Emission of Gas and Dust from Livestock
METHANE AND AMMONIA EMISSION MEASUREMENTS IN A NATURALLY VENTILATED DAIRY FREESTALL BARN USING SPECIFIC DATA CLASSIFICATION CRITERIA
SCHMITHAUSEN, A. J.1, TRIMBORN, M.1, GERLACH, K.2, SÜDEKUM, K.-H.2, BÜSCHER, W.1
1 Institute of Agricultural Engineering, University of Bonn, Nußallee 5, 53115 Bonn, Germany;
2 Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115 Bonn, Germany
ABSTRACT: Since it is difficult to determine the air exchange rate of naturally ventilated barns, measurements of emissions at herd level which generate reliable and robust results, are a continuous challenge. Therefore, the primary objective of this study was to define a validity range of the measurement variables ‘wind velocity’ and ‘wind direction’
for long-term measurements at barn level and to apply these requirements to a feeding trial in a naturally cross-ventilated dairy freestall barn. Data classification criteria (DCC) for the key variables ‘wind velocity’ and ‘wind direction’ were defined to allow quantification of the ventilation rate. As a result, 18% of the one-hour values of the key variables out of the 169-day measurement period fulfilled the validity range defined by DCC. For methane (CH4) no differences were observed (P > 0.05) but ammonia (NH3) emissions decreased from 30.0 to 22.3 g NH3 (livestock unit)-1 day-1 (-34.5%) by feeding an additive in the ration (P < 0.01). The data confirm and support previous findings that greenhouse gas measurements in a naturally ventilated barn on herd level are possible.
Keywords: GHG, CH4, NH3, Cattle, Mitigation strategy
INTRODUCTION: Livestock production systems in agriculture are one of the major emitters of climate-related trace gases. As livestock densities will increase regionally, mitigation strategies are required to reduce ammonia (NH3) emissions which affect surrounding ecosystems as well as emissions of the greenhouse gases (GHG) methane (CH4) and nitrous oxide (N2O). Extensive animal experimentation on individual animals in respiration chambers has already been carried out to evaluate the potential of dietary changes and opportunities to mitigate CH4 emissions from ruminants. The efficiency of mitigation measures of emissions, such as feeding strategies, from naturally ventilated animal buildings is difficult to quantify (Samer et al., 2011). Measurements under practical conditions in the barn environment are necessary and of great interest.
However, the determination of the ventilation rate in naturally ventilated barns is a big
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acoustic multi-gas analyser (1412 and multiplexer 1303; LumaSense Technologies SA, Ballerup, Denmark) (Schmithausen et al., 2016). To estimate emission rates, the ventilation rates of the barn were calculated by using the CO2 balance method. Wind velocity and direction were measured and used as criteria for exclusion of data. Due to the natural ventilation of the barn, location- and building-specific characteristics as well as current weather conditions had to be considered to allow quantification of the air exchange rate. Data of gas concentrations were only used for calculating emission rates if wind velocity and wind direction guaranteed that no cross contamination from neighbouring sectors within the barn or surrounding buildings of the experimental station could have occurred (Schmithausen et al., submitted).
During experimental time (169 days), 1% and 3% (of ration dry matter) of an extract rich in condensed tannins (CT) (from Acacia mearnsii) were added during four different periods to daily fodder rations of 48 dairy cows, whereas the control group received the regular diet (Gerlach et al., submitted). Variables such as individual values per cow (e.g., dry matter intake) as well as real-time and simultaneous detection of the gas concentrations for two feeding groups (section 1 ≙ control group; section 2 ≙ experimental group) each with 48 lactating German Holstein cows within one experimental barn (case-control) were considered.
2. RESULTS AND DISCUSSION: For calculation of the ventilation rate only values with defined data classification criteria (DCC) were used to obtain representative data.
Minimum criteria e.g. were a wind velocity of ≥ 0.7 m s-1 and an hourly mean wind direction from an angle of 210° to 300° (Figure 1). The hourly values were summarised to weekly means because of several days in which no measured data fulfilled the minimum criteria. Furthermore, management interventions such as milking time and homogenisation of slurry underneath the slatted floors were excluded from calculation.
Figure 1 shows the measurement frequencies in weeks with also irregularly distributed data over the timeline. Over the period of 24 weeks only 18% of the overall one-hour values fulfilled the named criteria of wind direction and wind velocity. Even long-term measurements showed periods when measured data were unsuitable for a naturally ventilated barn with a prevailing wind direction. The predefined DCC provided reliable results when applied to a feeding trial that was conducted with the aim to mitigate emissions of NH3 and the GHG CH4.
Figure 2 shows the air exchange rates of section 1 and section 2 depending on the measured wind velocity at the barn. The measured wind velocity was highly correlated with the calculated air exchange rates (R² > 0.85) as a function of the opening position of the curtains at the side wall of the barn. The logarithmised values show an air exchange rate even during windless conditions. However, these thermic effects were not taken into consideration in this investigation because of methodical reasons such as the minimum wind velocity of 0.7 m s-1 (Figure 2).
Measurement methods
Figure 1. Time axis over 24 weeks with hourly mean values of wind velocity (solid line) in the lower part and wind direction (open circle) between 210° and 300° in the upper part of the figure.
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Consequently, measurements of trace gas emissions from naturally ventilated dairy barns should be carried out as long as necessary to generate reliable values. Average emission rates of CH4 per livestock unit showed no difference between experimental- and control group (P > 0.05). When feeding 1% CT in ration dry matter no differences in NH3 emissions were detected (P > 0.05). However, during feeding 3% of CT NH3
emissions of the experimental group decreased by 34.5% in comparison to the control group. Differences between both groups were quantifiable on herd level. Furthermore, additional long-term measurements are needed considering defined characteristics and sufficient frequency of data acquisition to get reliable data.
3. CONCLUSION: The present study showed long-term measurements of CH4 and NH3
emissions in a naturally ventilated dairy barn. It was possible to quantify emission rates on barn level and to examine feed-related mitigation strategies. It was recognised that determination of the ventilation rate by using defined criteria of wind velocity and wind direction generates more reliable data for naturally ventilated buildings. However, it requires further long-term measurements implemented as long as necessary.
Acknowledgements. We are grateful for the cooperation of the Chamber of Agriculture of North Rhine-Westphalia and the team of the research facility Haus Riswick, where the measurements were carried out. This investigation was funded by the Landwirtschaftliche Rentenbank (Z-20039/-7) and the German Research Foundation (DFG; BU 1235/8-1), Germany. This research was partly conducted by members of the Center of Integrated Dairy Research (CIDRe), University of Bonn (Bonn, Germany).
REFERENCES:
Gerlach K., Pries M., Tholen E., Schmithausen A.J., Büscher W., Südekum K.-H., submitted. Effect of condensed tannins in rations of lactating dairy cows on production variables and nitrogen use efficiency.
Samer M., Ammon C., Loebsin C., Fiedler M., Berg W., Sanftleben P., Brunsch R., 2012.
Moisture balance and tracer gas technique for ventilation rates measurement and greenhouse gases and ammonia emissions quantification in naturally ventilated buildings. Building and Environment, 50, 10-20.
Samer M., Loebsin C., Fiedler M., Ammon C., Berg W., Sanftleben P., Brunsch, R., 2011.
Heat balance and tracer gas technique for airflow rates measurement and gaseous emissions quantification in naturally ventilated livestock buildings. Energy and Buildings, 43, 3718-3728.
Schmithausen A.J., Schiefler I., Trimborn M., Gerlach K., Südekum K.-H., Pries M., Büscher W., submitted. Quantification of methane and ammonia emissions in a naturally ventilated barn in response to supplementation of condensed tannins to a lactating dairy cow ration.
Schmithausen A.J., Trimborn M., Büscher W., 2016. Methodological Comparison between a Novel Automatic Sampling System for Gas Chromatography versus Photoacoustic Spectroscopy for Measuring Greenhouse Gas Emissions under Field Conditions. Sensors, 16, 1638.
Measurement methods
AMMONIA EMISSION MEASUREMENTS OF AN INTENSIVELY GRAZED PASTURE VOGLMEIER, K.1,2, HÄNI, C.3, JOCHER, M.1,AMMANN, C.1
1 Agroscope Research Station, Climate and Air Pollution, Switzerland
2 ETH Zürich, Institute of Agricultural Sciences, Switzerland;
3 Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, Switzerland
ABSTRACT: The quantification of ammonia (NH3) emissions at ambient air conditions is still a challenge and emission factors for ammonia have therefore a large uncertainty.
We present first results of a pasture experiment carried out in western Switzerland in 2016. During the measurement campaign, the pasture was grazed by 24 dairy cows in an intensive rotational management. NH3 concentrations were measured with line-integrating open-path instruments. The NH3 emission fluxes were calculated by applying a backward Lagrangian Stochastic dispersion model (bLS) to the difference of paired concentration measurements upwind and downwind of a grazed sub-plot. The instruments were able to retrieve small horizontal concentration differences (as small as 0.5 µg NH3 m-3) and the resulting fluxes were within a range of 0 to 3 µg N-NH3 m-2 s-1. We found, that the fluxes decreased to values below 0.5 µg N-NH3 m-2 s-1 typically within 48 hours. First flux evaluation showed, that rain events during the grazing period had a major effect on the cumulative emissions.
Keywords: NH3, emission, flux measurements, pasture, grazing, bLS, micrometeoro-logical methods
INTRODUCTION: Agricultural livestock production is the main source of air pollution by ammonia. Grazing is one efficient mitigation option to reduce NH3. However NH3
emission experiments over grazed pastures are rare and the available studies reported a large range of emissions factors (2.7 to 25.7% of excreted urine nitrogen (N); Bussink, 1992, Laubach et al., 2013). Many of the studies used manual applied urine and measured the emissions with chamber or wind tunnel methods. These techniques might lead to questionable results due to the altering of the environment and the high heterogeneity of the emissions. Sintermann et al. (2016) showed that line integrated ammonia concentrations can be quantified using open-path MiniDOAS systems. They also suggested that paired systems together with a dispersion model can be used to estimate emissions of a grazed system. This might lead to a better characterization of the emissions compared to previous methods. Häni et al. (this issue) already estimated
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system. Usually twice a day the cows were brought to the nearby barn for milking. The whole pasture was divided into two separate systems (north and south) where each system was divided into 11 paddocks resulting in a rotation period of about 20 days, depending on the grass condition. The herd for each system consisted of 12 dairy cows.
The rotation was managed synchronously on both systems and the main measurement campaign took place between May 2016 and October 2016. Monitoring of dung and urine patches on the paddock allowed for the quantification of excreted nitrogen (see also Ammann et al. (this issue).
1.2. Meteorological measurements: For the characterization of turbulent mixing the 3-dimensional wind velocity (u,v,w) and air temperature was measured at 10 Hz using an ultra-sonic anemometer-thermometer (HS-50, Gill Instruments Ltd., UK) mounted at 2 m above ground. Each system (north and south) was equipped with one of those anemometers. Further weather parameters (e.g. global radiation, precipitation) were measured with a standard automated weather station (Campell Scientific Ltd., UK) installed at the northern field.
1.3. Ammonia measurements: Line-integrated ammonia concentrations were measured using four MiniDOAS systems as specified in Sintermann et al. (2016). These open-path instruments make use of the differential optical absorption in the UV range. Two MiniDOAS systems (namely S5 and S2) were installed at the northern field and two instruments (S1 and S6) on the southern field, respectively. All instruments were installed at a height of about 1.3 m. Each MiniDOAS pair (e.g. S5 and S2) was separated by a horizontal distance of about 30 m which allowed for concentration measurements upwind and downwind of the paddock in between. The single light path between the sensor and the reflector for the individual devices had a length of 30 to 35 m. The instruments reported NH3 concentration at a temporal resolution of one minute. The one minute data were averaged to 30-minute values for further processing.
1. 4. Emission calculation: We used an open source R-version of the bLS dispersion model (bLSmodelR, Häni, 2016; based on Flesch et al., 2004) to relate the measured 30-minute concentration difference to the unknown source strength E of the emitting paddocks (see Eq. 1). The dispersion coefficient D was determined based on the simulated movement of many thousand fluid particles released at the sensor line positions and tracked backwards in time till their eventual touchdown on the specified source area.
(1) (1)
The bLS program uses wind and turbulence information measured by the sonic anemometer. In order to calculate a concentration footprint for each 30-minute period, we used averaged data of the wind direction, the standard deviations of the wind components, the friction velocity and values representing the surface roughness.
2. RESULTS AND DISCUSSION:
D C E CDownwind − Upwind
=
Measurement methods
2.1. Concentration results: Due to different problems (power supply, software issues…) the MiniDOAS systems were not running continuously during the grazing period. At the northern field, simultaneous concentration measurements of both systems could be achieved for five management rotations, at the southern field for four management rotations. Figure 1 shows an example of concentration measurements at the northern field during grazing (grey shaded area) and a few days afterwards of the sub-plot in between the MiniDOAS instruments. The concentration measurements yielded values between close to zero and more than 80 µg NH3 m-3. Depending on wind and atmospheric stability, the concentrations showed a strong temporal variation. The highest concentrations were usually observed during low wind conditions, which prevented an efficient mixing of the boundary layer. During periods with well-developed turbulence and favorable wind direction (no advection from the farm buildings) the horizontal concentration difference between paired MiniDOAS instruments was generally highest shortly after the cows left the monitored paddock with values up to 10 µg NH3 m-3. Typically the concentration difference decreased significantly within the first 48 hours to values less than 10-20 % in relation to the maximum measured concentration difference. Throughout the measurement campaign, the MiniDOAS instruments showed a high accuracy in the measurements. Concentration differences down to values of about 0.5 µg NH3 m-3 could be detected with sufficient precision.
Figure 12. Measured ammonia concentration (top panel) of the MiniDOAS systems S2 and S5. The black dots of the bottom panel show the concentration differences during favorable wind conditions whereas
Emissions of Gas and Dust from Livestock – Saint-Malo, France – May 21-24, 2017 148
regressions to the measured data points in order to account for missing or excluded data. Depending on the weather and turbulence conditions the highest emissions were observed at the end of the grazing period. The maximum fluxes observed during the grazing periods were in the range of 1.0 to 3.5 µg N-NH3 m-2 s-1. Rain events during the grazing period significantly reduced the emissions and subsequently resulted in less cumulative emissions. Typical accumulated emissions after 5 days were in the order of 200 to 350 µg N-NH3, depending on weather conditions and the time the cows spend on the paddock.
3. CONCLUSION: During a field campaign in western Switzerland in 2016 we tested the performance of the open–path MiniDOAS instruments to estimate NH3 emissions of a grazing system. We found that the instruments worked very well and that they reported stable and plausible NH3 concentration measurements throughout the field campaign.
The emissions were calculated using the measured concentration differences upwind and downwind of the emitting paddock and the dispersion coefficient modeled by bLS.
As expected, the highest emissions were observed at the end of the grazing period.
These emissions dropped to very low values usually within the first 48 hours after. Rain events during the grazing period resulted in decreased cumulative emissions after 5 days.
Acknowledgements. The financial support through project grants of the Swiss National Science Foundation (project NICEGRAS) is gratefully acknowledged. We also thank the many colleagues that supported the field measurements.
REFERENCES:
Ammann, C., Voglmeier, K., Häni, C., Jocher, M., 2017. Quantification of small scale nitrous oxide emissions and comparison with field-scale emissions of a rotational grazing system. This issue
Bussink D.W., 1992. Ammonia volatilization from grassland receiving nitrogen fertilizer and grazed by dairy cattle. Fertilizer Research, 33, 257-265
Flesch T.K., Wilson J.D., Harper L.A., Crenna B.P., Sharpe R.R., 2004. Deducing Ground-to-Air Emissions from Observed Trace Gas Concentrations: A Field Trial. J. Appl.
Meteorol., 43(3), 487–502.
Häni, C.: bLSmodelR - An atmospheric dispersion model in R. R package version 2.4.1.
URL: http://www.agrammon.ch/documents-to-download/blsmodelr/, last access: 21 April 2017.
Häni, C., Voglmeier, K., Jocher, M., Ammann, C., Neftel, A., Kupper, T.,2017. Evaluation of backward Lagrangian Stochastic dispersion modelling for NH3: Including a dry deposition Algorithm. This issue
Laubach J., Taghizadeh-Toosib A., Gibbs S.J., Sherlock R.R., Kelliher F.M., Grover S.P.P., 2013. Ammonia emissions from cattle urine and dung excreted on pasture.
Biogeosciences, 10, 327–338
Sintermann J., Dietrich K., Häni C., Bell M., Jocher M., Neftel A., 2016. A miniDOAS instrument optimised for ammonia field measurements. Atmos. Meas. Tech., 9(6), 2721–2734.
Measurement methods
International Symposium on
Emission of Gas and Dust from Livestock
May 21-24, 2017 Saint-Malo, France