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 concurrently makes the SFI method a relatively cost-effective analytical method. 
Measuring additional soil parameters along with those collected with the SFI method can provide a broader understanding of the impacts of changes in LOC and its associated variables on nutrient cycling. Total C concentration of the soil provides the ability to determine changes in the size of the soil LOC pool relative to total C29. Including assays of soil enzymes facilitates understanding of microbial utilization of the labile C substrate that leads to changes in soil nutrient forms and quantities. For example, dehydrogenase plays a key function in organic matter degradation and is a useful indicator of overall microbial actitivity30. Urease and phosphatase are examples of enzymes associated with nutrient cycling because they are correlated with microbial transformations of nitrogen and phosphorus, respectively31,32.
There are variations in procedures of the SFI method, and the number of fumigation and incubation cycles is adjustable to different soil conditions. In the experiment in which the SFI method was developed, five or eight cycles were used for the array of tropical and subtropical forest soils tested13. In that experiment, five fumigation-incubation cycles were used for the Oa horizon and deeper mineral soil horizons of an Alfisol soil while eight cycles were used for Ultisol, Oxisol, Inceptisol, and Entisol soils. More recent experiments with the SFI method have used five28 and seven11 fumigation-incubation cycles. The key consideration for the number of cycles is whether CO2 accumulation plateaus over the course of the cycles. The SFI method fails to provide estimates of labile organic C and potential turnover if the negative exponential model does not fit the CO2 accumulation data. For some soil conditions, such a plateau can be reached within five fumigation-incubation cycles whereas for others more cycles are necessary. Preliminary testing of the SFI method on the soil conditions of interest can aid in determining the appropriate number of cycles necessary. Graphing CO2 accumulation trends for all samples after each fumigation-incubation cycle can also aid in determining accuracy of the number of cycles to be used.
Due to its dependence on CO2 accumulation to derive the LOC and C turnover rate, accurate measurements of CO2 evolution during incubation are essential to the SFI method. The long history associated with the use of the chloroform fumigation-incubation method from which the SFI method was developed has provided a number of recommendations to minimize potential errors. For example, the 10-day pre-incubation detailed in this SFI protocol description is recommended to allow the influence of soil disturbance to subside and for living fragments of roots to die in order to better isolate microbial CO2 evolution during the initial incubation period33,34. There are several alternatives for incubation container design, but essential components of design include a vial with NaOH to react with CO2 from microbial respiration, separate containers for the NaOH and soil, a source of moisture to prevent soil from drying during incubation, and seals to prevent contamination from ambient air during the incubation period22. Care must also be taken to avoid breathing near the vial of NaOH, which would partially neutralize the alkali and yield erroneous CO2 measurements, during incubation container assembly22. Sample depth in the incubation container can affect accuracy of CO2 measurements. In a test of soil sample depths within incubation containers, sample depths less than 4 cm had significantly greater inconsistency in CO2 evolution measurements than depths from 4 to 10 cm35. The higher inconsistency from the shallower depths was attributed to greater potential for soil drying during incubation, greater diffusion of O2 into soil, greater diffusion of CO2 out of the soil, and a lower potential for anaerobic microorganisms to contribute to decomposition. Soil pH may be another important consideration for the accuracy of CO2 accumulation measurements. After chloroform fumigation, recolonizing microorganisms during the incubation period are primarily bacterial populations. However, at soil pH values under 4.5, bacterial population development is inhibited. For the chloroform fumigation-incubation method, this effect leads to underestimation of soil microbial biomass C relative to that measured with other methods. Effects of soil pH on values yielded by the SFI method have not yet been tested, but precaution about using the method on low-pH soils may be warranted due to the known effects of pH on chloroform fumigation-incubation results.
The SFI method is one of several methods for measuring LOC, and it is important to distinguish measurements from this method relative to that of others. All LOC methods are an attempt to emulate natural processes of microbial degradation in soil to estimate LOC pool size, but these methods can differ in the portions of the LOC pool they sample and estimate36. In a study of labile organic pools in the Wuyi Mountains of China, LOC was measured with KMnO4 oxidation, aqueous extraction, physical fractionation, and the SFI method37. Measurements from each method were positively correlated with each other except for light fraction carbon as measured by physical fractionation. However, there were differences in the ranges of values derived from each method that were a function of the different components of the LOC pool each method measured. Generally, the magnitude of LOC values derived from each method was physical fractionation &gt; KMnO4 oxidation &gt; SFI &gt; aqueous extraction. Physical fractionation was done as a measure of light fraction C, which is typically comprised of plant residues and animal remains. However, light fraction C can also contain charcoal and woody debris that decomposes relatively slowly; this LOC pool is an intermediate stage between plant residue and stable organic matter. Labile organic C values from KMnO4 oxidation (termed easily oxidized C) were approximately twice as large as those of the SFI method, which was attributed to the procedure sampling fractions of LOC with slower turnover than the SFI and aqueous extraction methods. Labile organic C measurements from aqueous extraction (termed water-soluble organic C) were approximately ten-fold lower than those of the SFI method. Due to microbial degradation of C being central to the SFI method, its LOC values were interpreted as microbial available C. These microbial available C values were greater than those of microbial biomass C measured in the same experiment, indicating that some of the C remaining in the soil after measuring microbial biomass C was available for mineralization by microbes. Greater values for LOC from the SFI method than those of microbial biomass C are consistent with several previous studies13,24,25,26,27. The SFI method has been used in several studies (which represent the majority of reported uses of this method to the authors' knowledge) conducted in an array of locations, vegetation types, and soil conditions11,13,24,25,26,27,28,38. Given the apparent differences in the fractions of LOC pools measured by different methods, future applications of the SFI method should be done in concert with LOC methods that estimate different types of labile C as done in the Wuyi Mountain study36. Distinguishing LOC measured by the SFI method as microbial available C may be warranted to more precisely describe the type of LOC estimated by SFI. Inferences derived from the SFI method could also be improved by refining understanding of the portion of the LOC pool that is estimated by SFI and its roles in soil organic C and nutrient cycling processes.

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 border="0" cellpadding="0" cellspacing="0" width="674">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Soil auger sampling kit</td>
			<td style="width:131px;">JMC</td>
			<td style="width:119px;">PN039</td>
			<td style="width:167px;">Several other manufacturers of punch augers are available</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Parafilm</td>
			<td style="width:131px;">Curwood</td>
			<td style="width:119px;">PM999</td>
			<td></td>
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		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Aluminum weighing boats</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">08-732-103</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">General purpose drying oven</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">15-103-0511</td>
			<td>Many other manufacturers of general purpose laboratory ovens are available</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">10.5 L vacuum desiccator</td>
			<td style="width:131px;">Corning</td>
			<td style="width:119px;">3121-250</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Glass scintillation vial</td>
			<td style="width:131px;">Wheaton</td>
			<td align="right" style="width:119px;">968560</td>
			<td></td>
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		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Glass threaded vials, 41 mL&#160;</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">03-339-21N</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Chloroform, stabilized with amylenes</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td style="width:119px;">67-66-3</td>
			<td></td>
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			<td height="21" style="height:21px;width:259px;">Glass rod</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">S63449</td>
			<td></td>
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			<td height="21" style="height:21px;width:259px;">Size 10 rubber stopper</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">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 height="21">
			<td height="21" style="height:21px;width:259px;">Wide-mouth PPCO bottle, 0.5 L</td>
			<td style="width:131px;">ThermoScientific</td>
			<td align="right" style="width:119px;">3121050016</td>
			<td></td>
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		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Sodium hydroxide, reagent grade</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td style="width:119px;">S5881</td>
			<td></td>
		</tr>
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			<td height="21" style="height:21px;width:259px;">Barium chloride</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td align="right" style="width:119px;">202738</td>
			<td></td>
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			<td height="21" style="height:21px;width:259px;">Phenolphthalein indicator</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">S25466</td>
			<td></td>
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			<td height="21" style="height:21px;width:259px;">Hydrochloric acid solution, 0.1 N</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">SA54-4</td>
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		</tr>
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</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 potential C turnover rates among fertilization and cropping practice treatments in all studies. There was overlap in the range of LOC and potential turnover rates reported in this series of studies (Figure 2). Variations in the ranges of LOC reported in these studies highlight the sensitivity of the SFI method in detecting differences in labile C and potential C turnover. The loblolly pine and switchgrass alley cropping system had the greatest range of LOC among the vegetation types; the studies of this vegetation type encompassed the widest array of site conditions relative to the other vegetation types. The sites varied in soil type and age of the loblolly pine overstory from juvenile to late-rotation. The chronosequence of overstory age likely created the largest variation in organic matter inputs among the vegetation types presented here for representative results. The switchgrass pasture vegetation type was studied at the widest array of soil textures, and it also exhibited a relatively high variance in reported values. The soybean vegetation type had relatively high LOC values among site types, which was likely associated with the annual deposition of dead above- and below-ground biomass to the soil during its harvest. Loblolly pine plantations, which are characterized by relatively recalcitrant acidic pine litter as the dominant source of soil organic matter, exhibited the highest potential C turnover rates among the vegetation types selected here for representative results. The range of values reported in this series of studies is also within the range of those found in the range of soils used to develop the SFI method13 and in later experiments conducted with subtropical forests in China by one of the scientists that developed the SFI method11,28.
<img alt="Figure 1" src="/files/ftp_upload/54614/54614fig1.jpg" /> 
Figure 1. Incubation container for conducting sequential fumigation incubation procedure for labile organic C and potential C turnover rate determination. On the left is the Nalgene bottle, vial suspended from a stopper rod to contain NaOH, and a vial containing 30 g of soil shown separated for demonstration purposes. The container on the right has the soil and the stopper rod positioned within the Nalgene bottle with Parafilm along the top as would be done during incubation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54614/54614fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54614/54614fig2.jpg" /> 
Figure 2. Range of labile organic C and potential C turnover rate as measured with the sequential fumigation incubation method in different soil and vegetation conditions in the southeastern United States. Ranges reported are adapted in part from previous studies24,25,26,27. Vegetation types are: (1) loblolly pine plantation, (2) loblolly pine and bahiagrass pasture, (3) loblolly pine and switchgrass alley cropping system, (4) switchgrass pasture, (5) soybean, and (6) cottonwood plantation. Bars represent standard deviation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54614/54614fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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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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

Biofilm Removal Using Carbon Dioxide Aerosols without Nitrogen Purge

Biofilms can cause serious concerns in many applications. Not only can they cause economic losses, but they can also present a public health hazard. Therefore, it is highly desirable to remove biofilms from surfaces. Many studies on CO2 aerosol cleaning have employed nitrogen purges to increase biofilm removal efficiency by reducing the moisture condensation generated during the cleaning. However, in this study, periodic jets of CO2 aerosols without nitrogen purges were used to remove Pseudomonas putida biofilms from polished stainless steel surfaces. CO2 aerosols are mixtures of solid and gaseous CO2 and are generated when high-pressure CO2 gas is adiabatically expanded through a nozzle. These high-speed aerosols were applied to a biofilm that had been grown for 24 hr. The removal efficiency ranged from 90.36% to 98.29% and was evaluated by measuring the fluorescence intensity of the biofilm as the treatment time was varied from 16 sec to 88 sec. We also performed experiments to compare the removal efficiencies with and without nitrogen purges; the measured biofilm removal efficiencies were not significantly different from each other (t-test, p &gt; 0.55). Therefore, this technique can be used to clean various bio-contaminated surfaces within one minute.

Biofilms on surfaces can be effectively and rapidly removed by using a periodic jet of carbon dioxide aerosols without a nitrogen purge.

Biofilms can cause serious concerns in many applications. Not only can they cause economic losses, but they can also present a public health hazard. ...

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

A rheometer system to measure the rheology of crude oil in equilibrium with carbon dioxide (CO2) at high temperatures and pressures is described. The system comprises a high-pressure rheometer which is connected to a circulation loop. The rheometer has a rotational flow-through measurement cell with two alternative geometries: coaxial cylinder and double gap. The circulation loop contains a mixer, to bring the crude oil sample into equilibrium with CO2, and a gear pump that transports the mixture from the mixer to the rheometer and recycles it back to the mixer. The CO2 and crude oil are brought to equilibrium by stirring and circulation and the rheology of the saturated mixture is measured by the rheometer. The system is used to measure the rheological properties of Zuata crude oil (and its toluene dilution) in equilibrium with CO2 at elevated pressures up to 220 bar and a temperature of 50 &#176;C. The results show that CO2 addition changes the oil rheology significantly, initially reducing the viscosity as the CO2 pressure is increased and then increasing the viscosity above a threshold pressure. The non-Newtonian response of the crude is also seen to change with the addition of CO2.

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

A method for measuring the rheology of crude oil in equilibrium with carbon dioxide at reservoir conditions is presented.

A rheometer system to measure the rheology of crude oil in equilibrium with carbon dioxide (CO2) at high temperatures and pressures is described. The ...

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 ...

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

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 ...

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon (EC). This is commonly accomplished using thermal-optical OC/EC analyzers (TOAs), which enable measurement via controlled thermal pyrolysis and oxidation under specific temperature protocols and within constrained atmospheres. Several commercial TOAs exist, including a semi-continuous instrument that enables on-line analyses in the field. This instrument employs an in-test calibration procedure that requires relatively frequent calibration. This article details a calibration protocol for this semi-continuous TOA and presents an open-source software tool for data analysis and rigorous Monte Carlo quantification of uncertainties. Notably, the software tool includes novel means to correct for instrument drift and identify and quantify the uncertainty in the OC/EC split point. This is a significant improvement on the uncertainty estimation in the manufacturer&#8217;s software, which ignores split point uncertainty and otherwise uses fixed equations for relative and absolute errors (generally leading to under-estimated uncertainties and often yielding non-physical results as demonstrated in several example data sets). The demonstrated calibration protocol and new software tool enabling accurate quantification of combined uncertainties from calibration, repeatability, and OC/EC split point are shared with the intent of assisting other researchers in achieving better measurements of OC, EC, and total carbon mass in aerosol samples.

This article presents a protocol and software tool for the quantification of uncertainties in the calibration and data analysis of a semi-continuous thermal-optical organic/elemental carbon analyzer.

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon ...

Fluorescently Labeled Bacteria as a Tracer to Reveal Novel Pathways of Organic Carbon Flow in Aquatic Ecosystems

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial communities has many limitations, and determining a predator, prey, and predatory rates is often difficult. Presented here is an optimized method based on the addition of fluorescently labelled prey as a tracer, which allows for reliable quantitation of the grazing rates in aquatic predatory eukaryotes and estimation of nutrient transfer to higher trophic levels.

Presented here is a protocol for a single-cell, epifluorescence microscopy-based technique to quantify grazing rates in aquatic predatory eukaryotes with high precision and taxonomic resolution.

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial ...

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment

Phosphorus (P) is a critical limiting nutrient in agroecosystems requiring careful management to reduce transport risk to aquatic environments. Routine laboratory measures of P bioavailability are based on chemical extractions performed on dried samples under oxidizing conditions. While useful, these tests are limited with respect to characterizing P release under prolonged water saturation. Labile orthophosphate bound to oxidized iron and other metals can rapidly desorb to solution in reducing environments, increasing P mobilization risk to surface runoff and groundwater. To better quantify P desorption potential and mobility during extended saturation, a laboratory microcosm method was developed based on repeated sampling of porewater and overlying floodwater over time. The method is useful for quantifying P release potential from soils and sediments varying in physicochemical properties and can improve site-specific P mitigation efforts by better characterizing P release risk in hydrologically active areas. Advantages of the method include its ability to simulate in situ dynamics, simplicity, low cost, and flexibility.

Accurate quantification of phosphorus (P) desorption potential in saturated soils and sediments is important for P modeling and transport mitigation efforts. To better account for in situ soil-water redox dynamics and P mobilization under prolonged saturation, a simple approach was developed based on repeated sampling of laboratory microcosms.

Phosphorus (P) is a critical limiting nutrient in agroecosystems requiring careful management to reduce transport risk to aquatic environments. Routine ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.