This material is based upon work supported by the National Science Foundation under Grant Number 1215858, by the US Department of Agriculture under Grant Number 2013-68002-20525, and by the US Department of Energy Great Lakes Bioenergy Research Center &#8211; DOE BER Office of Science (DE-FC02-07ER64494) and DOE OBP Office of Energy Efficiency and Renewable Energy (DE-AC05-76RL01830). In-field video and images were recorded at the Wisconsin Integrated Cropping System Trial project of the University of Wisconsin&#8211;Madison. The authors are grateful to Ryan Curtin for skillful videography and editing.

1. Chamber Construction and Anchor Installation
<ol>
	<li>Design and construct chambers &#8211; each consisting of an anchor that is inserted into the soil and a lid that is placed on top of the anchor during flux measurement &#8211; to meet experimental needs.
	<ol>
		<li>In designing chamber shape and size, consider spatial factors such as crop row spacing, fertilizer or manure banding, and plant height. Because protrusion of anchors above the soil surface can contribute to microclimate effects and water ponding, consider having the lids sit as low to the soil surface as possible. Because tradeoffs exist between chamber height and detection sensitivity, design lids to be as short as is feasible for the system under study.</li>
		<li>Build chambers of sturdy, nonreactive material such as stainless steel or PVC, and include a mechanism for sealing the lid onto the anchor. Insulate lids and cover with light-colored or reflective material to prevent heat buildup during measurement. Include a septum to allow sample collection and a vent tube to prevent pressure perturbations during chamber deployment and sample removal. For additional details refer to the Materials table, Parkin and Venterea8, and Clough et al10.</li>
	</ol>
	</li>
	<li>At least 1 day prior to sampling, install chamber anchors in the soil at desired sites. The installation method will depend on chamber design, but in general, apply even pressure across all points so that the anchor does not warp or distort the soil structure. Sink the anchor to a depth of 2.5-13 cm depending on soil type, deployment time, and chamber volume6,11. Leave as little as possible (no more than 5 cm) protruding above the soil surface.</li>
</ol>
2. Calibration and Experimental Design
Note: Prior to beginning the experiment, follow these steps to determine an appropriate sampling time course that will allow data to be fit to an appropriate linear or non-linear flux model (see Parkin et al.12). This will require the use of techniques described in steps 3-5 (Field Sampling, Sample Analysis, and Data Analysis). Optimal timing is a function of both the system under study and the dimensions of chambers being used. Some trial and error may be involved. See Venterea13 for alternate approaches.
<ol>
	<li>Calibration Sampling and Analysis
	<ol>
		<li>Under environmental or management conditions expected to generate relatively high trace gas fluxes, conduct intensive sampling following techniques described in section 3. Using tightly spaced sampling time points, populate a time series of longer duration than would be considered typical. Begin by sampling from several representative chambers at 5-10 evenly spaced time points over the course of an hour.</li>
		<li>Analyze samples by gas chromatography following section 4.</li>
	</ol>
	</li>
	<li>Calibration Interpretation
	<ol>
		<li>For each calibration time series and each gas of interest, plot time-by-concentration.</li>
		<li>Verify that flux rates are on the high end of the expected range. See section 5 for flux calculation. Refer to section 2.3 for troubleshooting tips.</li>
		<li>Inspect graphs for signs of non-linearity, or more specifically, plateauing of gas concentrations over time. 
		Note: The point at which concentration begins to plateau differs by gas type, and is a function of the rate of gas production or consumption within the soil, the concentration of the gas in the chamber headspace, and diffusion between the two zones. It is therefore strongly affected by chamber height, with shorter chambers yielding shorter time before plateau.</li>
		<li>Use the calibration sets to determine optimal chamber deployment time for the experimental system. If linear regression will be used in data analysis (as described here in section 5), select timing that maintains as close to a linear relationship as possible between time and concentration for all gases / systems of interest, while allowing for a minimum of three, preferably four, sampling times within the time series6. For chambers 10-30 cm high used for CO2 and N2O measurements, time series typically range from 20-60 min8,14.</li>
	</ol>
	</li>
	<li>Calibration Troubleshooting
	<ol>
		<li>If there is poor differentiation and/or difficulty discerning linearity or plateau, use tighter calibration time points or longer calibration time series, and check that concentrations are within detection limits. For low rates of flux, a reduction in rate of accumulation may not be observed within the tested timeframe. This should not cause concern.</li>
		<li>If the fluxes are not at the high end of the expected experimental range, repeat calibration, altering treatment or environmental conditions to induce higher flux (by applying fertilizer or irrigation, for example). Alternately, use at least four time points in experimental design, so that if experimental fluxes are significantly higher than those observed during calibration and plateauing does occur, later time points can be excluded while retaining at least three time points for linear regression. Curvilinear regression approaches may also be employed.</li>
	</ol>
	</li>
	<li>Experimental Design
	<ol>
		<li>Based on the optimal timing determined in section 2.2.4, devise an overall sampling scheme that captures all relevant sites, treatments, and/or replicates, and allows personnel to move through chamber sites efficiently. If necessary, divide the chamber sites into several &#8220;rounds&#8221; to be sampled one after the other.
		<ol>
			<li>If measurements are to be taken as representative of a whole day, sample at a time of day when temperatures are moderate relative to daily extremes. In typical temperate cropping systems, the ideal window is mid- to late-morning.</li>
			<li>If samples are to be collected in consecutive rounds, be careful not to introduce a bias by repeatedly sampling the same treatments at the same time of day. Construct rounds out of blocks of replicates rather than treatment-by-treatment.</li>
			<li>Include time for any necessary ancillary measures to be taken either within rounds or before/after, as appropriate. (See section 3.3 for typical ancillary measures.)</li>
			<li>Optionally, include time for collection of ambient air samples for use in non-linear flux models, or as an approximation of starting (time zero, &#8220;T0&#8221;) concentration (not described here).</li>
			<li>Optionally, include time for loading reference gas into vials at the time of sampling to assess possible sample degradation between sampling and analysis. See Parkin and Venterea8 for sample storage considerations.</li>
		</ol>
		</li>
		<li>Determine the frequency of flux measurements that is appropriate for research goals. This may range from a single measurement to daily, weekly, or periodic measurements over the course of months or years. Refer to Rochette et al.14 for a thorough discussion of experimental design considerations.</li>
		<li>If samples are to be collected in cold conditions, plan for inclusion of a warming device such as a hot pack with vials to prevent septa from becoming brittle.</li>
	</ol>
	</li>
</ol>
3. Field Sampling
Note: On each sampling date, follow the sampling scheme established in section 2.4, using the techniques described below. Equipment and sample volume can vary depending on the collection and transfer methods being employed and the amount of sample required for GC analysis8. This protocol utilizes 5.9 ml collection vials and 30 ml syringes, with a flushing method of sample transfer. See Discussion for alternate approaches.
<ol>
	<li>Preparation
	<ol>
		<li>If sampling from multiple chambers per round, prepare a time point reference grid (see Figure 2) to easily track where and when to sample. Alternately, make arrangements to record each time point during sampling.</li>
		<li>Pre-label and arrange collection vials for maximal efficiency and minimal likelihood of confusion during sampling.</li>
		<li>In order to save time during sampling, prepare all materials and equipment beforehand. Include extras of anything that may break or is easily lost (needles, syringes, stopcocks, etc.), and place in a carry tote, bucket, or other container.</li>
		<li>Be prepared to record any delayed time points which can happen due to equipment malfunction or other unforeseen circumstances, and which can be easily corrected during data analysis by adjusting the time associated with a certain sample.</li>
	</ol>
	</li>
	<li>Sample Collection
	<ol>
		<li>Attach and seal the chamber lid to the pre-installed chamber anchor, and start a stopwatch. This is T0.</li>
		<li>Immediately after sealing the lid collect a sample of ambient air from a location adjacent to the chamber, at the approximate height of the chamber top: with an empty 30 ml syringe fitted with a needle and a stopcock in the open position, draw a 30 ml air sample and close the stopcock. This is the T0 sample. Alternatively, take the T0 sample from the chamber6. 
		Note: Tradeoffs exist between the two approaches &#8211; evaluate spatial (distance from site or external microclimate for outside samples) vs. timing (delay between lid closure and sample collection for inside samples) considerations and determine the most appropriate technique for the equipment being used and the system under study.</li>
		<li>With the syringe needle, pierce the septum of a 5.9 ml collection vial that already has another needle poked through near the edge of the septum.</li>
		<li>Open the syringe stopcock and inject approximately 20 ml of the sample into the vial (this causes the previous contents of the vial to be expelled through the extra needle, replaced by sample).</li>
		<li>In a smooth motion, remove the extra needle while continuing to inject as much of the remaining sample (approximately 10 ml) as possible, slightly over-pressurizing the vial to ensure sample integrity and allow analysis of multiple samples if necessary8.</li>
		<li>Close the stopcock and withdraw the syringe needle from the septum. Turn the filled vial upside-down to distinguish from unfilled vials.</li>
		<li>Proceed to the next chamber, repeat steps 3.2.1-3.2.6, sealing the lid on the correct pre-determined T0 time point.</li>
		<li>Continue to repeat steps 3.2.1-3.2.7 until all chambers in the round have been sealed and T0 samples have been collected.</li>
		<li>Return to the first chamber.</li>
		<li>As the time approaches 10 seconds until T1, pierce the septum in the chamber top with the syringe needle.</li>
		<li>Within a 10 second range of T1, withdraw a 30 ml sample of air from inside the chamber and close the stopcock. Remove the syringe needle from the chamber septum.</li>
		<li>Transfer the sample to a collection vial following steps 3.2.3-3.2.6.</li>
		<li>Continue to collect samples following steps 3.2.10-3.2.12, according to the sampling scheme established in section 2.4.</li>
	</ol>
	</li>
	<li>Ancillary Measures
	<ol>
		<li>In order to convert gas concentration to mass, measure the air temperature at the time of sampling. Depending on research goals, record or perform other ancillary measures such as soil temperature and soil moisture content at each location and/or time, daily rainfall, soil bulk density, soil nitrate and ammonium concentrations, etc. Various means exist to obtain these measures &#8211; follow standard protocols.</li>
		<li>Optionally, collect ambient air samples and/or load field standards of known concentrations into vials to assess ambient GHG concentrations and potential storage-vial degradation in the period between sampling and analysis (see sections 2.4.1.4 and 2.4.1.5).</li>
	</ol>
	</li>
</ol>
4. Sample Analysis
<ol>
	<li>Determine the concentration of gases of interest for each sample by gas chromatography, using equipment fitted with an electron capture detector for N2O, an infrared gas analyzer or thermal conductivity detector for CO2, and a flame ionization detector for CH4. 
	Note: It is essential to obtain access to an instrument that is properly configured for GHG analysis and has sufficient run time available. Principles and methods of gas chromatography are described elsewhere5,15,16.</li>
	<li>Convert trace gas concentration from volumetric to mass using the Ideal Gas Law:</li>
</ol>
PV = nRT
Where P = pressure, V = volume, n = moles of gas, R = gas law constant, and T = temperature. Thus:
<img alt="Equation 1" fo:content-width="4in" src="/files/ftp_upload/52110/52110eq1.jpg" width="400" />
5. Data Analysis
<ol>
	<li>For each time series, plot time-by-concentration and evaluate for linearity. Evaluate using goodness of fit or by visual inspection, excluding later time points showing signs of plateau from further analysis. Use a minimum of three time points including T0 for flux calculation (T0, T1, T2 &#8230;). Establish a consistent protocol, and reject any time series that fail to meet that protocol&#8217;s standards for linearity. See Parkin and Venterea8 for a thorough discussion of error, bias, and variance in flux calculation.</li>
	<li>Perform linear regression.</li>
	<li>Use the slope of the regression to calculate flux:</li>
</ol>
F = S &#8226; V &#8226; A-1
Where F = flux, S = slope of the regression, V = chamber volume, and A = chamber area. Thus:
<img alt="Equation 2" fo:content-width="1.3in" src="/files/ftp_upload/52110/52110eq2.jpg" width="130" />
Note: Refer to the Discussion and Parkin et al.12 for non-linear approaches to flux calculation.

<ol><li>Myhre, G., et al. Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aMyhre%2C+G+%22Contribution+of+Working+Group+I+to+the+Fifth+Assessment+Report+of+the+Intergovernmental+Panel+on+Climate+Change%22">Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change</a>. Stocker, T. F. , 8, Cambridge University Press. Cambridge, UK. (2013).</li>
<li>Denmead, O. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Approaches+to+measuring+fluxes+of+methane+and+nitrous+oxide+between+landscapes+and+the+atmosphere%5BTitle%5D)+AND+%22Plant+and+Soil%22%5BJournal%5D)">Approaches to measuring fluxes of methane and nitrous oxide between landscapes and the atmosphere.</a> Plant and Soil. 309 (1-2), 5-24 (2008).</li>
<li>Smith, K. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Micrometeorological+and+chamber+methods+for+measurement+of+nitrous+oxide+fluxes+between+soils+and+the+atmosphere%3A+Overview+and+conclusions%5BTitle%5D)+AND+%22Journal+of+Geophysical+Research%3A+Atmospheres%22%5BJournal%5D)">Micrometeorological and chamber methods for measurement of nitrous oxide fluxes between soils and the atmosphere: Overview and conclusions.</a> Journal of Geophysical Research: Atmospheres. 99 (1984-2012), 16541-16548 (1984).</li>
<li>Bouwman, A. F., Boumans, L. J. M., Batjes, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Emissions+of+N2O+and+NO+from+fertilized+fields%3A+Summary+of+available+measurement+data%5BTitle%5D)+AND+%22Global+Biogeochemical+Cycles%22%5BJournal%5D)">Emissions of N2O and NO from fertilized fields: Summary of available measurement data.</a> Global Biogeochemical Cycles. 16 (4), 1058(2002).</li>
<li>Hensen, A., Skiba, U., Famulari, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Low+cost+and+state+of+the+art+methods+to+measure+nitrous+oxide+emissions%5BTitle%5D)+AND+%22Environmental+Research+Letters%22%5BJournal%5D)">Low cost and state of the art methods to measure nitrous oxide emissions.</a> Environmental Research Letters. 8 (2), 025022(2013).</li>
<li>Rochette, P., Eriksen-Hamel, N. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Chamber+Measurements+of+Soil+Nitrous+Oxide+Flux%3A+Are+Absolute+Values+Reliable%5BTitle%5D)+AND+%22Soil+Science+Society+of+America+Journal%22%5BJournal%5D)">Chamber Measurements of Soil Nitrous Oxide Flux: Are Absolute Values Reliable.</a> Soil Science Society of America Journal. 72 (2), 331(2008).</li>
<li>Robertson, G. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Greenhouse+Gases+in+Intensive+Agriculture%3A+Contributions+of+Individual+Gases+to+the+Radiative+Forcing+of+the+Atmosphere%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Greenhouse Gases in Intensive Agriculture: Contributions of Individual Gases to the Radiative Forcing of the Atmosphere.</a> Science. 289 (5486), 1922-1925 (2000).</li>
<li>Parkin, T. B., Venterea, R. T. Chamber-Based Trace Gas Flux Measurements. In: Sampling Protocols. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aParkin%2C+TB+%22http%3A%2F%2Fwww.ars.usda.gov%2Fresearch%2FGRACEnet%22">http://www.ars.usda.gov/research/GRACEnet</a>. Follet, R. F. 3 (1), 3-39 (2010).</li>
<li>Klein, C., Harvey, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nitrous+Oxide+Chamber+Methodology+Guidelines%5BTitle%5D)+AND+%22Ministry+for+Primary+Industries%22%5BJournal%5D)">Nitrous Oxide Chamber Methodology Guidelines.</a> Ministry for Primary Industries. , Wellington, UK. 1-146 (2013).</li>
<li>Clough, T. J., Rochette, P., Thomas, S. M., Pihlatie, M., Christiansen, J. R., Thorman, R. E. Chamber Design. In: Nitrous Oxide Chamber Methodology Guidelines. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aClough%2C+TJ+%22Ministry+for+Primary+Industries%22">Ministry for Primary Industries</a>. deKlein, C., Harvey, M. , 2, Wellington, UK. (2013).</li>
<li>Hutchinson, G. L., Livingston, G. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Vents+and+seals+in+non-steady-state+chambers+used+for+measuring+gas+exchange+between+soil+and+the+atmosphere%5BTitle%5D)+AND+%22European+Journal+of+Soil+Science%22%5BJournal%5D)">Vents and seals in non-steady-state chambers used for measuring gas exchange between soil and the atmosphere.</a> European Journal of Soil Science. 52 (4), 675-682 (2001).</li>
<li>Parkin, T. B., Venterea, R. T., Hargreaves, S. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Calculating+the+Detection+Limits+of+Chamber-based+Soil+Greenhouse+Gas+Flux+Measurements%5BTitle%5D)+AND+%22Journal+of+Environment+Quality%22%5BJournal%5D)">Calculating the Detection Limits of Chamber-based Soil Greenhouse Gas Flux Measurements.</a> Journal of Environment Quality. 41 (3), 705(2012).</li>
<li>Venterea, R. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Simplified+Method+for+Quantifying+Theoretical+Underestimation+of+Chamber-Based+Trace+Gas+Fluxes%5BTitle%5D)+AND+%22Journal+of+Environment+Quality%22%5BJournal%5D)">Simplified Method for Quantifying Theoretical Underestimation of Chamber-Based Trace Gas Fluxes.</a> Journal of Environment Quality. 39 (1), 126(2010).</li>
<li>Rochette, P., Chadwick, D. R., de Klein, C. Deployment Protocol. In: Nitrous Oxide Chamber Methodolog Guidelines. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aRochette%2C+P+%22Ministry+for+Primary+Industries%22">Ministry for Primary Industries</a>. deKlein, C., Harvey, M. (3), Wellington, UK. (2013).</li>
<li><a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fundamentals+of+Gas+Chromatography%5BTitle%5D)+AND+%22Agilent+Technologies%2C+Inc%22%5BJournal%5D)">Fundamentals of Gas Chromatography.</a> Agilent Technologies, Inc. , 1-60 (2002).</li>
<li>Holland, E. A., Robertson, G. P., Greenberg, J., Groffman, P. M., Boone, R. D., Gosz, J. R. Soil CO2, N2O, and CH4 Exchange. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aHolland%2C+EA+%22Standard+Soil+Methods+for+Long-Term+Ecological+Research%22">Standard Soil Methods for Long-Term Ecological Research</a>. Robertson, G. P., Coleman, D. C., Bledsoe, C. S. , Oxford University Press. New York, NY. (1999).</li>
<li>Venterea, R. T., Burger, M., Spokas, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nitrogen+Oxide+and+Methane+Emissions+under+Varying+Tillage+and+Fertilizer+Management%5BTitle%5D)+AND+%22Journal+of+Environment+Quality%22%5BJournal%5D)">Nitrogen Oxide and Methane Emissions under Varying Tillage and Fertilizer Management.</a> Journal of Environment Quality. 34 (5), 1467(2005).</li>
<li>Hutchinson, G. L., Mosier, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Improved+soil+cover+method+for+field+measurement+of+nitrous+oxide+fluxes%5BTitle%5D)+AND+%22Soil+Science+Society+of+America+Journal%22%5BJournal%5D)">Improved soil cover method for field measurement of nitrous oxide fluxes.</a> Soil Science Society of America Journal. 45 (2), 311-316 (1981).</li>
<li>Pedersen, A. R., Petersen, S. O., Vinther, F. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stochastic+diffusion+model+for+estimating+trace+gas+emissions+with+static+chambers%5BTitle%5D)+AND+%22Soil+Science+Society+of+America+Journal%22%5BJournal%5D)">Stochastic diffusion model for estimating trace gas emissions with static chambers.</a> Soil Science Society of America Journal. 65, 49-58 (2001).</li>
<li>Pedersen, A. R., Peterson, S. O., Schelde, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+comprehensive+approach+to+soil-atmosphere+trace-gas+flux+estimation+with+static+chambers%5BTitle%5D)+AND+%22European+Journal+of+Soil+Science%22%5BJournal%5D)">A comprehensive approach to soil-atmosphere trace-gas flux estimation with static chambers.</a> European Journal of Soil Science. 61, 888-902 (2010).</li>
<li>Wagner, S. W., Reicosky, D. C., Alessi, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Regression+models+for+calculating+gas+fluxes+measured+with+a+closed+chamber%5BTitle%5D)+AND+%22Agronomy+Journal%22%5BJournal%5D)">Regression models for calculating gas fluxes measured with a closed chamber.</a> Agronomy Journal. 89, 279-284 (1997).</li>
<li>Livingston, G. P., Hutchinson, G. L., Spartalian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Trace+gas+emission+in+chambers%5BTitle%5D)+AND+%22Soil+Science+Society+of+America+Journal%22%5BJournal%5D)">Trace gas emission in chambers.</a> Soil Science Society of America Journal. 70, 1459-1469 (2006).</li>
<li>Venterea, R. T., Parkin, T. B., Cardenas, L., Petersen, S. O., Pedersen, A. R. Data Analysis Considerations. In: Nitrous Oxide Chamber Methodology Guidelines. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aVenterea%2C+RT+%22Ministry+for+Primary+Industries%22">Ministry for Primary Industries</a>. deKlein, C., Harvey, M. (6), Wellington, UK. (2013).</li>
<li>Grace, P., van der Weerden, T. J., Kelly, K., Rees, R. M., Skiba, U. M. Automated Greenhouse Gas Measurement in the Field. In: Nitrous Oxide Chamber Methodology Guidelines. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aGrace%2C+P+%22Ministry+for+Primary+Industries%22">Ministry for Primary Industries</a>. deKlein, C., Harvey, M. , 5, Wellington, UK. (2013).</li>
<li>Alfaro, M. A., Giltrap, D., Topp, C. F. E., de Klein, C. How to Report Your Experimental Data. In: Nitrous Oxide Chamber Methodology Guidelines. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aAlfaro%2C+MA+%22Ministry+for+Primary+Industries%22">Ministry for Primary Industries</a>. deKlein, C., Harvey, M. , 7, Wellington, UK. (2013).</li>
</ol>

The authors have nothing to disclose.

The static chamber-based approach described here is an efficient method for measurement of GHG flux from soil systems. The relative simplicity of its components makes it especially well suited to conditions or systems in which more infrastructure-intensive methods are infeasible. In order to generate high quality data, however, the static chamber approach must be carried out with strict attention to experimental design6. One notable consideration that must be taken into account is the spatial variability of soil gas fluxes, which can result in high variability among replicate chamber-based measurements. In designing experiments, therefore, it is important to include enough replicates to provide adequate power for statistical analysis. Tradeoffs may exist between the number of treatments which can be studied while maintaining sufficient replication, and a minimum of four replicates per treatment is a general guideline14.
If measured fluxes will be used to estimate daily emissions, diurnal variations in air temperature, soil temperature, and gas emissions must be taken into account. If research goals require measurements to be obtained in mid-morning when temperatures reflect daily averages, the restricted window for sampling may affect the number of chambers that can feasibly be monitored. An additional consideration to be evaluated is the impact that inclusion or exclusion of plant roots and above ground biomass will have on gas fluxes. Chamber placement relative to plant tissue will impact the interpretation of flux data, particularly in the case of CO2 where not only microbial respiration but also root and shoot respiration and photosynthesis must be appropriately balanced. For additional discussion of these factors, see Parkin and Venterea8.
As noted previously, many variations on this methodology exist, including chamber design and sampling volume. One such variation is in the method employed to transfer samples between the syringe and collection vial. The technique described here first flushes the collection vial with sample before filling the vial to positive pressure5. A more commonly used technique is the transfer of samples from syringes to vials that have been pre-evacuated using a vacuum pump, and the use of non-evacuated vials without flushing has also been reported8,17. Another significant point where a range of approaches exists is in data analysis and the selection of the flux model most appropriate to the system under study. In addition to the linear regression method described here, non-linear models may also be employed, particularly when longer deployment times are used. These models include the algorithm developed by Hutchinson and Mosier18 and derivations thereof19,20, the quadratic procedure described by Wagner et al.21, and the non-steady-state diffusive flux estimator described by Livingston et al22. For a thorough discussion of non-linear flux models, refer to Parkin et al.12 and Venterea et al23.
Methods similar to the static chamber approach include the use of flow-through measurement systems with Fourier transfer infrared (FTIR) spectrometry as an alternate to syringe sampling and gas chromatography, as well as automation of chamber closure and sampling through various means. Automated systems enable more frequent measurements with reduced personnel, but also require additional infrastructure investments. Grace et al.24 provide an extensive summary of options and tradeoffs in automated chamber-based N2O measurement.
Characterization of greenhouse gas flux from both managed and natural systems is important to inform process-based models, understand the impacts of management practices and inform mitigation strategies, and to support global accounting and climate change modeling. Thus while individual studies are informative at the local scale, much additional value is derived through contributing to, and drawing from, a global body of knowledge on gas exchange between the landscape and the atmosphere. It is key, therefore, that data be collected and reported in a way that ensures longevity and interoperability with the broader knowledge base. This includes following best practices to ensure data quality, as well as collection of ancillary measures and comprehensive reporting of metadata to allow extension of findings beyond discrete studies. Excellent guidelines for data reporting are available from the GRACEnet project and the GRA25.

Understanding the contributions of both human activities and natural systems to radiative properties of the atmosphere is an area of critical importance as we strive to mitigate anthropogenic contributions to the greenhouse effect. In addition to carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) are also potent GHGs, accounting for an estimated 7% and 19% of global warming, respectively, with the majority of emissions coming from landscape sources1,2. These range from managed systems such as agricultural fields, rice paddies, and landfills, to natural systems such as forest floors, wetlands, and termite mounds. Accurate measurement, supporting well-informed modeling of such landscape-based emissions is critical in order to understand the drivers of climate change as well as to identify mitigation opportunities.
A variety of greenhouse gas measurement strategies exist, each with their own strengths and weaknesses2-5. Mass balance techniques rely on wind-based dispersion of gases and are suited to measurement of flux from small, well-defined sources such as landfills and animal paddocks. Micrometeorological approaches such as eddy covariance are based on real-time direct measurement of vertical gas flux, and can provide direct measurements over large areas. However, homogeneity in source topography is an implicit assumption (in that measurements yield a mean for the area under study), and costly infrastructure can limit deployment possibilities. Finally, chamber-based methods focus on change in gas concentration at the soil surface by sampling from a restricted above ground headspace. They allow measurements to be obtained from small areas and numerous treatments, but are subject to high coefficients of variation due to spatial variation in soil gas flux.
Here we discuss the most prevalent and easily implemented form of chamber-based measurement, utilizing the type of closed chambers without air flow-through commonly referred to as &#8220;static&#8221; or &#8220;non-steady-state non-flow-through&#8221; chambers. In this approach, gas emissions from the soil surface are trapped within a vented chamber, and rates of flux are determined by measuring the change in gas concentration over time within the chamber headspace. The static chamber technique has been widely deployed across both managed and natural landscapes and underpins the bulk of data reporting soil-based flux of greenhouse gases, particularly N2O6,7. It is ideally suited to the study of small experimental plots, diverse sites over variable terrain, or in other situations where multiple distinct locations must be studied without significant infrastructure investments. Typical experimental uses might include the exploration of alternative landscape management practices and their impact on soil-based CO2, N2O, and/or CH4 emissions, examination of landscape-based flux dynamics under artificially induced climate change scenarios such as warming and rainfall exclusion/supplementation, or the descriptive study of natural and agricultural ecosystems and subsystems.
As a critical tool in GHG measurement and flux estimation, the static chamber method has been thoroughly evaluated, and significant efforts have been made towards standardization of techniques and harmonization of data reporting4,6,8,9. Of particular note are the detailed reviews and guidelines produced by the U.S. Department of Agriculture &#8211; Agricultural Research Service&#8217;s Greenhouse gas Reduction through Agricultural Carbon Enhancement network (GRACEnet)8 and by the Global Research Alliance on Agricultural Greenhouse Gases (GRA)9. Such guidelines provide an invaluable resource and platform for coordination, as ultimately the interoperability of data from a myriad of studies is critical for scaling up local findings to global modeling, and for translating research results into viable mitigation strategies.
GRACEnet, GRA, and other reviews also highlight the fact that specific techniques in static chamber-based greenhouse gas flux measurement are extremely diverse, with significant methodological variations possible at nearly every step of the way, including chamber design, temporal and spatial deployment, sampling volumes, sample analysis, and flux calculations. The method described here presents one possible variant, while showcasing best practices and highlighting critical considerations for the generation of high quality, broadly transferrable data. It is intended to provide an accessible overview of this standardized procedure, and a platform from which to explore further nuances and variations described in the literature.

<table><tbody><tr><td>5.9 ml soda glass flat bottom 55 x 15.5 mm</td><td>Labco Limited</td><td>719W</td><td>Collection vials</td></tr><tr><td>16.5 mm screw caps with pierceable rubber septum</td><td>Labco Limited</td><td>VC309</td><td>Caps for&amp;nbsp; vials</td></tr><tr><td>90-well plastic vial rack, 17.1 mm well I.D.</td><td>Wheaton</td><td>868810</td><td>Rack for organizing vials</td></tr><tr><td>Regular bevel needles 23 G x 1&quot;</td><td>BD</td><td>305193</td><td>Needles for sample collection</td></tr><tr><td>Stopcocks with luer connections, 1-way, male slip</td><td>Cole-Parmer</td><td>EW-30600-01</td><td>Stopcocks for syringes</td></tr><tr><td>30 ml syringe, slip tip</td><td>BD</td><td>309651</td><td>Syringes for sample collection</td></tr><tr><td>Stopwatch or timer</td><td>Various</td><td>N/A</td><td>For timing field sampling</td></tr><tr><td>Stainless steel or galvanized utility pans with rim, or fabricated stainless steel or PVC chambers and lids, dimensions as appropriate to experimental system</td><td>Various</td><td>N/A</td><td>Chamber anchor and lid - bottom cut out of anchor, holes for septum and vent tubing bored in lid</td></tr><tr><td>Gray butyl stoppers 20 mm</td><td>Wheaton</td><td>W224100-173</td><td>Chamber septa for syringe sampling - insert into hole bored in lid top</td></tr><tr><td>Tygon tubing 4.0 mm I.D. x 5.6 mm O.D.</td><td>Sigma-Aldrich</td><td>Z685623</td><td>Chamber vent tubing - insert in hole bored in lid side, flush with exterior, approximately 25 cm coiled in lid interior (a 1 ml syringe tip may be used as an attachment mechanism)</td></tr><tr><td>Adhesive foam rubber tape or HDPE O-ring</td><td>Various</td><td>N/A</td><td>Chamber sealing mechanism - fastened to underside of lid rim</td></tr><tr><td>Reflective&amp;nbsp; insulation, 0.3125&quot; thickness</td><td>Lowe&#039;s</td><td>409818</td><td>Insulating and reflective coating - affix to exterior of chamber lid</td></tr><tr><td>Large metal binder clips, 2&quot; size with 1&quot; capacity, or manufactured draw latch as appropriate</td><td>Staples / McMaster</td><td>831610 (Staples) / 1863A21 (McMaster)</td><td>Lid attachment mechanism - for clamping lid to anchor during sampling</td></tr><tr><td>Gas chromatography equipment fitted with electron capture detector for nitrous oxide, infrared gas analyzer or thermal conductivity detector for carbon dioxide, flame ionization detector for methane</td><td>Various</td><td>N/A</td><td>For sample analysis</td></tr></tbody></table>

measurement of greenhouse gas flux from agricultural soils using static chambers

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the biogeochemical drivers of climate change and to the development and evaluation of GHG mitigation strategies based on modulation of landscape management practices. The static chamber-based method described here is based on trapping gases emitted from the soil surface within a chamber and collecting samples from the chamber headspace at regular intervals for analysis by gas chromatography. Change in gas concentration over time is used to calculate flux. This method can be utilized to measure landscape-based flux of carbon dioxide, nitrous oxide, and methane, and to estimate differences between treatments or explore system dynamics over seasons or years. Infrastructure requirements are modest, but a comprehensive experimental design is essential. This method is easily deployed in the field, conforms to established guidelines, and produces data suitable to large-scale GHG emissions studies.

Prior to beginning a research project with static chambers, it is important to understand the overall workflow, and the organization of in silico, field- and laboratory-based elements (Figure 1). Provided careful experimental design and system calibration (Figure 2), data analysis will generally be relatively straightforward. A rate of flux is determined for each chamber and sampling time by regression of time by concentration using a pre-determined flux model appropriate to the system (Figure 3). However, even following best practices, difficulties may be encountered, and quality control of raw data is critical. For example, failure of a chamber seal or leaky sample vials can result in anomalous concentration values. These are readily identified through visual inspection of time series concentration plots (Figure 4), with CO2 time series often serving as a particularly useful indicator due to the typically more robust and continuous flux of CO2 compared to sometimes negligible, near-detection-limit, or even negative fluxes of N2O or CH4. Once data quality has been confirmed, results may be used to compare gas flux dynamics between treatments or over the course of a season (Figure 5). As can be seen from May and June flux values and error bars, the variation caused by spatial heterogeneity of flux may be significant, and more pronounced under conditions producing high rates of flux. Such variability is not unusual, and underscores the importance of sufficient replication in this technique.
<img alt="Figure 1" src="/files/ftp_upload/52110/52110fig1highres.jpg" width="600" /> 
Figure 1. Workflow overview. Various elements of this protocol will be carried out in the planning stage, in the field, in the laboratory, and in silico. Arrows indicate the sequence of workflow, beginning with chamber design (and construction if necessary), and concluding with data analysis. Multiple boxes/arrows between field sampling and sample analysis represent the possibility of multiple sampling dates over the course of an experiment.
<img alt="Figure 2" src="/files/ftp_upload/52110/52110fig2highres.jpg" width="400" /> 
Figure 2. Sample timing. An example timing scheme for the collection of samples from multiple chambers simultaneously. Chamber numbers are indicated at left and time points at top, with sampling times listed in whole minutes within the grid. In this example, four separate time series of 36 min&#160;each (one for each chamber) are carried out within the space of 46 min, with 12 min&#160;spacing between time points within a series, and 2 min&#160;walking time between chambers. For this hypothetical example, the suitability of 36-min&#160;time series would have been determined by prior calibration. While evenly spaced timing is not necessary, it often simplifies the sampling scheme. Alternately, researchers may individually record each sampling timepoint to determine sampling intervals.
<img alt="Figure 3" src="/files/ftp_upload/52110/52110fig3highres.jpg" width="600" /> 
Figure 3. Flux calculation. A typical static chamber time series, consisting of N2O concentrations measured at four time points over the course of a 36-min&#160;sampling period. The linear regression is displayed, the slope of which yields flux rate.
<img alt="Figure 4" src="/files/ftp_upload/52110/52110fig4highres.jpg" width="600" /> 
Figure 4. Quality control. Paired time series from the same set of samples but different gases are shown in which vial leakage has been identified by visual inspection (red point). A) CO2 concentration over time. B) N2O concentration over time.
<img alt="Figure 5" src="/files/ftp_upload/52110/52110fig5highres.jpg" width="600" /> 
Figure 5. Synthesis results. N2O flux rate from an agricultural field over the course of a single growing season. Flux values represent the mean of six chambers, using four-point time series. Error bars are standard error.

Watch this Scientific Journal Video about Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers at JoVE.com

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

This article showcases the static chamber-based method for measurement of greenhouse gas flux from soil systems. With relatively modest infrastructure investments, measurements may be obtained from multiple treatments/locations and over timeframes ranging from hours to years.

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the ...

measurement-greenhouse-gas-flux-from-agricultural-soils-using-static

Research

Education

JoVE Core

JoVE Journal

Physics

Environment

Unit 1

The Kinetic Theory of Gases

SCIENTISTS IN ACTION

41.2K Views.  University of Wisconsin-Madison. This article showcases the static chamber-based method for measurement of greenhouse gas flux from soil systems. With relatively modest infrastructure investments, measurements may be obtained from multiple treatments/locations and over timeframes ranging from hours to years.

Video: Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be assimilated for energy and nutrients. Measuring soil microbial enzyme activity is crucial in understanding soil ecosystem functional dynamics. The general concept of the fluorescence enzyme assay is that synthetic C-, N-, or P-rich substrates bound with a fluorescent dye are added to soil samples. When intact, the labeled substrates do not fluoresce. Enzyme activity is measured as the increase in fluorescence as the fluorescent dyes are cleaved from their substrates, which allows them to fluoresce. Enzyme measurements can be expressed in units of molarity or activity. To perform this assay, soil slurries are prepared by combining soil with a pH buffer. The pH buffer (typically a 50 mM sodium acetate or 50 mM Tris buffer), is chosen for the buffer&#39;s particular acid dissociation constant (pKa) to best match the soil sample pH. The soil slurries are inoculated with a nonlimiting amount of fluorescently labeled (i.e. C-, N-, or P-rich) substrate. Using soil slurries in the assay serves to minimize limitations on enzyme and substrate diffusion. Therefore, this assay controls for differences in substrate limitation, diffusion rates, and soil pH conditions; thus detecting potential enzyme activity rates as a function of the difference in enzyme concentrations (per sample).
Fluorescence enzyme assays are typically more sensitive than spectrophotometric (i.e. colorimetric) assays,&#160;but can suffer from interference caused by impurities and the instability of many fluorescent compounds when exposed to light; so caution is required when handling fluorescent substrates. Likewise, this method only assesses potential enzyme activities under laboratory conditions when substrates are not limiting. Caution should be used when interpreting the data representing cross-site comparisons with differing temperatures or soil types, as in situ soil type and temperature can influence enzyme kinetics.

To measure potential rates of soil extracellular enzyme activities, synthetic substrates that are bound to a fluorescent dye are added to soil samples. Enzyme activity is measured as the fluorescent dye is released from the substrate by an enzyme-catalyzed reaction, where higher fluorescence indicates more substrate degradation.

Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be ...

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during storage. These processes are the major sources of greenhouse gas (GHG) emissions from animal production systems. Techniques for measuring enteric CH4 vary from direct measurements (respiration chambers, which are highly accurate, but with limited applicability) to various indirect methods (sniffers, laser technology, which are practical, but with variable accuracy). The sulfur hexafluoride (SF6)&#160;tracer gas method is commonly used to measure enteric CH4 production by animal scientists and more recently, application of an Automated Head-Chamber System (AHCS) (GreenFeed, C-Lock, Inc., Rapid City, SD), which is the focus of this experiment, has been growing. AHCS is an automated system to monitor CH4 and carbon dioxide (CO2) mass fluxes from the breath of ruminant animals. In a typical AHCS operation, small quantities of baiting feed are dispensed to individual animals to lure them to AHCS multiple times daily. As the animal visits AHCS, a fan system pulls air past the animal&#8217;s muzzle into an intake manifold, and through an air collection pipe where continuous airflow rates are measured. A sub-sample of air is pumped out of the pipe into non-dispersive infra-red sensors for continuous measurement of CH4 and CO2 concentrations. Field comparisons of AHCS to respiration chambers or SF6 have demonstrated that AHCS produces repeatable and accurate CH4 emission results, provided that animal visits to AHCS are sufficient so emission estimates are representative of the diurnal rhythm of rumen gas production. Here, we demonstrate the use of AHCS to measure CO2 and CH4 fluxes from dairy cows given a control diet or a diet supplemented with technical-grade cashew nut shell liquid.

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Accuracy and precision of the techniques used to measure methane emissions from ruminant animals are critically important for the success of greenhouse gas mitigation efforts. This manuscript describes the principles and operation of an automated system to monitor methane and carbon dioxide mass fluxes from the breath of ruminant animals.

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during ...

A CO2 Concentration Gradient Facility for Testing CO2 Enrichment and Soil Effects on Grassland Ecosystem Function

Continuing increases in atmospheric carbon dioxide concentrations (CA) mandate techniques for examining impacts on terrestrial ecosystems. Most experiments examine only two or a few levels of CA concentration and a single soil type, but if CA can be varied as a gradient from subambient to superambient concentrations on multiple soils, we can discern whether past ecosystem responses may continue linearly in the future and whether responses may vary across the landscape. The Lysimeter Carbon Dioxide Gradient Facility applies a 250 to 500 &#181;l L-1 CA gradient to Blackland prairie plant communities established on lysimeters containing clay, silty clay, and sandy soils. The gradient is created as photosynthesis by vegetation enclosed in in temperature-controlled chambers progressively depletes carbon dioxide from air flowing directionally through the chambers. Maintaining proper air flow rate, adequate photosynthetic capacity, and temperature control are critical to overcome the main limitations of the system, which are declining photosynthetic rates and increased water stress during summer. The facility is an economical alternative to other techniques of CA enrichment, successfully discerns the shape of ecosystem responses to subambient to superambient CA enrichment, and can be adapted to test for interactions of carbon dioxide with other greenhouse gases such as methane or ozone.

A CO2 Concentration Gradient Facility for Testing CO2 Enrichment and Soil Effects on Grassland Ecosystem Function

The Lysimeter Carbon Dioxide Gradient Facility creates a 250 to 500 &#181;l L-1 linear carbon dioxide gradient in temperature-controlled chambers housing grassland plant communities on clay, silty clay, and sandy soil monoliths. The facility is used to determine how past and future carbon dioxide levels affect grassland carbon cycling.

Continuing increases in atmospheric carbon dioxide concentrations (CA) mandate techniques for examining impacts on terrestrial ecosystems. Most ...

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

A method is described which uses the absence of radiocarbon in industrial chemicals and fuels made from petroleum feedstocks which frequently contaminate the environment. This radiocarbon signal &#8212; or rather the absence of signal &#8212; is evenly distributed throughout a contaminant source pool (unlike an added tracer) and is not impacted by biological, chemical or physical processes (e.g., the 14C radioactive decay rate is immutable). If the fossil-derived contaminant is fully degraded to CO2, a harmless end-product, that CO2 will contain no radiocarbon. CO2 derived from natural organic matter (NOM) degradation will reflect the NOM radiocarbon content (usually &#60;30,000 years old). Given a known radiocarbon content for NOM (a site background), a two end-member mixing model can be used to determine the CO2 derived from a fossil source in a given soil gas or groundwater sample. Coupling the percent CO2 derived from the contaminant with the CO2 respiration rate provides an estimate for the total amount of contaminant degraded per unit time. Finally, determining a zone of influence (ZOI) representing the volume from which site CO2 is collected allows determining the contaminant degradation per unit time and volume. Along with estimates for total contaminant mass, this can ultimately be used to calculate time-to-remediate or otherwise used by site managers for decision-making.

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

A protocol is described wherein CO2 mineralized from organic contaminant (derived from petroleum feedstocks) biodegradation is trapped, quantified, and analyzed for 14C content. A model is developed to determine CO2 capture zone's spatial extent. Spatial and temporal measurements allow integrating contaminant mineralization rates for predicting remediation extent and time.

A method is described which uses the absence of radiocarbon in industrial chemicals and fuels made from petroleum feedstocks which frequently contaminate ...

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Agricultural ammonia (NH3) emissions (90% of total EU emissions) are responsible for about 45% airborne eutrophication, 31% soil acidification and 12% fine dust formation within the EU15. But NH3 emissions also mean a considerable loss of nutrients. Many studies on NH3 emission from organic and mineral fertilizer application have been performed in recent decades. Nevertheless, research related to NH3 emissions after application fertilizers is still limited in particular with respect to relationships to emissions, fertilizer type, site conditions and crop growth. Due to the variable response of crops to treatments, effects can only be validated in experimental designs including field replication for statistical testing. The dominating ammonia loss methods yielding quantitative emissions require large field areas, expensive equipment or current supply, which restricts their application in replicated field trials. This protocol describes a new methodology for the measurement of NH3 emissions on many plots linking a simple semi-quantitative measuring method used in all plots, with a quantitative method by simultaneous measurements using both methods on selected plots. As a semi-quantitative measurement method passive samplers are used. The second method is a dynamic chamber method (Dynamic Tube Method) to obtain a transfer quotient, which converts the semi-quantitative losses of the passive sampler to quantitative losses (kg nitrogen ha-1). The principle underlying this approach is that passive samplers placed in a homogeneous experimental field have the same NH3 absorption behavior under identical environmental conditions. Therefore, a transfer co-efficient obtained from single passive samplers can be used to scale the values of all passive samplers used in the same field trial. The method proved valid under a wide range of experimental conditions and is recommended to be used under conditions with bare soil or small canopies (&#60;0.3 m). Results obtained from experiments with taller plants should be treated more carefully.

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Ammonia emissions are a major threat to the environment by eutrophication, soil acidification and fine particle formation and stem mainly from agricultural sources. This method allows ammonia loss measurements in replicated field trials enabling statistical analysis of emissions and of relationships between crop development and emissions.

Agricultural ammonia (NH3) emissions (90% of total EU emissions) are responsible for about 45% airborne eutrophication, 31% soil acidification and 12% ...

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace

This protocol describes the measurement of greenhouse gas (GHG) emissions from paddy soils using the static closed chamber technique. This method is based on the diffusion theory. A known volume of air overlaying a defined soil area is enclosed within a parallelepiped cover (named &#34;chamber&#34;), for a defined period of time. During this enclosure period, gases (methane (CH4) and nitrous oxide (N2O)) move from soil pore air near their microbial source (i.e., methanogens, nitrifiers, denitrifiers) to the chamber headspace, following a natural concentration gradient. Fluxes are then estimated from chamber headspace concentration variations sampled at regular intervals throughout the enclosure and then analyzed with gas chromatography. Among the techniques available for GHG measurement, the static closed chamber method is suitable for plot experiments, as it does not require large homogenously treated soil areas. Furthermore, it is manageable with limited resources and can identify relationships among ecosystem properties, processes, and fluxes, especially when combined with GHG driving force measurements. Nevertheless, with respect to the micrometeorological method, it causes a minimal but still unavoidable soil disturbance, and allows a minor temporal resolution. Several phases are key to the method implementation: i) chamber design and deployment, ii) sample handling and analyses, and iii) flux estimation. Technique implementation success in paddy fields demands adjustments for field flooding during much of the cropping cycle, and for rice plant maintenance within the chamber headspace during measurements. Therefore, the additional elements to be considered with respect to the usual application of non-flooded agricultural soils consist of devices for: i) avoiding any unintended water disturbance that could overestimate fluxes, and ii) including rice plants within the chamber headspace to fully consider gases emitted through aerenchyma transportation.

The overall goal of this protocol is to measure greenhouse gas emissions from paddy fields using the static closed chamber technique. The measurement system needs specific adjustments due to the presence of both a permanent water layer in the field and of the plants within the chamber headspace.

This protocol describes the measurement of greenhouse gas (GHG) emissions from paddy soils using the static closed chamber technique. This method is based ...

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

A lab-scaled simulated bedded pack model was developed to study air quality and nutrient composition of deep-bedded packs used in cattle mono-slope facilities. This protocol has been used to effectively evaluate many different bedding materials, environmental variables (temperature, humidity), and potential mitigation treatments that can improve air quality in commercial deep-bedded mono-slope facilities. The model is dynamic and allows researchers to easily collect many chemical and physical measurements from the bedded pack. Weekly measurements, collected over the course of six to seven weeks, allows sufficient time to see changes in air quality measurements over time as the bedded pack matures. The data collected from the simulated bedded packs is within the range of concentrations previously measured in commercial deep-bedded mono-slope facilities. Past studies have demonstrated that 8 - 10 experimental units per treatment are sufficient to detect statistical differences among the simulated bedded packs. The bedded packs are easy to maintain, requiring less than 10 minutes of labor per bedded packs per week to add urine, feces, and bedding. Sample collection using the gas sampling system requires 20 - 30 minutes per bedded pack, depending on the measurements that are being collected. The use of lab-scaled bedded packs allows the researcher to control variables such as temperature, humidity, and bedding source that are difficult or impossible to control in a research or commercial facility. While not a perfect simulation of &#34;real-world&#34; conditions, the simulated bedded packs serve as a good model for researchers to use to examine treatment differences among bedded packs. Several lab-scale studies can be conducted to eliminate possible treatments before trying them in a research or commercial-sized facility.

A protocol has been developed to measure gases, odors, and nutrient composition in lab-scaled bedded manure packs, which can be used to study ways to improve air quality in commercial cattle facilities using deep-bedded manure packs.

A lab-scaled simulated bedded pack model was developed to study air quality and nutrient composition of deep-bedded packs used in cattle mono-slope ...

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

This protocol is an example of utilizing the eddy covariance (EC) technique to investigate spatially and temporally averaged net CO2 fluxes (net ecosystem production, NEP), in non-typical ecosystems, on a currently reforested windthrow area in Poland. After a tornado event, a relatively narrow &#8220;corridor&#8221; was created within surviving forest stands, which complicates such kind of experiments. The application of other measuring techniques, such as the chamber method, is even more difficult under these circumstances, because especially at the beginning, fallen trees and in general great heterogeneity of the site provide a challenging platform to perform flux measurements and then to properly upscale obtained results. In comparison with standard EC measurements carried out in untouched forests, the case of windthrow areas requires special consideration when it comes to the site location and data analysis in order to ensure their representativeness. Therefore, here we present a protocol of real-time, continuous CO2 flux measurements at a dynamically changing, non-ideal EC site, which includes (1) site location and instrumentation setup, (2) flux computation, (3) rigorous data filtering and quality control, and (4) gap filling and net fluxes partitioning into CO2 respiration and absorption. The main advantage of the described methodology is that it provides a detailed description of the experimental setup and measurement performance from scratch, which can be applied to other spatially limited ecosystems. It can also be viewed as a list of recommendations on how to deal with unconventional site operation, providing a description for non-specialists. Obtained quality-checked, gap filled, half-hour values of net CO2, as well as absorption and respiration fluxes, can be finally aggregated into daily, monthly, seasonal or annual totals.

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

The presented protocol uses the eddy covariance method at non-typical locations, applicable to all types of short-canopy ecosystems with limited area, on a currently reforested windthrow site in Poland. Details of measuring site setup rules, flux calculations and quality control, and final result analysis, are described.

This protocol is an example of utilizing the eddy covariance (EC) technique to investigate spatially and temporally averaged net CO2 fluxes (net ecosystem ...

Measurements of Soil Carbon by Neutron-Gamma Analysis in Static and Scanning Modes

The herein described application of the inelastic neutron scattering (INS) method for soil carbon analysis is based on the registration and analysis of gamma rays created when neutrons interact with soil elements. The main parts of the INS system are a pulsed neutron generator, NaI(Tl) gamma detectors, split electronics to separate gamma spectra due to INS and thermo-neutron capture (TNC) processes, and software for gamma spectra acquisition and data processing. This method has several advantages over other methods in that it is a non-destructive in situ method that measures the average carbon content in large soil volumes, is negligibly impacted by local sharp changes in soil carbon, and can be used in stationary or scanning modes. The result of the INS method is the carbon content from a site with a footprint of ~2.5 - 3 m2 in the stationary regime, or the average carbon content of the traversed area in the scanning regime. The measurement range of the current INS system is &#62;1.5 carbon weight % (standard deviation &#177; 0.3 w%) in the upper 10 cm soil layer for a 1 hmeasurement.

Here, we present the protocol for in situ measurement of soil carbon using the neutron-gamma technique for single point measurements (static mode) or field averages (scanning mode). We also describe system construction and elaborate data treatment procedures.

The herein described application of the inelastic neutron scattering (INS) method for soil carbon analysis is based on the registration and analysis of ...

Engineering

Simulating Temperature in a Soil Incubation Experiment

The study of warming impact on soils requires a realistic and accurate representation of temperature. In laboratory incubation studies, a widely adopted method has been to render constant temperatures in multiple chambers, and via comparisons of soil responses between low- and high-temperature chambers, to derive the warming impact on soil changes. However, this commonly used method failed to imitate both the magnitude and amplitude of actual temperatures as observed in field conditions, thus potentially undermining the validity of such studies. With sophisticated environmental chambers becoming increasingly available, it is imperative to examine alternative methods of temperature control for soil incubation research. This protocol will introduce a state-of-the-art environmental chamber and demonstrate both conventional and new methods of temperature control to improve the experimental design of soil incubation. The protocol mainly comprises four steps: temperature monitoring and programming, soil collection, laboratory incubation, and warming effect comparison. One example will be presented to demonstrate different methods of temperature control and the resultant contrasting warming scenarios; that is, a constant temperature design referred to as stepwise warming (SW) and simulated in situ temperature design as gradual warming (GW), as well as their effects on soil respiration, microbial biomass, and extracellular enzyme activities. In addition, we present a strategy to diversify temperature change scenarios to meet specific climate change research needs (e.g., extreme heat). The temperature control protocol and the recommended well-tailored and diversified temperature change scenarios will assist researchers in establishing reliable and realistic soil incubation experiments in the laboratory.

Laboratory soil warming experiments usually employ two or more constant temperatures in multiple chambers. By presenting a sophisticated environmental chamber, we provide an accurate temperature control method to imitate the magnitude and amplitude of in situ soil temperature and improve the experimental design of soil incubation studies.

The study of warming impact on soils requires a realistic and accurate representation of temperature. In laboratory incubation studies, a widely adopted ...

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

Samenvatting

Meer video's verkennen