field experiments and in real-world farm management situations. The results presented here show that an optimised micrometeorological technique is capable of meeting this challenge. It should be stressed that this success rests on a number of carefully implemented design features. These features are to: 1) place treatment and control into identical flow conditions, 2) sample their emissions almost simultaneously with identical high-precision equipment, 3) rigorously filter out times of unsuitable wind conditions, and 4) apply relevant statistical tests not to the mean group emissions, but to the distribution of runwise group differences. The last can rarely be realised in micrometeorological experiments. It is made possible only by combination with the first three features, discussed in the following. Identical flow conditions were achieved by selecting terrain that was flat and free of obstacles, using equal set-up geometry for both groups, and handling both groups simultaneously. (Equality of other environmental parameters, such as temperature, is implied, too.) Identical flow conditions ensure that errors in the measurement of the meteorological parameters are equal for treatment and control. Hence, while such errors contribute to the absolute error of Q itself, they cancel out in the group difference. This desirable feature constitutes a major improvement over the approach of Laubach et al. (2013), where different treatments were applied to the same group of animals but in successive weeks. Flow conditions in that experiment varied considerably between weeks (treatments), and contributed to errors in mean emission rates, potentially in three ways: by measurement error, by BLS model error, and by filtering bias due to differences in data yield. In the present experiment, these error sources were irrelevant. Simultaneous sampling of gas emissions was only approximated, by a 5-min switching sequence for the four intake pipes, in which the downwind intakes were sampled during the second and third time slots within a 20-min run. The guiding assumption was that emission rates and flow conditions over a 5-min air collection period would not be too different from their means over the entire run. The disadvantage of instationarity effects from sequential switching was outweighed by the advantages of using a single instrument to measure CH4 (no need for intercomparisons), and that instrument being of high precision. Laubach et al. (2013) showed the closed-path CH4 analyser used here to be one magnitude more precise than an open-path laser system of the kind used by McGinn et al. (2009). This high precision contributed crucially to the ability of resolving a group emissions difference of 10% from only 10 ‘replicates’ (days). Rigorous filtering of data (by friction velocity, wind direction, TD fraction and relative agreement of upwind CH4 mole fractions) was necessary to remove runs with cross-contamination between groups, as well as to ensure that the BLS model was applied only to
The micrometeorological technique presented here is available for testing the efficacy of emissions mitigation treatments under field conditions, provided the expected treatment effect is at least 10%. It will be challenging to use the technique for proving the significance of even smaller effects, because under field conditions it is almost impossible to control all relevant environmental factors, and it is difficult to further reduce the sources of random error discussed in Section 4.2. Perhaps the most promising avenue for improvement could be to develop a design that achieves truly simultaneous sampling of all gas intakes and flow parameters, while maintaining the precision of the individual mole-fraction measurements. Apart from that, increasing the number of replications (sampling days) is a possibility to reduce the statistical detection threshold, at increased operational cost. The technique's application is not restricted to animal groups. Other kinds of emission sources (e.g. fields of suitable sizes, manure storage tanks) could also be compared, pair-wise or even in larger longitudinal arrays, such as in McMillan et al. (2014), provided that it was possible to match their flow environments.
5. Conclusions Both techniques used in the reported experiment, the micrometeorological technique (analysed here in detail) and the SF6 tracer-ratio technique, were capable of detecting a CH4 emissions difference of 10% between two groups of grazing cattle. For the micrometeorological technique, this is only the second rigorous attempt of this kind. It constitutes an improvement over the first, by McGinn et al. (2009), in two respects: the detected difference was smaller than theirs, and it was resolved from a dataset with far fewer ‘replicates’ (days), 10 instead of 54. This success rests on a combination of: careful design using line-averaged mole fractions, a high-precision CH4 analyser, and strict data screening for steady wind conditions. Advantages of the micrometeorological technique are that it does not affect the animals and is less labour-intensive in the field than the SF6 technique.
Acknowledgements This research was undertaken with CRI Core Funding from the Ministry of Business, Innovation and Employment. We thank Steve Lees, Derek Birch, Colin Faiers and Francisco Franco (AgResearch) and Tony McSeveny, Peter Berben and Arezoo Taghizadeh-Toosi (Landcare Research) for their contributions in the field. Ross Martin and Gary LaRosa (NIWA, Wellington) carried out the GC analyses for the SF6 tracer-ratio technique. Guy Forrester (Landcare Research) helped with the statistical analyses.
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