The authors gratefully acknowledge Michelle Gonzales, Kenny Kidd, Brad Osbon, and all other personnel that conducted the laboratory procedures for these data. The authors are thankful for assistance from Andrew Scott in developing software coding to conduct model-fitting procedures. The authors also appreciate the funding from the U.S. Department of Agriculture National Institute of Food and Agriculture, Sustainable Agriculture and Research &amp; Education, Sun Grant South Central region, and the National Council of Air and Stream Improvement that made possible the studies from which representative results provided in this paper were drawn.

1. Collect Soil to Get Samples Representative of Conditions within the Experimental Area and within Experimental Units20
<ol>
	<li>Identify any differences in site properties such as slope and soil properties including texture, bulk density, pH, organic horizon depth, and/or nutrient concentrations. Identify any differences in vegetation type within plots. Use known or published estimates of coefficients of variation for site properties to estimate the number of samples required to attain a pre-specified relative error.</li>
	<li>Sample soil using an auger or other collection device in a pattern based on site and experimental unit conditions.
	<ol>
		<li>For homogeneous conditions, use a random sampling pattern within each experimental unit.
		<ol>
			<li>Assign sample points at either completely random locations within the experimental unit or in a zigzag pattern.</li>
			<li>Sample soil at each random point or at points assigned in a zigzag pattern. Brush aside organic matter from the surface of mineral soil before using the auger or other collection device to excavate soil sample. 
			NOTE: The SFI method was developed using soil horizons from the Oa and below13. Further testing is necessary if horizons above the Oa can be tested using the SFI method.</li>
			<li>Combine all samples collected within the experimental unit into a single container and physically mix the individual samples within the container to create a composite sample for each experimental unit.</li>
		</ol>
		</li>
		<li>For heterogeneous conditions, which are much more common, use a systematic sampling pattern within each experimental unit.
		<ol>
			<li>Sample soils along the transect in the center of each experimental unit such that the distance between sample points within the transect is smaller than the distance needed to represent variability within the experimental units.</li>
			<li>Sample soils along multiple transects within each experimental unit that form a grid pattern in relatively large experimental units or experimental units with multiple sources of variability.</li>
			<li>Combine all samples collected along each transect into a single container and physically mix the individual samples within the container to create a composite sample for each transect.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Prepare Soil for the SFI Assay
<ol>
	<li>Place samples in an ice-pack filled cooler immediately after collection in the field.</li>
	<li>Upon arrival at the facility at which samples are to be stored until analysis, place samples in a refrigerator at 4 &#176;C until sample preparation and SFI procedures are conducted.</li>
	<li>Sieve soil samples through a 6.4 mm x 6.4 mm mesh sieve. Clean the mesh with water between each sample to prevent contamination between samples.</li>
	<li>For each sample, measure three 100 g subsamples and place the 100 g subsamples in a 250 ml beaker. Cover each beaker with Parafilm and leave them on a countertop for 10 days at 25 &#176;C.</li>
</ol>
3. Take Subsamples for Oven-dry Weight Determination
<ol>
	<li>At the end of the 10 day pre-incubation of soil samples, remove Parafilm from each sample.</li>
	<li>Record the weight of an aluminum weigh boat. Take 1 g of soil from all samples and place in weigh boat.</li>
	<li>Record the weight of the moist soil and weigh boat.</li>
	<li>Place the weigh boats with soil in an oven at 105 &#176;C. After the samples reach a constant weight, which is typically after 48 hr, record weights of the weigh boats and soil.</li>
	<li>Subtract weigh boat weight from the weights taken of the moist soil and dry soil within the weigh boat to get moist and dry soil weight. Derive the dry:moist soil ratio by dividing dry soil weight by the moist soil weight.</li>
</ol>
4. Fumigate Soil Samples
<ol>
	<li>Place a damp paper towel in the bottom of at least two (more may be necessary depending on the number of samples) 10.5 L glass vacuum desiccators with porcelain plates.</li>
	<li>For all samples, weigh 30 g of soil into three separate glass vials. Use vials large enough to hold 40 g of soil and narrow enough to fit within a 40 mm opening if the incubation container design described in section 5 is used.</li>
	<li>If using labeling tape to identify each 30 g soil subsample, use pencil because fumigation degrades ink.</li>
	<li>Place two of the three 30 g subsamples for each soil sample into a vacuum desiccator for fumigation and one subsample into a vacuum desiccator that will not conduct fumigation.</li>
	<li>In a 100 ml beaker, place a layer of boiling stones sufficient to cover the bottom of the beaker.</li>
	<li>Pour 50 ml of ethanol-free chloroform (CHCl3) into the 100 ml beaker with a layer of boiling stones. Place the 100 ml beaker with boiling stones and CHCl3 in the center of a desiccator filled with the 30 g soil subsamples. Conduct this step under a fume hood.</li>
	<li>Under a fume hood, use a vacuum to boil the CHCl3 to fumigate two sets of subsamples per soil sample.
	<ol>
		<li>Connect the vacuum to the vacuum desiccator with vacuum tubing. Start the vacuum and watch as CHCl3 begins to boil.</li>
		<li>Allow CHCl3 to boil for 30 sec and disconnect the vacuum tubing from the desiccator to allow air to flow back into the desiccator. This step promotes CHCl3 gas entry into the soil samples. Repeat twice.</li>
		<li>Perform a fourth and final boil of CHCl3, allowing it to boil for 2 min.</li>
		<li>With vacuum still running, close the seal on the vacuum desiccator so that the vacuum within the desiccator is maintained. Turn off the vacuum and disconnect the vacuum tubing from the desiccator.</li>
	</ol>
	</li>
	<li>Seal the desiccator containing the non-fumigated samples by placing a lid on the desiccator and sealing the vacuum stopper. Place the desiccators (fumigated and non-fumigated) in a darkened area (such as a cabinet) for 24 hr. Do not repeat the vacuum procedures of subsection 4.7 on the desiccator containing non-fumigated samples.</li>
</ol>
5. Assemble Containers for Soil Sample Incubation
<ol>
	<li>Push a 15 cm length glass rod through a size 10 rubber stopper with a hole drilled in the center. The rod diameter should be sufficient to fit through the hole snugly.</li>
	<li>Label 0.5 L translucent wide mouth polypropylene bottles with identification that corresponds to the fumigated and non-fumigated subsample identification.</li>
</ol>
6. Evacuate Chloroform from Desiccators Under a Fume Hood
<ol>
	<li>Open the stopper on a vacuum desiccator to allow airflow into the desiccator. Remove the lid from the desiccator, and take the samples and the damp towel out of desiccator.</li>
	<li>Use a vacuum to evacuate CHCl3 gas from soil samples.
	<ol>
		<li>Place the lid on the desiccator. Connect the desiccator to a vacuum with vacuum tubing.</li>
		<li>Turn on the vacuum pump and allow pump to run for five minutes. Disconnect the vacuum tubing from the desiccator to allow airflow into the desiccator.</li>
		<li>Repeat step 6.3.2 four times.</li>
	</ol>
	</li>
</ol>
7. Move Each Soil Subsample into an Incubation Container (Figure 1) to Conduct a 10 Day Incubation
<ol>
	<li>Pipette 1 ml of deionized water into the incubation container. Connect an empty glass vial to the glass rod extending from the size 10 stopper using a rubber band. The open end of the glass vial should face the base of the stopper. The glass vial should be of a size sufficient to hold up to 40 ml of fluid.</li>
	<li>Place a vial containing the 30 g soil subsample into the incubation container.</li>
	<li>Add 1 g of non-fumigated soil from the original soil sample to each of its corresponding subsamples (fumigated and non-fumigated) as inoculum.</li>
	<li>Pipette 1 ml of 2 M NaOH into the glass vial connected to the stopper/glass rod. Push the stopper/glass rod onto the top of the incubation container. Cover the top of the incubation container with Parafilm.</li>
	<li>Create an incubation container that contains no soil. Assemble three to five no-soil incubation containers. 
	NOTE: The acid used to titrate samples of the no-soil container is essential to the determination of CO2 mineralization during the incubation period, which is described below in subsection 9.3. As such, multiple no-soil containers are created as a safeguard against incorrect handling or titration of a no-soil incubation container that would create an error in CO2 mineralization calculation for all samples. The acid used to titrate samples from the no-soil containers should be close in values; a highly dissimilar acid value among the no-soil container samples is likely the result of incorrect sample handling or titration.
	<ol>
		<li>Follow the procedures of section 5 to assemble incubation containers.</li>
		<li>Follow the procedures of 7.1 and 7.4.</li>
	</ol>
	</li>
	<li>Place all incubation containers in a darkened storage area at 25 &#176;C. Leave all incubation containers in the storage area for 10 days.</li>
</ol>
8. Perform Titration on Each Subsample to Quantify CO2 Produced by Microbial Respiration during the Incubation Period
<ol>
	<li>Remove the glass vial containing the 2 M NaOH from the incubation container.</li>
	<li>Pipette 2 ml of 1 M BaCl2 into the glass vial containing 2 M NaOH.</li>
	<li>Add one drop of phenolphthalein (C20H14O4) from a pipette or medicine dropper into the glass vial containing the mixture of BaCl2 and NaOH. Place a magnetic stir bar in the glass vial and place the glass vial on a stir plate.</li>
	<li>With the stir plate activated, slowly add 0.1 N HCl with a burette until the red coloration of the mixture in the glass vial turns clear.</li>
	<li>Record the amount of HCl required to change the coloration of the mixture in the glass vial.</li>
</ol>
9. Determine Microbial Biomass C from Data Collected during the First Fumigation-incubation Cycle16,21,22
<ol>
	<li>Determine the dry weight of soil in each subsample by multiplying its moist weight by the dry:moist weight ratio obtained in step 3.8.</li>
	<li>Determine the average amount of HCl used to titrate the no-soil incubation containers.</li>
	<li>Calculate CO2 mineralized during the 10-day incubation using the formula: 
	<img alt="Equation 1" src="/files/ftp_upload/54614/54614eq1.jpg" /> 
	where CO2 = CO2 mineralized during the 10-day incubation 
	NS = Acid used to titrate samples in no-soil incubation container 
	S = Acid used to titrate samples that contained soil in the incubation container 
	M = molarity of the HCl 
	E = 6, the equivalent weight 
	W = dry weight of soil contained in the incubation container</li>
	<li>Calculate microbial biomass C using the formula: 
	<img alt="Equation 2" src="/files/ftp_upload/54614/54614eq2.jpg" /> 
	where BioC = microbial biomass C 
	F = CO2 mineralized from soil subsamples that were fumigated 
	NF = CO2 mineralized from soil subsamples that were non-fumigated 
	K = fraction of microbial biomass C mineralized to CO2
	<ol>
		<li>Determine the value for K by either direct measurement of 14C mineralization in preliminary tests with the soil or published values22. A value of 0.45 is commonly used for K for this assay23.</li>
	</ol>
	</li>
	<li>Perform sequential fumigation and incubation cycles by repeating sections 4-8 seven times for the soil subsamples that were fumigated in the first fumigation-incubation cycle.</li>
</ol>
10. Determine Labile C and Potential C Turnover Rate Using CO2 Mineralized over the Course of the Eight Fumigation and Incubation Cycles
<ol>
	<li>Use the following formula to determine a correction factor for the soil inoculum added to samples after each fumigation: 
	<img alt="Equation 3" src="/files/ftp_upload/54614/54614eq3.jpg" /> 
	Where IC = Correction factor for inoculum 
	C&#39; = Amount of CO2 from the non-fumigated subsample during the first 10-day incubation 
	r = Weight ratio of inoculum soil to fumigated soil in the first fumigation incubation cycle 
	Ct = incubation cycle (1, 2 &#8230; 8), such that Ct-1 = 0 when t = 1</li>
	<li>Use the following formula to estimate the CO2 released during each incubation for each subsample: 
	<img alt="Equation 4" src="/files/ftp_upload/54614/54614eq4.jpg" /> 
	where Ct = CO2 released during incubation 
	NS = Acid used to titrate samples in no-soil incubation container 
	S = Acid used to titrate samples that contained soil in the incubation container 
	IC = Correction factor for inoculum (determined in step 10.1) 
	E = 6, the equivalent weight 
	W = dry weight of soil contained in the incubation container</li>
	<li>Derive labile organic C using non-linear regression.
	<ol>
		<li>Organize a spreadsheet that includes for each sample identifiers for the sample, incubation cycle number (1, 2 &#8230;8), and CO2 released during the incubation (derived in step 10.2).</li>
		<li>Using software capable of non-linear regression, fit the following model to the dataset: 
		<img alt="Equation 5" src="/files/ftp_upload/54614/54614eq5.jpg" /> 
		where Csum = the sum of CO2 released during the eight incubation cycles 
		LOC = soil labile organic C 
		k = potential turnover time 
		t = incubation cycle (1, 2 &#8230;8)</li>
	</ol>
	</li>
	<li>Convert potential turnover time from step 10.3.2 into days by multiplying the inverse of k by 10 due to the 10 day incubation cycle.</li>
</ol>

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Biochem. 70, 221-228 (2012).</li><li>Kong, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enzyme+and+root+activities+in+surface-flow+constructed+wetlands.">Enzyme and root activities in surface-flow constructed wetlands.</a> Chemosphere. 76, 601-608 (2009).</li><li>Cui, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+nutrient+removal+efficiency+and+microbial+enzyme+activity+in+a+baffled+subsurface-flow+constructed+wetland+system.">Evaluation of nutrient removal efficiency and microbial enzyme activity in a baffled subsurface-flow constructed wetland system.</a> Bioresour. Technol. 146, 656-662 (2013).</li><li>Jenkinson, D. S., Wilson, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determination+of+microbial+biomass+carbon+and+nitrogen+in+soil.">Determination of microbial biomass carbon and nitrogen in soil.</a> Advances in nitrogen cycling in agricultural ecosystems. , 368-386 (1988).</li><li>Sparling, G. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interference+from+plant+roots+in+the+estimation+of+soil+microbial+ATP,+C,+N,+and+P.">Interference from plant roots in the estimation of soil microbial ATP, C, N, and P.</a> Soil Biol. Biochem. 17, 275-278 (1985).</li><li>Christie, P., Beatte, J. A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Significance+of+sample+size+in+measurement+of+soil+microbial+biomass+by+the+chloroform+fumigation-incubation+method.">Significance of sample size in measurement of soil microbial biomass by the chloroform fumigation-incubation method.</a> Soil Biol. Biochem. 19, 149-152 (1987).</li><li>McLaughlin, K. K., Hobbie, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+labile+soil+organic+matter+fractionation+techniques.">Comparison of labile soil organic matter fractionation techniques.</a> Soil Sci. Soc. Am. J. 68, 1616-1625 (2004).</li><li>Xia, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Variation+of+soil+labile+organic+carbon+pools+along+an+elevational+gradient+in+the+Wuyi+Mountains,+China.">Variation of soil labile organic carbon pools along an elevational gradient in the Wuyi Mountains, China.</a> J. Resour. Ecol. 1, 368-374 (2010).</li></ol>

The authors have nothing to disclose.

The SFI method is an effective protocol for detecting differences in soil LOC and potential C turnover rates over a range of management practices (such as fertilization, tillage, vegetation control, and harvest practices) and soil conditions. Soil LOC content and C turnover rate can be used to understand alterations of nutrient cycles. The SFI method also provides measurement of microbial biomass C from the first fumigation-incubation event. The ability to measure soil LOC, C turnover, and microbial biomass C concurrentl...

Due to its vital roles in carbon (C) and nutrient cycling and its sensitivity to soil change, soil LOC is an important parameter to measure as an indicator of soil organic matter quality. Forests and agroecosystems to a large degree depend on the mineralization of nutrients in soil organic matter as a source of nutrients. Management activities can change the pool size and turnover rate of soil organic C, resulting in changes in nutrient supply1. Soil organic C consists of two primary fractions of recalcitrant C, which has turnover rates of several thousand years, and LOC, which has turnover rates from a few weeks to a few years2,3,4. Soil labile C consists of readily decomposable substrates such as microbial biomass C, low-molecular-weight compounds (amino acids, simple carbohydrates) from plant rhizodeposition, and decomposition byproducts and leachates from plant litter1,4,5. Because soil labile C is readily decomposable, it is highly sensitive to management practices and natural phenomena that disturb or alter soil6. Soil labile C serves as the primary energy source for soil microorganisms in the decomposition of organic matter7. As such, LOC impacts nutrient cycling to a greater degree than does stable forms of soil organic C8. Soil microorganisms are also responsible for the majority of heterotrophic respiration that occurs during decomposition of recalcitrant soil organic matter facilitated by the priming effect of LOC9,10,11. This respiration plays a substantial role in global C cycles because soil organic C is approximately double that of atmospheric C11.
As a result of its importance in terrestrial ecosystems, several methods have been developed to estimate soil LOC. These methods can be delineated into three general classifications: physical, chemical, and biochemical. Densitometric separation methods are physical methods that consist of separating soil organic C into heavy or light fractions or into coarse and fine particulate organic C12,13,14,15. Separation methods are relatively easy to perform, but they do not often produce consistent results because these fractions vary with soil type mineral composition, plant material size and density, and soil aggregate consistency13,15. Separation methods also produce only quantitative information about LOC15.
Several chemical methods are available for LOC estimation. Aqueous extraction of organic carbon is relatively easy to perform, and the methods often provide easily reproducible results. However, these extractions do not involve the whole spectrum of available substrates for microorganisms15. Several oxidation methods for chemical fractionation of soil organic C have been developed. Oxidation methods have the advantage of characterizing the quantity and quality of labile organic C, although some methods require work with hazardous chemicals and there is variability among the methods in reproducibility of results15. The acid hydrolysis extraction method is another type of chemical fractionation procedure that can measure the quantity and quality of LOC, but results of this method do not facilitate interpretation of its biological properties13,15.
Biochemical methods for interpretation of soil LOC have been developed. Labile organic C can be measured as CO2 released by microorganisms in respiration assays. These assays provide estimates of true mineralizable organic matter, but typically only the most labile compounds are mineralized during the assays15. Soil microbial biomass C measured by fumigation-incubation16 and fumigation-extraction17 has been used to develop inferences about LOC. However, these procedures provide estimates of C in microbial biomass rather than LOC. Both fumigation procedures include subtraction of values from non-fumigated soil to determine microbial biomass C, but it has been suggested that values obtained without subtraction of non-fumigated soil provide a measure of labile organic fractions of C in addition to microbial biomass18.
The sequential fumigation-incubation (SFI) procedure13 for measuring LOC is a biochemical method adapted from the fumigation-incubation procedure16 for soil microbial biomass C measurement. The SFI method has some advantages relative to other methods of estimating LOC. A conceptual basis for the method is that LOC is the microbially degradable C that governs microbial growth and that LOC is physically accessible and chemically degradable by soil microorganisms. Under field conditions, microbial growth is typically limited by carbon availability, nutrient availability, available pore space, and/or predation. These factors are nearly eliminated by fumigation, creating unimpeded conditions for microbial growth. No nutrients are removed during the incubation period of the method. Over the course of multiple fumigation and incubation cycles, microbial growth becomes limited by C quantity and quality (lability)13. The accumulated CO2 respired during the incubation cycles is used to extrapolate LOC with a simple negative exponential model11,13,19. The potential C turnover rate can also be derived from the slope of the exponential model, so the SFI method has the advantage over most other LOC methods of simultaneously estimating the concentrations and potential turnover rate of LOC11. For other methods, information on the potential turnover rates of LOC can only be ascertained if tracers such as 14C are used13. The SFI method is thus a relatively simple and inexpensive technique for obtaining measurements of both LOC and its potential turnover rates.

<table><tbody><tr><td>Soil auger sampling kit</td><td>JMC</td><td>PN039</td><td>Several other manufacturers of punch augers are available</td></tr><tr><td>Parafilm</td><td>Curwood</td><td>PM999</td><td></td></tr><tr><td>Aluminum weighing boats</td><td>Fisherbrand</td><td>08-732-103</td><td></td></tr><tr><td>General purpose drying oven</td><td>Fisher Scientific</td><td>15-103-0511</td><td>Many other manufacturers of general purpose laboratory ovens are available</td></tr><tr><td>10.5 L vacuum desiccator</td><td>Corning</td><td>3121-250</td><td></td></tr><tr><td>Glass scintillation vial</td><td>Wheaton</td><td>968560</td><td></td></tr><tr><td>Glass threaded vials, 41&amp;nbsp;ml</td><td>Fisherbrand</td><td>03-339-21N</td><td></td></tr><tr><td>Chloroform, stabilized with amylenes</td><td>Sigma-Aldrich</td><td>67-66-3</td><td></td></tr><tr><td>Boiling chips</td><td>Fisher Scientific</td><td>S25201</td><td></td></tr><tr><td>Glass rod</td><td>Fisherbrand</td><td>S63449</td><td></td></tr><tr><td>Size 10 rubber stopper</td><td>Fisherbrand</td><td>14-130P</td><td>Rubber stoppers can be purchased as solid and drilled in center to install glass rod or bought with a hole to insert glass rod</td></tr><tr><td>Wide-mouth PPCO bottle, 0.5 L</td><td>ThermoScientific</td><td>3121050016</td><td></td></tr><tr><td>Sodium hydroxide, reagent grade</td><td>Sigma-Aldrich</td><td>S5881</td><td></td></tr><tr><td>Barium chloride</td><td>Sigma-Aldrich</td><td>202738</td><td></td></tr><tr><td>Phenolphthalein indicator</td><td>Fisher Scientific</td><td>S25466</td><td></td></tr><tr><td>Hydrochloric acid solution, 0.1 N</td><td>Fisher Scientific</td><td>SA54-4</td><td></td></tr></tbody></table>

assessment of labile organic carbon in soil using sequential fumigation incubation procedures

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is highly sensitive to disturbance. It is also the primary substrate for soil microorganisms, which is fundamental to nutrient cycling. Due to these attributes, labile organic carbon (LOC) has been identified as an indicator parameter for soil health. Quantifying the turnover rate of LOC also aids in understanding changes in soil nutrient cycling processes. A sequential fumigation incubation method has been developed to estimate soil LOC and potential C turnover rate. The method requires fumigating soil samples and quantifying CO2-C respired during a 10 day incubation period over a series of fumigation-incubation cycles. Labile organic C and potential C turnover rate are then extrapolated from accumulated CO2 with a negative exponential model. Procedures for conducting this method are described.

The SFI method has been used as described in this paper in a series of experiments conducted in the southeastern United States24,25,26,27. Together, these experiments encompassed a variety of vegetation types, including loblolly pine (Pinus taeda L.), switchgrass (Panicum virgatum L.), cottonwood (Populus deltoides Bartram ex Marsh.), and soybean (Glycine max L. Merr.). The method was sensitive at determining differences in LOC and/or potenti...

Watch this Scientific Journal Video about Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures at JoVE.com

Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures

Labile organic carbon (LOC) and the potential carbon turnover rate are sensitive indicators of changes in soil nutrient cycling processes. Details are provided for a method based on fumigating and incubating soil in a series of cycles and using the CO2 accumulated during the incubation periods to estimate these parameters.

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is ...

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Environment

11.4K Views.  Louisiana State University Agricultural Center. Labile organic carbon (LOC) and the potential carbon turnover rate are sensitive indicators of changes in soil nutrient cycling processes. Details are provided for a method based on fumigating and incubating soil in a series of cycles and using the CO2 accumulated during the incubation periods to estimate these parameters.

Video: Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites

The physical and chemical properties of biochar vary based on feedstock sources and production conditions, making it possible to engineer biochars with specific functions (e.g. carbon sequestration, soil quality improvements, or contaminant sorption). In 2013, the International Biochar Initiative (IBI) made publically available their Standardized Product Definition and Product Testing Guidelines (Version 1.1) which set standards for physical and chemical characteristics for biochar. Six biochars made from three different feedstocks and at two temperatures were analyzed for characteristics related to their use as a soil amendment. The protocol describes analyses of the feedstocks and biochars and includes: cation exchange capacity (CEC), specific surface area (SSA), organic carbon (OC) and moisture percentage, pH, particle size distribution, and proximate and ultimate analysis. Also described in the protocol are the analyses of the feedstocks and biochars for contaminants including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), metals and mercury as well as nutrients (phosphorous, nitrite and nitrate and ammonium as nitrogen). The protocol also includes the biological testing procedures, earthworm avoidance and germination assays. Based on the quality assurance / quality control (QA/QC) results of blanks, duplicates, standards and reference materials, all methods were determined adequate for use with biochar and feedstock materials. All biochars and feedstocks were well within the criterion set by the IBI and there were little differences among biochars, except in the case of the biochar produced from construction waste materials. This biochar (referred to as Old biochar) was determined to have elevated levels of arsenic, chromium, copper, and lead, and failed the earthworm avoidance and germination assays. Based on these results, Old biochar would not be appropriate for use as a soil amendment for carbon sequestration, substrate quality improvements or remediation.

Biochar is a carbon-rich material used as a soil amendment with the ability to sustainably sequester carbon, improve substrate quality and sorb contaminants. This protocol describes the 17 analytical methods used for the characterization of biochar, which is required prior to large scale implementation of these amendments in the environment.

The physical and chemical properties of biochar vary based on feedstock sources and production conditions, making it possible to engineer biochars with ...

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition.
An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. 
This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial ...

Procedures of Laboratory Fumigation for Pest Control with Nitric Oxide Gas

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on fresh products and procedures for residue analysis and product quality evaluation. An airtight fumigation chamber containing fresh fruit and vegetables is first flushed with nitrogen (N2) to establish an ultralow oxygen (ULO) environment followed by injection of NO. The fumigation chamber is then kept at a low temperature of 2 - 5 &#176;C for a specified time period necessary to kill a target pest to complete a fumigation treatment. At the end of a fumigation treatment, the fumigation chamber is flushed with N2 to dilute NO prior to opening the chamber to ambient air to prevent the reaction between NO and O2, which produces NO2 and may damage delicate fresh products. At different times after NO fumigation, NO2 in headspace and nitrate and nitrite in liquid samples were measured as residues. Product quality was evaluated after 2 weeks of post-treatment cold storage to determine effects of NO fumigation on product quality. Keeping&#160;O2 from reacting with NO is critical to NO fumigation and is an important part of the protocols. Measuring NO levels is challenging and a practical solution is provided. Possible protocol modifications are also suggested for measuring NO levels in the fumigation chambers as well as residues. NO fumigation has the potential to be a practical alternative to methyl bromide fumigation for postharvest pest control on fresh and stored products. This publication is intended to assist other researchers in conducting NO fumigation research for postharvest pest control and accelerating the development of NO fumigation for practical applications.

This paper describes nitric oxide (NO) fumigation protocols for postharvest pest control. Fumigation chambers are flushed with nitrogen (N2) to establish ultralow oxygen conditions before NO is injected. At the end, chambers are flushed with N2 to dilute NO before exposing products to ambient air to prevent exposure to NO2.

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on ...

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic bonds that constitute the organic component of soils. However, the relatively low organic matter content of soils (commonly &#60; 5% by mass) and absorbance overlap of mineral and organic functional groups in the mid-infrared (MIR) region (4,000-400 cm-1) engenders substantial interference by dominant mineral absorbances, challenging or even preventing interpretation of spectra for SOM characterization. Spectral subtractions, a post-hoc mathematical treatment of spectra, can reduce mineral interference and enhance resolution of spectral regions corresponding to organic functional groups by mathematically removing mineral absorbances. This requires a mineral-enriched reference spectrum, which can be empirically obtained for a given soil sample by removing SOM. The mineral-enriched reference spectrum is subtracted from the original (untreated) spectrum of the soil sample to produce a spectrum representing SOM absorbances. Common SOM removal methods include high-temperature combustion (&#39;ashing&#39;) and chemical oxidation. Selection of the SOM removal method carries two considerations: (1) the amount of SOM removed, and (2) absorbance artifacts in the mineral reference spectrum and thus the resulting subtraction spectrum. These potential issues can, and should, be identified and quantified in order to avoid fallacious or biased interpretations of spectra for organic functional group composition of SOM. Following SOM removal, the resulting mineral-enriched sample is used to collect a mineral reference spectrum. Several strategies exist to perform subtractions depending on the experimental goals and sample characteristics, most notably the determination of the subtraction factor. The resulting subtraction spectrum requires careful interpretation based on the aforementioned methodology. For many soil and other environmental samples containing substantial mineral components, subtractions have strong potential to improve FTIR spectroscopic characterization of organic matter composition.

SOM underlies many soil functions and processes, but its characterization by FTIR spectroscopy is often challenged by mineral interferences. The described method can increase the utility of SOM analysis by FTIR spectroscopy by subtracting mineral interferences in soil spectra using empirically obtained mineral reference spectra.

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Monitoring Pedogenic Inorganic Carbon Accumulation Due to Weathering of Amended Silicate Minerals in Agricultural Soils.

The present study aims to demonstrate a systematic procedure for monitoring inorganic carbon induced by enhanced weathering of comminuted rocks in agricultural soils. To this end, the core soil samples taken at different depth (including 0-15 cm, 15-30 cm, and 30-60 cm profiles) are collected from an agriculture field, the topsoil of which has already been enriched with an alkaline earth metal silicate containing mineral (such as wollastonite). After transporting to the laboratory, the soil samples are air-dried and sieved. Then, the inorganic carbon content of the samples is determined by a volumetric method called calcimetry. The representative results presented herein showed five folded increments of inorganic carbon content in the soils amended with the Ca-silicate compared to control soils. This compositional change was accompanied by more than 1 unit of pH increase in the amended soils, implying high dissolution of the silicate. Mineralogical and morphological analyses, as well as elemental composition, further corroborate the increase in the inorganic carbon content of silicate-amended soils. The sampling and analysis methods presented in this study can be adopted by researchers and professionals looking to trace pedogenic inorganic carbon changes in soils and subsoils, including those amended with other suitable silicate rocks such as basalt and olivine. These methods can also be exploited as tools for verifying soil inorganic carbon sequestration by private and governmental entities to certify and award carbon credits.

The verification method described here is adaptable for monitoring pedogenic inorganic carbon sequestration in various agricultural soils amended with alkaline earth metal silicate-containing rocks, such as wollastonite, basalt, and olivine. This type of validation is essential for carbon credit programs, which can benefit farmers that sequester carbon in their fields.

The present study aims to demonstrate a systematic procedure for monitoring inorganic carbon induced by enhanced weathering of comminuted rocks in ...

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 ...

Utilizing Soil Density Fractionation to Separate Distinct Soil Carbon Pools

Soil organic matter (SOM) is a complicated mixture of different compounds that span the range from free, partially degraded plant components to more microbially altered compounds held in the soil aggregates to highly processed microbial by-products with strong associations with reactive soil minerals. Soil scientists have struggled to find ways to separate soil into fractions that are easily measurable and useful for soil carbon (C) modeling. Fractionating soil based on density is increasingly being used, and it is easy to perform and yields C pools based on the degree of association between the SOM and different minerals; thus, soil density fractionation can help to characterize the SOM and identify SOM stabilization mechanisms. However, the reported soil density fractionation protocols vary significantly, making the results from different studies and ecosystems hard to compare. Here, we describe a robust density fractionation procedure that separates particulate and mineral-associated organic matter and explain the benefits and drawbacks of separating soil into two, three, or more density fractions. Such fractions often differ in their chemical and mineral composition, turnover time, and degree of microbial processing, as well as the degree of mineral stabilization.

Soil density fractionation separates soil organic matter into distinct pools with differing stabilization mechanisms, chemistries, and turnover times. Sodium polytungstate solutions with specific densities allow the separation of free particulate organic matter and mineral-associated organic matter, resulting in organic matter fractions suitable for describing the soil response to management and climate change.

Soil organic matter (SOM) is a complicated mixture of different compounds that span the range from free, partially degraded plant components to more ...

Capturing Actively Produced Microbial Volatile Organic Compounds from Human-Associated Samples with Vacuum-Assisted Sorbent Extraction

Volatile organic compounds (VOCs) from biological samples have unknown origins. VOCs may originate from the host or different organisms from within the host&#39;s microbial community. To disentangle the origin of microbial VOCs, volatile headspace analysis of bacterial mono- and co-cultures of Staphylococcus aureus, Pseudomonas aeruginosa, and Acinetobacter baumannii, and stable isotope probing in biological samples of feces, saliva, sewage, and sputum were performed. Mono- and co-cultures were used to identify volatile production from individual bacterial species or in combination with stable isotope probing to identify the active metabolism of microbes from the biological samples.
Vacuum-assisted sorbent extraction (VASE) was employed to extract the VOCs. VASE is an easy-to-use, commercialized, solvent-free headspace extraction method for semi-volatile and volatile compounds. The lack of solvents and the near-vacuum conditions used during extraction make developing a method relatively easy and fast when compared to other extraction options such as tert-butylation and solid phase microextraction. The workflow described here was used to identify specific volatile signatures from mono- and co-cultures. Furthermore, analysis of the stable isotope probing of human associated biological samples identified VOCs that were either commonly or uniquely produced. This paper presents the general workflow and experimental considerations of VASE in conjunction with stable isotope probing of live microbial cultures.

This protocol describes the extraction of volatile organic compounds from a biological sample with the vacuum-assisted sorbent extraction method, gas chromatography coupled with mass spectrometry using the Entech Sample Preparation Rail, and data analysis. It also describes culture of biological samples and stable isotope probing.

Volatile organic compounds (VOCs) from biological samples have unknown origins. VOCs may originate from the host or different organisms from within the ...

Biochemistry

Isolation of Culturable Yeasts and Molds from Soils to Investigate Fungal Population Structure

Soil is host to an incredible amount of microbial life, with each gram containing up to billions of bacterial, archaeal, and fungal cells. Multicellular fungi such as molds and unicellular fungi, broadly defined as yeasts, fulfill essential roles in soil ecosystems as decomposers of organic material and as food sources for other soil dwellers. Fungal species diversity in soil is dependent on a multitude of climatic factors such as rainfall and temperature, as well as soil properties including organic matter, pH, and moisture. Lack of adequate environmental sampling, especially in regions of Asia, Africa, South America, and Central America, hinders the characterization of soil fungal communities and the discovery of novel species. 
We characterized soil fungal communities in nine countries across six continents using ~4,000 soil samples and a protocol developed in the laboratory for the isolation of yeasts and molds. This protocol begins with separate selective enrichment for yeasts and the medically relevant mold Aspergillus fumigatus, in liquid media while inhibiting bacterial growth. Resulting colonies are then transferred to solid media and further processed to obtain pure cultures, followed by downstream genetic characterization. Yeast species identity is established via sequencing of their internal transcribed spacer (ITS) region of the nuclear ribosomal RNA gene cluster, while global population structure of A. fumigatus is explored via microsatellite marker analysis. 
The protocol was successfully applied to isolate and characterize soil yeast and A. fumigatus populations in Cameroon, Canada, China, Costa Rica, Iceland, Peru, New Zealand, and Saudi Arabia. These findings revealed much-needed insights on global patterns in soil yeast diversity, as well as global population structure and antifungal resistance profiles of A. fumigatus. This paper presents the method of isolating both yeasts and A. fumigatus from international soil samples.

This protocol is an effective, speedy method of culturing yeasts and the mold Aspergillus fumigatus from large sets of soil samples in as little as 7 days. The methods can be easily modified to accommodate a range of incubation media and temperatures as needed for experiments.

Soil is host to an incredible amount of microbial life, with each gram containing up to billions of bacterial, archaeal, and fungal cells. Multicellular ...

Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures

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