Name: assessment of labile organic carbon in soil using sequential fumigation incubation procedures
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Description: Watch this Scientific Journal Video about Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures at JoVE.com

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
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	<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 rel...

<ol>
	<li>Blair, G., 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=Soil+carbon+fractions+based+on+their+degree+of+oxidation,+and+the+development+of+a+carbon+management+index+for+agricultural+systems.">Soil carbon fractions based on their degree of oxidation, and the development of a carbon management index for agricultural systems.</a> Aust. J. Agric. Res. 46, 1459-1466 (1995).</li><li>Schimel, D. S., 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=Soil+organic+matter+dynamics+in+paired+rangeland+and+cropland+toposequences+in+North+Dakota.">Soil organic matter dynamics in paired rangeland and cropland toposequences in North Dakota.</a> Geoderma. 36, 201-214 (1985).</li><li>Parton, W. J., 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=Analysis+of+factors+controlling+soil+organic+matter+levels+in+great-plains+grasslands.">Analysis of factors controlling soil organic matter levels in great-plains grasslands.</a> Soil Sci. Soc. Am. J. 51, 1173-1179 (1987).</li><li>Wu, H., 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=Labile+organic+C+and+N+mineralization+of+soil+aggregate+size+classes+in+semiarid+grasslands+as+affected+by+grazing+management.">Labile organic C and N mineralization of soil aggregate size classes in semiarid grasslands as affected by grazing management.</a> Biol. Fertil. Soils. 48, 305-313 (2011).</li><li>Jones, D. 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=Plant+and+mycorrhizal+regulation+of+rhizodeposition.">Plant and mycorrhizal regulation of rhizodeposition.</a> New Phytol. 163, 459-480 (2004).</li><li>Harrison, K. G., 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=The+effect+of+changing+land+use+of+soil+radiocarbon.">The effect of changing land use of soil radiocarbon.</a> Science. 262, 725-726 (1993).</li><li>Jinbo, Z., 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=Land+use+effects+on+the+distribution+of+labile+organic+carbon+fractions+through+soil+profiles.">Land use effects on the distribution of labile organic carbon fractions through soil profiles.</a> Soil Sci Soc. Am. J. 70, 660-667 (2006).</li><li>Whalen, J. K., 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=Carbon+and+nitrogen+mineralization+from+light-+and+heavy-fraction+additions+to+soil.">Carbon and nitrogen mineralization from light- and heavy-fraction additions to soil.</a> Soil Biol Biochem. 32, 1345-1352 (2000).</li><li>Gregorich, E. G., 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=Towards+a+minimum+data+set+to+assess+soil+organic+matter+quality+in+agricultural+soils.">Towards a minimum data set to assess soil organic matter quality in agricultural soils.</a> Can. J. Soil Sci. 74, 367-385 (1994).</li><li>Hamer, U., 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=Priming+effects+in+different+soil+types+induced+by+fructose,+alanine,+oxalic+acid+and+catechol+additions.">Priming effects in different soil types induced by fructose, alanine, oxalic acid and catechol additions.</a> Soil Biol. Biochem. 37, 445-454 (2005).</li><li>Feng, W., 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=Shifting+sources+of+soil+labile+organic+carbon+after+termination+of+plant+carbon+inputs+in+a+subtropical+moist+forest+of+southwest+China.">Shifting sources of soil labile organic carbon after termination of plant carbon inputs in a subtropical moist forest of southwest China.</a> Ecol. Res. 26, 437-444 (2011).</li><li>Tisdall, J. M., Carter, M. R., Stewart, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Formation+of+soil+aggregates+and+accumulation+of+soil+organic+matter.">Formation of soil aggregates and accumulation of soil organic matter.</a> Structure and Organic Matter Storage in Agricultural Soils. , 57-96 (1996).</li><li>Zou, X. M., 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=Estimating+soil+labile+organic+carbon+and+potential+turnover+rates+using+a+sequential+fumigation-incubation+procedure.">Estimating soil labile organic carbon and potential turnover rates using a sequential fumigation-incubation procedure.</a> Soil Biol. Biochem. 37, 1923-1928 (2005).</li><li>Cambardella, C. A., Elliott, E. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particulate+soil+organic+matter+changes+across+a+grassland+cultivation+sequence.">Particulate soil organic matter changes across a grassland cultivation sequence.</a> Soil Sci. Soc. Am. J. 56, 777-783 (1992).</li><li>Strosser, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+determination+of+labile+soil+organic+matter:+an+overview.">Methods for determination of labile soil organic matter: an overview.</a> J. Agrobiol. 27, 49-60 (2010).</li><li>Jenkinson, D. A., Powlson, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effects+of+biocidal+treatment+on+metabolism+in+soil+V:+a+method+for+measuring+soil+biomass.">The effects of biocidal treatment on metabolism in soil V: a method for measuring soil biomass.</a> Soil Biol. Biochem. 8, 209-213 (1976).</li><li>Vance, E. D., 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=An+extraction+method+for+measuring+soil+microbial+biomass+C.">An extraction method for measuring soil microbial biomass C.</a> Soil Biol. Biochem. 19, 703-707 (1987).</li><li>De-Polli, H., 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=Chloroform+fumigation-extraction+labile+C+pool+(microbial+biomass+C+&amp;#34;plus&amp;#34;)+shows+high+correlation+to+microbial+biomass+C+in+Argentinian+and+Brazilian+soils.">Chloroform fumigation-extraction labile C pool (microbial biomass C &amp;#34;plus&amp;#34;) shows high correlation to microbial biomass C in Argentinian and Brazilian soils.</a> Cienc. Suelo. 25, 15-22 (2007).</li><li>Olson, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Energy+storage+and+the+balance+of+producers+and+decomposers+in+ecological+systems.">Energy storage and the balance of producers and decomposers in ecological systems.</a> Ecology. 44, 322-331 (1963).</li><li>Pennock, D., Carter, M. R., Gregorich, E. G., 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=Chapter+1,+Unit+1,+Soil+sampling+designs.">Chapter 1, Unit 1, Soil sampling designs.</a> Soil Sampling and Methods of Analysis. , (2008).</li><li>Luizao, R. C. C., 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=Seasonal+variation+of+soil+microbial+biomass:+the+effects+of+clearfelling+a+tropical+rainforest+and+establishment+of+pasture+in+the+central+Amazon.">Seasonal variation of soil microbial biomass: the effects of clearfelling a tropical rainforest and establishment of pasture in the central Amazon.</a> Soil Biol. Biochem. 24, 805-813 (1992).</li><li>Horwath, W. R., Paul, E. A., Weaver, R. W., 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=Microbial+biomass.">Microbial biomass.</a> Methods of soil analysis part 2: microbiological and biochemical properties. , 753-773 (1994).</li><li>Jenkinson, D. S., Ladd, J. N., Paul, E. A., Ladd, J. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+biomass+in+soil:+measurement+and+turnover.">Microbial biomass in soil: measurement and turnover.</a> Soil Biochemistry. , 415-471 (1981).</li><li>Blazier, M. A., Gungor, E. B. O., 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=Poultry+litter+fertilization+impacts+on+soil,+plant,+and+water+characteristics+in+loblolly+pine+(Pinus+taeda+L.)+plantations+and+silvopastures+in+the+mid-South+USA.">Poultry litter fertilization impacts on soil, plant, and water characteristics in loblolly pine (Pinus taeda L.) plantations and silvopastures in the mid-South USA.</a> Principles, application, and assessment in soil science. , 43-74 (2011).</li><li>Blazier, M. A., 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=Straw+harvesting,+fertilization,+and+fertilizer+type+alter+soil+biophysical+properties+in+a+loblolly+pine+plantation+in+the+mid-South+USA.">Straw harvesting, fertilization, and fertilizer type alter soil biophysical properties in a loblolly pine plantation in the mid-South USA.</a> Biol. Fertil. Soils. 45, 145-153 (2008).</li><li>Blazier, M. A., 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=Loblolly+pine+age+and+density+affects+switchgrass+growth+and+soil+carbon+in+an+agroforestry+system.">Loblolly pine age and density affects switchgrass growth and soil carbon in an agroforestry system.</a> For. Sci. 58, 485-496 (2012).</li><li>Blazier, M. A., 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=Nitrogen+and+carbon+of+switchgrass,+loblolly+pine,+and+cottonwood+biofuel+production+systems+in+the+Southeast+United+States.">Nitrogen and carbon of switchgrass, loblolly pine, and cottonwood biofuel production systems in the Southeast United States.</a> For. Sci. 61, 522-534 (2015).</li><li>Zhang, M., 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=Decomposition+differences+of+labile+carbon+from+litter+to+soil+in+a+tropical+rain+forest+and+rubber+plantation+of+Xishuagbanna,+southwest+China.">Decomposition differences of labile carbon from litter to soil in a tropical rain forest and rubber plantation of Xishuagbanna, southwest China.</a> Eur. J. Soil Biol. 55, 55-61 (2013).</li><li>Nelson, D. W., Sommers, L. E., Sparks, D., 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=Total+carbon,+organic+carbon,+and+organic+matter.">Total carbon, organic carbon, and organic matter.</a> Methods of soil analysis. Part 3: chemical methods. , 961-1090 (1996).</li><li>Huang, 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=Correlation+among+soil+microorganisms,+soil+enzyme+activities,+and+removal+rates+of+pollutants+in+three+constructed+wetlands+purifying+micro-polluted+river+water.">Correlation among soil microorganisms, soil enzyme activities, and removal rates of pollutants in three constructed wetlands purifying micro-polluted river water.</a> Soil Biol. 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 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 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>
		</tr>
		<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>
		</tr>
		<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>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Boiling chips</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">S25201</td>
			<td></td>
		</tr>
		<tr height="21">
			<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>
		</tr>
		<tr height="21">
			<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>
		</tr>
		<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>
		<tr height="21">
			<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>
		</tr>
		<tr height="21">
			<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>
		</tr>
		<tr height="21">
			<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>
			<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 ...

The overall goal of this sequential fumigation incubation procedure is to estimate labile carbon and the potential carbon turnover rate in soil. This method can answer key things in the soil microbiology field, such as the amount of substrate that soil microbes use to transform nutrients in the soil. One of the main advantages of this technique is that it's relatively simple to use and inexpensive.Prior to collection, identify differences in soil properties and vegetation within the collection site. Using the appropriate coefficients of variation, determine the number of samples needed and the distance of variability. For heterogeneous soil conditions, designate a grid sample pattern wherein the distance between sample points on each transect is smaller than the distance of variability within an experimental unit.Clear organic matter from the soil surface at the sample points. Using an auger, collect each soil sample. For each transect of the grid, mix the samples in a single container.Place each composite sample in a cooler with ice packs immediately after collection. Once field work is completed, sieve the soil samples. Clean the mesh with water after sieving each sample.For each composite soil sample, place three individual 100 gram samples into beakers. Cover the beakers with laboratory film and pre-incubate the beakers at 25 degrees Celsius for 10 days. Then, for each sample, remove the film and place one gram of soil in a pre-weighed aluminum weigh boat.Record the combined weight of the moist soil and weigh boat. Dry each sample in the weigh boats at 105 degrees Celsius for 48 hours. Check the weight every eight hours for the first 24 hours and then every six hours until no more weight is lost.Using the known weights of the weigh boats, determine the weight of the dry soil. Calculate the dry to moist soil weight ratio. To begin the fumigation, place a damp paper towel at the bottom of each of two glass vacuum desiccators with porcelain plates.Designate one desiccator for fumigation. For each sample, prepare three narrow glass vials, each containing 30 grams of soil. Label each vial in pencil on laboratory tape.For each set, place two vials in the fumigation desiccator and the third vial in the other desiccator. Place the fumigation desiccator in a fume hood. Cover the bottom of a 100 milliliter beaker with boiling stones.In the fume hood, pour 50 milliliters of ethanol-free chloroform into the beaker. Place the beaker in the center of the fumigation desiccator and close the desiccator. Connect the fumigation desiccator to a vacuum pump.Turn on the vacuum pump and allow the chloroform to boil for 30 seconds. Then, turn off and disconnect the pump to allow air into the desiccator. Repeat this step twice more.Boil the chloroform a fourth time for two minutes. Then, close off the desiccator to maintain the vacuum. Turn off and disconnect the vacuum pump.Close and seal the other desiccator. Store both desiccators in a dark area at room temperature for 24 hours. Next, place the fumigation desiccator in a fume hood.Vent the desiccator and remove the paper towel. Place vials with soil into the desiccator, close the desiccator, connect the vacuum pump, and open the vacuum to remove chloroform gas from the soil samples. Run the vacuum for five minutes, and then vent the desiccator.Perform this step four more times, then remove the soil-filled vials from the desiccator to prepare them for incubation procedures. To prepare incubation containers, label wide-mouth polypropylene bottles for the fumigated and non-fumigated subsamples. Label three more bottles as no soil controls for later titration.For each labeled bottle, drill a hole in the center of a size 10 rubber stopper and insert a 15 centimeter length glass rod into the hole. Affix a 40 milliliter glass vial to the glass rod with a rubber band. Then, add one gram of soil from the original pre-incubated soil sample to each of its three subsamples.Place one milliliter of deionized water into one of the bottles labeled for fumigated soil. Place a vial of fumigated soil into the bottle. Pipette one milliliter of two molar sodium hydroxide into the glass vial of the stopper assembly.Carefully stopper the bottle and seal the bottle with laboratory film. Repeat this process for the remaining soil samples. Prepare the no soil containers in the same way but without soil samples.Store the incubation chambers in a dark area at room temperature for 10 days. After incubation, carefully remove the vial of sodium hydroxide from an incubation container. Pipette two milliliters of one molar barium chloride into the vial.Add one drop of phenolphthalein to the vial. Add a magnetic stir bar to the vial, and begin stirring the mixture on a stir plate. With a burette, slowly add 0.1 normal hydrochloric acid until the solution appearance changes from red to colorless.Record the volume of hydrochloric acid added. Repeat the titration for all of the incubation chambers noting the no soil values separately. Using the titration data and the dry-moist soil weight ratio, calculate the carbon dioxide mineralized during incubation of the soil samples.From this, calculate the microbial biomass carbon for each set of samples. Perform seven more rounds of fumigation and incubation on the fumigated samples, calculating the mineralized carbon dioxide after each cycle. Using this data, determine the amount of labile organic carbon and the potential turnover time for each fumigated sample by non-linear regression.The sequential soil fumigation and incubation method identify differences in labile organic carbon, or LOC, and potential carbon turnover rates among different vegetation conditions. Soybean conditions have the highest LOC values, which was associated with biomass deposition during soybean harvests. Loblolly pine plantations had the highest potential carbon turnover rates, which was associated with acidic pine litter being the dominant source of soil organic matter.The loblolly pine and switchgrass alley cropping system had the broadest array of sampling site conditions and the switchgrass pasture had the greatest variation in soil textures among sampling sites. Both of these conditions exhibited wide ranges of both LOC values and potential carbon turnover rates. Once mastered, this technique takes about 30 minutes per sample over the course of an 80 day period that it takes to run each sample.To conduct this procedure accurately, some of the things to remember are to make sure the desiccators are sealed tightly, as well as the incubation chambers, and to not breathe too closely to the vials that contain sodium hydroxide. In tandem with this procedure or following this procedure, there's several different enzyme assays that can be conducted in soil to better understand soil microbial function. After watching this video, you should have a good understanding of how to conduct Labile Carbon Analysis using the sequential fumigation incubation procedure.Don't forget that working with chloroform can be extremely hazardous. You need personal protection equipment and a fume hood to conduct these procedures safely.

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Research

JoVE Journal

Environment

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.