The field study site is on a farm managed by David Drake and we thank him for his cooperation during this long-term research. We also thank Mike Murphy for his many years of field work on this project and Robin Montenieri for her help with graphics used in this paper.

1. Land Surface Elevation Data Collection
<ol>
	<li>GPS calibration for site
	<ol>
		<li>Locate or set a stable benchmark in a secure location at the survey site for use as the base station GPS for RTKGPS data collection.</li>
		<li>Set up base station for RTKGPS data collection at this local benchmark using best approximation of coordinates for the base station location (i.e., WAAS-corrected GPS position).</li>
		<li>With the rover GPS, visit at least three horizontal and vertical control point benchmarks within the radio communication limits of the RTKGPS (approximately 10 km radius) and record positions. 
		NOTE: Benchmarks described by the National Geodetic Survey can be searched online33 and were used here.</li>
		<li>Given the measured and published coordinates of the control points, use RTKGPS field software to perform a site calibration34, solving for coordinates of local benchmark to be used as base station. Check that coordinate residuals (horizontal and vertical) for the control points are within tolerable limits (&#177; 0.02 m for this calibration).</li>
	</ol>
	</li>
	<li>GPS point data collection
	<ol>
		<li>With base station GPS set on local benchmark and using local site calibration and field software, record RTKGPS position data into GPS data collector at approximately a 5-m horizontal spacing throughout the survey area.
		<ol>
			<li>Collect data efficiently by mounting rover GPS antenna at a measured fixed height above the ground surface on a vehicle and driving transects through the area (Figure 6).</li>
		</ol>
		</li>
		<li>For vehicle method, define transect endpoints to create parallel transects spaced 5 m apart. Import transect endpoints into GPS data collector for navigation of transects while driving. Collect points with the data collector automatically once per second while driving transects at approximately 5 m s-1 to obtain point data about every 5 m.</li>
		<li>Repeat point data collection at site as described above at later time (8 to 9 years later in this study) so that land surface elevation changes can be analyzed; the original GPS site calibration is used for all surveys and is not repeated.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/56064/56064fig6.jpg" /> 
Figure 6. RTKGPS Elevation Surface Data Collection. RTKGPS elevation surface data is collected while driving a utility vehicle through the field (a), while real-time GPS corrections are provided by the on-site base station (b).
2. DEM Creation and Processing
<ol>
	<li>Creating the DEMs
	<ol>
		<li>Import position data into GIS software and interpolate to a 5-m grid DEM. Using GIS software, cross-validate measured point elevations to interpolated elevation values and choose an interpolation method that minimizes these cross-validation errors. 
		NOTE: Ordinary kriging with a Gaussian semivariogram model was the optimal interpolation method for the elevation data at this site. Cross-validation also provides a measure of elevation accuracy for the survey method35.</li>
		<li>Repeat 2.1.1 for second set of position data to create second DEM.</li>
	</ol>
	</li>
	<li>Mapping DEM change
	<ol>
		<li>Using a raster calculator tool in GIS, subtract the most recent DEM from the original DEM to create a raster map of DEM change (Figure 5), where negative values of elevation change represent erosion and positive values represent deposition.</li>
	</ol>
	</li>
	<li>Land Classification
	<ol>
		<li>Compute land surface topographic attributes (slope, aspect, contributing area) from the first grid DEM using DEM processing software.</li>
		<li>Classify land areas as summit, sideslope, or toeslope based on slope and contributing area of each DEM grid cell. 
		NOTE: Summits are represented by low slopes and low contributing areas. Sideslopes are represented by high slopes and intermediate contributing areas. Toeslopes are represented by low slopes and high contributing areas. Slope and contributing area values defining these classifications will depend on the land surface topography at the site and are qualitatively chosen to give the desired representation of each classification area for a particular site.</li>
		<li>Divide sideslope areas by the two dominant aspects, north-facing and south-facing at this site.</li>
	</ol>
	</li>
</ol>
3. Soil Sampling
<ol>
	<li>Sample Planning
	<ol>
		<li>Reference maps in GIS to plan soil sample locations. Choose a number of locations to adequately represent all landscape positions.</li>
		<li>Upload sample location coordinates to the GPS data collector so that sample sites can be located in the field.</li>
		<li>Use prior knowledge of soils at the site to guide decisions of sample depth increments in order to capture CaCO3 variability. Pre-label sealable plastic bags to indicate sample location and depth increment.</li>
	</ol>
	</li>
	<li>Field Sampling
	<ol>
		<li>Drive to the sample sites with a utility vehicle equipped with a hydraulic soil coring machine and the RTKGPS rover antenna for navigation.</li>
		<li>Using soil coring machine and sampling tube for desired soil core diameter (5.1 cm in this study), extract soil core from each sample location (Figure 7). 
		NOTE: The number of cores extracted at each location, as well as the soil core depth and increments varied in this study. In 2001, a single core to a depth of 90 cm was taken and divided into 30 cm increments. In 2012, two soil cores were taken (within 1 m of the corresponding 2001 sample) to a depth of 30 cm and divided into 15 cm increments, with the two cores being aggregated for analyses. The 2012 method is recommended.</li>
		<li>Record RTKGPS position data (x, y, z) at each sample location.</li>
		<li>Cut the soil core into desired depth increments and transfer into pre-labelled sealable plastic bags and then place in coolers for transport back to the laboratory.</li>
		<li>Repeat field sampling after significant erosion and/or deposition has occurred (11 years between samples in this study).</li>
	</ol>
	</li>
</ol>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/56064/56064fig7.jpg" /> 
Figure 7. Soil Sampling. Soil sample locations are navigated to using a GPS-guided utility vehicle equipped with a hydraulic soil coring machine (a) so that soil cores can be extracted (b) and divided into desired depth increments.
<ol start="3">
	<li>Position Data Processing
	<ol>
		<li>Measure differences in elevations recorded at each soil sample location between the two sample dates (198 locations sampled in 2001 and 2012 in this study). 
		NOTE: Elevations for 2001 were taken from the 2001 DEM since point elevations were not recorded at the time of soil sampling. Positive changes in elevation &#62; 0.05 m are considered depositional sites, while negative changes in elevation &#60; -0.05 m are considered erosional sites.</li>
		<li>Classify each sample location as summit, North-facing sideslope, South-facing sideslope, or toeslope based on the DEM processing (see Protocol 2.3.2); classification at a single location, as defined by the slope and contributing area criteria, may be reclassified to match the dominant classification of surrounding points.</li>
		<li>Use spatial joining tools in GIS software to assign sample locations to other spatial data layers used for analyses (management strip and soil mapping unit).</li>
	</ol>
	</li>
</ol>
4. Soils Analyses
<ol>
	<li>Soil Sample Preparation
	<ol>
		<li>Dry soil samples from the field at 60 &#176;C in a laboratory oven overnight.</li>
		<li>Grind oven-dried soils to pass through a 2 mm sieve using a motorized grinder or a mortar and pestle.</li>
	</ol>
	</li>
	<li>Modified Pressure-Calcimeter Apparatus Setup
	<ol>
		<li>Set up the modified pressure-calcimeter apparatus (Figure 8) by connecting a pressure transducer (0 - 105 kPa range, 0.03 - 5 V DC output) to a power supply with 14 gauge wire and a digital voltmeter wired in line to monitor output from the transducer.
		<ol>
			<li>Attach 9.5-mm ID tubing to the base of the pressure transducer and connect tubing to an 18 gauge Luer lock hypodermic needle with a particle filter (0.6 &#181;m) in the middle to collect any reflux from reaching the pressure transducer.</li>
		</ol>
		</li>
		<li>Use serum bottles as reaction vessels connected to the pressure transducer (Figure 9).&#160; Determine the size of the serum bottle to use by wetting a metal tablespoon with water and adding approximately 5 mL of soil that you expect to have high CaCO3 concentration.&#160; Pipet 1 mL of 0.5 N H2SO4 to this soil and observe effervescence.</li>
		<li>If effervescence is high, then assume greater than 15% CaCO3 concentration and use a 100 mL serum bottle as the reaction vessel, otherwise use a 20 mL serum bottle.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/56064/56064fig8.jpg" /> 
Figure 8. Modified Pressure-calcimeter Apparatus. The modified pressure-calcimeter apparatus uses a serum bottle as the reaction vessel and a pressure transducer wired to a voltage meter to output the signal (from Sherrod et al.)30.
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/56064/56064fig9.jpg" /> 
Figure 9. Reaction Vessels for the Modified Pressure-calcimeter Method. Reaction vessels for the modified pressure-calcimeter method are serum bottles containing a 0.5 dram vial with 2 mL acid reagent and a 1 g soil sample.
<ol start="3">
	<li>Carbonate Measurement
	<ol>
		<li>Place a 1 g subsample of the prepared soil (see Protocol 4.1) into a labelled reaction vessel. For soils containing greater than 50% CaCO3, use only 0.5 g of soil.</li>
		<li>Pipet 2 mL of acid reagent (6 N HCI containing 3% FeCL2O4H2O) into a 0.5 g glass vial. Place vial into reaction vessel gently so that the solution contents do not spill out by tilting the reaction vessel almost to the side position.</li>
		<li>While keeping the reaction vessel containing the soil sample and acid vial tilted, seal with gray butyl rubber stoppers and crimp with aluminum sealing ring.</li>
		<li>Shake reaction vessel with a swirling motion to insure complete mixing of soil with acid. Place the reaction vessel on the lab bench and let the reaction proceed for at least 2 hr.</li>
		<li>While waiting for reaction vessels to complete, determine a standard curve by measuring voltages of known CaCO3 concentrations using the same reaction vessel setup as soil samples (Figure 10).&#160; Mix 100% CaCO3 with glass beads or sand on a weight percentage basis to create known CaCO3 concentrations.&#160; Include a blank sample without CaCO3.</li>
		<li>After soil sample reactions are complete, pierce the rubber septum of the reaction vessel with an 18 gauge hypodermic needle and record voltage output by the pressure transducer.</li>
		<li>Solve for CaCO3 percentage given the measured voltage and the equation determined from the standard curve (Figure 10a). 
		NOTE: Increase in pressure produced by release of CO2 is linearly related to the concentration of CaCO3 present in the soil such that: % CaCO3 = (regression coefficient * change in pressure in volts) + intercept.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/56064/56064fig10.jpg" /> 
Figure 10. CaCO3 Measurement. (a) A standard curve for CaCO3 is created using the voltage readings from the pressure transducer based on known percentages of CaCO3 (b) mixed with powder glass beads or sand.
5. Statistical Analyses
<ol>
	<li>Define two dependent variables as the change in ground surface elevations and soil surface layer CaCO3 concentrations from the first to second sample dates (2001 to 2012 in this study). Define independent or explanatory variables as management (odd- or even- numbered strip), individual strips, west or east block of strips, soil mapping unit, landscape classification, and erosional/depositional classification.</li>
	<li>Perform correlation analysis and analysis of variance to statistically quantify relationships between variables. Perform analyses in any preferred statistical package.</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accounting+for+uncertainty+in+DEMs+from+repeat+topographic+surveys:+improved+sediment+budgets.">Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets.</a> Earth Surf. Proc. and Landforms. 35 (2), 136-156 (2010).</li><li>. Survey Marks and Datasheets Available from: <a target="_blank" href="https://www.ngs.noaa.gov/datasheets/">https://www.ngs.noaa.gov/datasheets/</a> (2017)</li><li>Trimble Inc. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Trimble Access Software – General Survey. Version 1.60. Revision A. , (2011).</li><li>Erskine, R. H., Green, T. R., Ramirez, J. A., MacDonald, L. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+elevation+accuracy+and+grid+cell+size:+effects+on+estimated+terrain+attributes.">Digital elevation accuracy and grid cell size: effects on estimated terrain attributes.</a> Soil Sci. Soc. of Am. J. 71, 1371-1380 (2007).</li></ol>

The authors have nothing to disclose.

Mapped changes in elevation (Figure 5) illustrate significant erosion and deposition on an agricultural field and spatial patterns indicative of multiple controlling factors over multiple scales. From field scale patterns associated with wind, down to fine scale dendritic patterns produced by water flow, processes relevant to this study are discernable. The level of elevation change detection provided by repeated RTKGPS ground surveys appears optimal. Finer detection levels, as provided by TLS, may complicate results by introducing microtopographic features, such as cropping ridges and furrows, while coarser detection levels, as found with airborne surveys, might not be sufficient to capture fine scale patterns. Conducting RTKGPS ground surveys over an area of this size (~100 ha) takes a few days, however, due to the limitations of collecting only a single point per second and the travel time to cover the site area. Advances in airborne surveys may provide better alternative methods in the future if survey accuracies can improve to match the accuracy of ground surveys used here.
Mixed results were seen in correlating changes in surface soil CaCO3 with changes in land surface elevation, as predicted by the conceptual model. In 2001, surface soil samples for CaCO3 concentrations were only available in one depth increment over the top 30 cm. In 2012, the soil samples were divided into two 15 cm increments over the top 30 cm. An analysis of &#916;CaCO3 in only the top 15 cm will likely change these results and may prove to be more strongly related to erosion and deposition. The modified pressure-calcimeter method30 continues to serve as an efficient method for soil CaCO3 measurement.
This paper provides a detailed approach for field-scale quantification and description of erosional and depositional processes in an agricultural field. The methods described here can be applied to other sites where calcareous layers are present near the soil surface. Future work is planned for this site to compute a new DEM using and to measure surface elevation changes since 2009, when the last complete RTKGPS ground survey was conducted. Also, the soil sampling scheme of 2012 for surface soil CaCO3 will be repeated in 15 cm depth increments so that changes in the top 15 cm can be compared between the future sample and 2012.

Soil erosion threatens the sustainability of agricultural lands. Crop management, such as a conventionally-tilled winter wheat-fallow crop rotation, can accelerate erosion and deposition processes as bare soils during fallow periods are more susceptible to wind and water forces1,2,3,4,5 (Figure 1). While these processes might be evident, they can be difficult to quantify.
The purpose of this study is first to provide an efficient method for quantifying and describing spatial patterns of erosion and deposition at the field scale using global positioning system (GPS) technology and geographic information systems (GIS) mapping tools. A simple conceptual model relating these patterns to near-surface soil carbonates (CaCO3) is also presented and tested by prescribed field and laboratory methods. These relationships provide indirect measures of erosion and deposition, while validating the results of the GPS method. The present paper emphasizes the methods used in Sherrod et al. so that they can be repeated, in part or whole, for similar research in other locations6.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56064/56064fig1.jpg" /> 
Figure 1. Photos of (a) Erosion and (b) Deposition at the Study Site Following a Heavy Rainfall Event. A tractor tire track in the lower right corner of photo (b) indicates the depth of deposition at the wheat/fallow strip border.
Various direct methods for measuring soil erosion were reviewed by Stroosnijder7. Suggested methods vary with the measurement purpose and resources available, but a &#34;change in surface elevation&#34; method is recommended at the hillslope scale and provides the advantage of measuring both erosion and deposition. One way to apply this method is to install pins in the soil and monitor the change in height of the soil relative to the top of the pin7. With advances in land surveying technology, however, this labor-intensive approach can be replaced by other techniques, such as terrestrial laser scanning (TLS)8,9,10,11,12,13,14,15,16, airborne laser scanning (ALS)17,18,19,20,21, GPS6,22, advanced photogrammetry23,24, or combinations of these techniques25,26,27. While laser scanning, commonly referred to as LiDAR (Light Detection And Ranging), provides the most rapid acquisition of dense surface elevation data sets, corrections must be made to remove standing objects, such as vegetation. With millimeter-level vertical precision, TLS can detect the smallest elevation change, however Perroy et al. recommended ALS over TLS for gulley erosion estimates due to the larger scanning footprint and better instrument orientation (less topographic shadowing) for scanning into deeply incised gullies28. Real-time kinematic GPS (RTKGPS), providing centimeter-level precision without data post-processing, is used for this study. The spatial resolution and precision of RTKGPS-collected data are optimal for detecting the dominant erosional and depositional features in an agricultural field or other environments with substantial ground cover.
The pressure-calcimeter method for quantifying soil CaCO3 relies on the soil&#39;s reaction to acid in a closed system, resulting in the release of CO2. The increase in pressure within the reaction vessel at a constant temperature is linearly correlated to the amount of soil CaCO329. Modifications to the traditional pressure-calcimeter method, described by Sherrod et al., include changing the reaction vessel to serum bottles and using a pressure transducer wired to a digital voltmeter for the detection of pressure changes30. These modifications allow for lower detection limits and a higher capacity for daily soil sample runs. Gravimetric or simple titrimetric methods for soil CaCO3 measurement produced larger errors and detection limits than this modified pressure-calcimeter method30.
Conceptual Model
When direct measures of erosion and deposition are not feasible, indirect indicators of these processes may be used. Sherrod et al. hypothesized that soil surface layer CaCO3 concentration in a semi-arid climate is inversely correlated with the change in ground surface elevation (positively correlated with erosion, negatively correlated with deposition)6. The hypothesis should apply broadly, but specific relationships will depend upon site conditions (soil, vegetation, management, and climate). Soils at the test site (Table 1) typically contain a distinct calcareous layer 15-20 cm below the soil surface. Conceptually, erosion will remove the surface layer of relatively low CaCO3 concentration leaving this calcareous layer of high CaCO3 closer to the soil surface. The low CaCO3 soil is then transported to the depositional areas, causing the calcareous layer to be buried deeper below the soil surface (Figure 2). Sampling these soils over time at appropriate depth intervals, either erosion or deposition (or neither) may be inferred by CaCO3 concentration, according to this model.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Soil Series</td>
			<td>Slope</td>
			<td>Taxonomic classification</td>
			<td>Depth</td>
			<td>pH</td>
			<td>EC</td>
			<td>Total N</td>
			<td>SOC</td>
			<td>CaCO3</td>
		</tr>
		<tr>
			<td></td>
			<td>%</td>
			<td></td>
			<td>cm</td>
			<td>1:2</td>
			<td>dS m-1</td>
			<td>g kg-1</td>
			<td>g kg-1</td>
			<td>g kg-1</td>
		</tr>
		<tr>
			<td>Colby loam</td>
			<td>5-9</td>
			<td>fine-silty, mixed, superactive, calcareous, mesic Aridic Ustorthent</td>
			<td>0-15</td>
			<td>8.2</td>
			<td>0.24</td>
			<td>0.7</td>
			<td>6.1</td>
			<td>69.8</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td>15-30</td>
			<td>8.3</td>
			<td>0.24</td>
			<td>0.5</td>
			<td>4.0</td>
			<td>84.3</td>
		</tr>
		<tr>
			<td>Kim sandy loam</td>
			<td>2-5</td>
			<td>fine-loamy, mixed, active, calcareous, mesic Ustic Torriorthent</td>
			<td>0-15</td>
			<td>7.8</td>
			<td>0.26</td>
			<td>0.8</td>
			<td>7.0</td>
			<td>29.8</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td>15-30</td>
			<td>8.0</td>
			<td>0.27</td>
			<td>0.6</td>
			<td>5.0</td>
			<td>51.5</td>
		</tr>
		<tr>
			<td></td>
			<td>5-9</td>
			<td>fine-loamy, mixed, active, calcareous, mesic Ustic Torriorthent</td>
			<td>0-15</td>
			<td>8.1</td>
			<td>0.22</td>
			<td>0.6</td>
			<td>5.4</td>
			<td>26.7</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td>15-30</td>
			<td>8.1</td>
			<td>0.19</td>
			<td>0.5</td>
			<td>4.1</td>
			<td>25.8</td>
		</tr>
		<tr>
			<td>Wagonwheel loam</td>
			<td>0-2</td>
			<td>coarse-silty, mixed, superactive, mesic Aridic Calciustept</td>
			<td>0-15</td>
			<td>8.2</td>
			<td>0.23</td>
			<td>0.7</td>
			<td>5.9</td>
			<td>66.2</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td>15-30</td>
			<td>8.2</td>
			<td>0.23</td>
			<td>0.6</td>
			<td>3.7</td>
			<td>98.1</td>
		</tr>
		<tr>
			<td></td>
			<td>2-5</td>
			<td>coarse-silty, mixed, superactive, mesic Aridic Calciustept</td>
			<td>0-15</td>
			<td>8.3</td>
			<td>0.23</td>
			<td>0.8</td>
			<td>6.6</td>
			<td>52.0</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td>15-30</td>
			<td>8.4</td>
			<td>0.26</td>
			<td>0.7</td>
			<td>5.4</td>
			<td>118.3</td>
		</tr>
	</tbody>
</table>
Table 1. Soils at the Test Site. Soil mapping units and taxonomic classification, with average soil pH, electrical conductivity (EC), total N, soil organic C (SOC), and CaCO3 concentrations in the 0- to 15- and 15- to 30-cm depth increments for the Scott field in 2012 (from Sherrod et al.)6.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56064/56064fig2.jpg" /> 
Figure 2. Conceptual Soil Profiles. Conceptual soil profiles for (a) a static soil matrix with CaCO3 leached from the surface layer and precipitated in a deeper layer, (b) moderate erosion of the surface layer, and (c) moderate deposition of material above the previous surface layer. Depth intervals (left) are approximate based on site data (from Sherrod et al.)6. <a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56064/56064fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Site Description and History
The 109-ha Scott field is part of the Drake Farm in northeastern Colorado (40.61oN, 104.84oW, Figure 3) and was monitored from 2001 to 2012 for this study. Average annual precipitation and evapotranspiration were approximately 350 and 1200 mm, respectively, in this semi-arid climate, where convective rain of short duration and high intensity were common during the summer. Elevations range from 1559 to 1588 m in this undulating terrain with distinct landscape positions: summit, sideslope north-facing (side-NF), sideslope south-facing (side-SF), and toeslope (Figure 4b). Alternating strips (~120 m wide) were typically managed in this rainfed winter wheat-fallow rotation such that every other strip was fallow for about 14 months out of every 24-month rotation cycle. Shallow tillage (~7 cm), typically v-blade sweeps, occurred 4 to 6 times through the fallow period for weed control. Soils at the site were classified to have a soil-loss tolerance, or T value, of 11 Mg ha-1 year-1, where erosion rates below this T value are considered acceptable for continued agricultural production4.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56064/56064fig3.jpg" /> 
Figure 3. Site Location is Shown on a Topographic Relief Image (1011 to 4401 m) of the State of Colorado, USA. Mean elevation of the site is 1577 m.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56064/56064fig4.jpg" /> 
Figure 4. Soils map and Land Surface Elevation of the Scott Field. (a) Soils map of the Scott Field showing point soil sample locations and the crop management strips. Soil unit abbreviations are: 1 = Wagonwheel loam 0-2% slope, 2 = Wagonwheel loam 2-5% slope, 3 = Colby loam 5 - 9% slope, 4 = Kim fine sandy loam 2 - 5% slope, 5 = Kim fine sandy loam 5-9% slope; and (b) land surface elevation of the field based on the 2001 5-m grid digital elevation model (DEM), with soil sample locations shown by land classification (from Sherrod et al.)6.
The first ground surface elevation survey was collected by RTKGPS in 2001 to produce a digital elevation model (DEM) for the site. In conjunction with McCutcheon et al., an intensive soil sample (Figure 4a) was also performed in 2001, from which surface soil CaCO3 were analyzed by a modified pressure-calcimeter method30,31. Visually evident erosion and deposition occurring over the subsequent decade due to wind, predominantly from the northwest, and rainfall-runoff events prompted a second RTKGPS elevation survey in 2009 (with a portion of the field completed in 2010). Comparison of the new DEM to the original 2001 DEM via a DEM of Difference map32 confirmed significant erosion and deposition, displaying patterns which suggested multiple controlling factors for these processes (Figure 5). Given the substantial surface soil redistribution at the site and the historical soil CaCO3 data, the 2001 soil sample was repeated in 2012 to test a conceptual model of hydropedological processes6, as described in the previous section.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56064/56064fig5.jpg" /> 
Figure 5. Map of Changes (2001-2009*) in Land Surface elevation (&#916;z) on a 5-m Grid within the Scott Field in Northeastern Colorado. Crop strip numbers are labeled over the alternating winter-wheat-fallow cropping system, and section A-A&#39; is shown (details given in Figure 11). *Strips 2, 4, 6, 8 surveyed in 2010 to complete the 2009 DEM (from Sherrod et al.)6. <a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56064/56064fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table width="1330">
	<tbody>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Real-time kinematic GPS system</td>
			<td style="width:200px;">Trimble</td>
			<td style="width:119px;">Model 5800</td>
			<td style="width:759px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">GPS field data collector</td>
			<td style="width:200px;">Trimble</td>
			<td style="width:119px;">Model TSC2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">GPS field software</td>
			<td style="width:200px;">Trimble</td>
			<td style="width:119px;"></td>
			<td>Trimble Access (Trimble Survey Controller used in 2001 for site calibration but this software is no longer supported)</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Hydraulic soil coring machine</td>
			<td style="width:200px;">Giddings Machine Company</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Utility vehicle</td>
			<td style="width:200px;">John Deere</td>
			<td style="width:119px;">Gator 6x4</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">GIS software</td>
			<td style="width:200px;">ESRI</td>
			<td style="width:119px;"></td>
			<td>ArcGIS for Desktop with Spatial Analyst and Geostatistical Analyst Extensions</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Statistical software</td>
			<td style="width:200px;">SAS</td>
			<td style="width:119px;"></td>
			<td>SAS Institute Inc.</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Pressure transducer 0-105 kPa</td>
			<td style="width:200px;">Serta</td>
			<td style="width:119px;">Model 280E</td>
			<td>Setra Systems, In., Boxborough, MA</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Volt meter</td>
			<td style="width:200px;">WaveTek</td>
			<td style="width:119px;">5XL</td>
			<td>Digital meter set to read volts</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Serum Bottles</td>
			<td style="width:200px;">Wheaton</td>
			<td align="right" style="width:119px;">223747</td>
			<td>100 ml</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Serum Bottles</td>
			<td style="width:200px;">Wheaton</td>
			<td align="right" style="width:119px;">223762</td>
			<td>20 ml</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Sealing Cap 20 mm Aluminum</td>
			<td style="width:200px;">Wheaton</td>
			<td style="width:119px;">224183-01</td>
			<td>Case of 1000</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">20 mm gray butyl stopper (2-prong)</td>
			<td style="width:200px;">Wheaton</td>
			<td style="width:119px;">224100-192</td>
			<td>Septum; Case of 1000</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Hand crimper</td>
			<td style="width:200px;">Wheaton</td>
			<td style="width:119px;">W225303</td>
			<td>20 mm size</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Hand Decapper</td>
			<td style="width:200px;">Wheaton</td>
			<td style="width:119px;">W225353</td>
			<td>20 mm size</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Acid vials</td>
			<td style="width:200px;">Wheaton</td>
			<td align="right" style="width:119px;">224881</td>
			<td>0.50 dram size (2-ml)</td>
		</tr>
		<tr height="23">
			<td height="23" style="width:252px;height:23px;">Power supply</td>
			<td style="width:200px;">SR Components</td>
			<td style="width:119px;">DDU240060</td>
			<td>Class 2 Transformer AC adaptor; Input 120VAC , Output 24VDC</td>
		</tr>
		<tr height="21">
			<td height="21" style="width:252px;height:21px;">Calcium carbonate</td>
			<td style="width:200px;">Fisher</td>
			<td style="width:119px;">471-34-1</td>
			<td>500 g of 100% w/w CaCO3</td>
		</tr>
	</tbody>
</table>

measuring and mapping patterns of soil erosion and deposition related to soil carbonate concentrations under agricultural management

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO3) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO3 and changes in elevation consistent with the conceptual model; CaCO3 generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO3 to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO3.

Mapping DEM differences from 2001 and 2009 reveals erosion (red) and deposition (green) over that 8-year period, with decimeter-level changes in elevation over most areas (Figure 5). At the field-scale, erosion is dominant in the west and southwest, while deposition is seen along a northwest to southeast diagonal band on the eastern side of the field. Alternating bands of erosion and deposition are seen at the management-scale, often with abrupt changes at the management strip boundaries. Patterns related to soil types (Figure 4a) are less pronounced but do appear to coincide with topographic features with which soil types are strongly interrelated. Fine-scale erosional and depositional patterns are seen resembling water flow paths in areas of topographic convergence. Three distinct linear features of erosion, parallel to management, are seen in Strip 6. A west-east transect (A-A&#39; in Figure 5) is plotted to examine these unique features (Figure 11), which were most likely caused by tillage operations. No soil sample sites were located within these features in Strip 6.
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/56064/56064fig11.jpg" /> 
Figure 11. Elevation Profiles (2001 and 2009* DEMs) Along a West-east Transect Through Strip 6 (A-A&#39; in Figure 5). *Strip 6 elevation data collected in 2010.
Dividing the western management strips (West Block, Strips 1 - 6, Figure 5), where erosion was dominant, from the eastern management (East Block, Strips 7 - 12, Figure 5), where deposition was dominant, an inverse relationship is shown between the change in surface soil CaCO3 (&#916;CaCO3) and the change in land surface elevation (&#916;z) at the sample sites between 2001 and 2012 (Figure 12). In the erosional West Block, CaCO3 concentrations increased an average of approximately 3 g kg-1 while the average elevation decreased approximately 2 cm. Conversely, the depositional East Block showed an average &#62; 4 g/kg decrease in CaCO3 concentrations while the average elevation increased approximately 5 cm. This inverse relationship is not present when pooling all strips together over the entire field.
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/56064/56064fig12.jpg" /> 
Figure 12. Spatially Averaged Change in CaCO3 Concentration (0-30-cm depth) and Land Surface Elevation as Affected by Blocks of West (Strips 1-6) and East (Strips 7-12) Management Areas after 11 Years (2001-2012). Figure 5 shows strip numbers and boundaries. Error bars are &#177;1 SEM (from Sherrod et al.)6.
Separating erosional sample locations (&#916;z &#60; -5 cm) from depositional locations (&#916;z &#62; 5 cm), all landscape positions reveal an inverse relationship between &#916;CaCO3 and &#916;z, except north-facing sideslopes (Figure 13). Averaging across all landscapes, this inverse relationship is maintained in erosional locations; in depositional locations, however, &#916;CaCO3 is nearly unchanged. For depositional locations, deposition is greatest in toeslope positions, averaging near 13 cm. Average decreases in CaCO3 are also greatest (8.2 g/kg) in these depositional toeslope positions. For the erosional sample locations, average erosion is greatest (17.4 cm) at north-facing sideslopes, however, this did not correspond with an increase in CaCO3 as seen in all other erosional landscape positions. Assuming a soil bulk density of 1.4 g/cm3, a 17.4 cm soil loss equals an erosion rate of 221 Mg/ha/year over the study period, nearly 20 times the T value for this site. Calculation of net erosion over the entire field would require detailed knowledge of soil bulk density before erosion and after deposition. Since there is an average net gain in elevation over the field, a constant bulk density would indicate a net deposition, but since deposited soils are likely at a lower bulk density than before being transported, a net soil loss is assumed.
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/56064/56064fig13.jpg" /> 
Figure 13. Spatially Averaged Change (2001-2012) in CaCO3 Concentration (0-30-cm depth) and Land Surface Elevation as Affected by Landscape Classification in Erosional and Depositional Landscape Areas; NF is north facing and SF is south facing (from Sherrod et al.)6. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56064/56064fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
A correlation analysis of &#916;z and &#916;CaCO3 with site variables related to management (odd- or even- numbered strip), individual strips, west or east block of strips, soil mapping unit, and landscape position is presented in Table 2. The most significant correlations (p &#60; 0.0001) are seen between &#916;z and individual management strips or strip blocks (west or east). Change in CaCO3 was most significantly correlated (p = 0.016) to the soil mapping unit, while &#916;z was not correlated to soils. Less significant (p = 0.036) was the correlation between &#916;CaCO3 and the strip blocks, with which &#916;z was correlated as well. An analysis of variance shows &#916;z to be significantly affected (p &#60; 0.06) by all site variables (Table 3). Erosional class (erosional, depositional, or unchanged: EDU) most significantly (p = 0.075) affected &#916;CaCO3, followed by soil mapping unit and individual strips, both significant below a 10% significance level.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td colspan="2">Pearson correlation coefficient</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Variable</td>
			<td>Strips</td>
			<td>Block</td>
			<td>Soil</td>
			<td>Landscape</td>
			<td>&#916;CaCO3</td>
			<td>&#916;z</td>
		</tr>
		<tr>
			<td>Management</td>
			<td>0.090 (0.222)*</td>
			<td>-0.027 (0.715)</td>
			<td>0.028 (0.708)</td>
			<td>0.004 (0.959)</td>
			<td>0.019 (0.799)</td>
			<td>-0.115 (0.120)</td>
		</tr>
		<tr>
			<td>Strips</td>
			<td>-</td>
			<td>0.868 (&#60;0.0001)</td>
			<td>0.411 (&#60;0.0001)</td>
			<td>0.077 (0.295)</td>
			<td>-0.120 (0.104)</td>
			<td>0.425 (&#60;0.0001)</td>
		</tr>
		<tr>
			<td>Block</td>
			<td></td>
			<td>-</td>
			<td>0.414 (&#60;0.0001)</td>
			<td>0.114 (0.124)</td>
			<td>-0.154 (0.036)</td>
			<td>0.303 (&#60;0.0001)</td>
		</tr>
		<tr>
			<td>Soil</td>
			<td></td>
			<td></td>
			<td>-</td>
			<td>0.408 (&#60;0.0001)</td>
			<td>-0.177 (0.016)</td>
			<td>0.025 (0.738)</td>
		</tr>
		<tr>
			<td>Landscape</td>
			<td></td>
			<td></td>
			<td></td>
			<td>-</td>
			<td>-0.101 (0.172)</td>
			<td>-0.083 (0.260)</td>
		</tr>
		<tr>
			<td>&#916;CaCO3</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>-</td>
			<td>-0.001 (0.990)</td>
		</tr>
		<tr>
			<td>* Pr &#62; r in parentheses.</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2. Correlation Analysis of &#916;z and &#916;CaCO3 with Site Variables. Correlation matrix for site variables associated with elevation (z) and CaCO3 changes after 11 years as affected by management (odd or even strips), individual strips, west block (Strips 1 - 6) or east block (Strips 7 - 12), soil mapping unit, and landscape position (summit, side-north facing, side-south facing, and toeslope) (from Sherrod et al.)6.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>&#916;Elevation</td>
			<td></td>
			<td>&#916;CaCO3</td>
			<td></td>
		</tr>
		<tr>
			<td>Variable</td>
			<td>F value</td>
			<td>Pr &#62; F</td>
			<td>F value</td>
			<td>Pr &#62; F</td>
		</tr>
		<tr>
			<td>Management</td>
			<td>3.47</td>
			<td>0.0643</td>
			<td>0.07</td>
			<td>0.7957</td>
		</tr>
		<tr>
			<td>Strips</td>
			<td>50.25</td>
			<td>&#60;0.0001</td>
			<td>2.84</td>
			<td>0.0937</td>
		</tr>
		<tr>
			<td>Block</td>
			<td>7.48</td>
			<td>0.0069</td>
			<td>1.79</td>
			<td>0.1824</td>
		</tr>
		<tr>
			<td>Soil</td>
			<td>5.57</td>
			<td>0.0193</td>
			<td>3.16</td>
			<td>0.0773</td>
		</tr>
		<tr>
			<td>EDU</td>
			<td>NA*</td>
			<td>NA</td>
			<td>3.21</td>
			<td>0.0750</td>
		</tr>
		<tr>
			<td colspan="5">* NA, not applicable because EDU is determined from &#916;Elevation.</td>
		</tr>
		<tr>
			<td colspan="4">EDU = Erosional (E), Depositional (D), Unchanged (U)</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3. Analysis of Variance for Dependent Variables of Change in Elevation and Change in CaCO3 Concentration. Analysis of variance for dependent variables of change in elevation and change in CaCO3 concentration in the 0- to 30-cm depth increment after 11 years as affected by management (odd or even strips), individual strips, west block (Strips 1 - 6) or east block (Strips 7 - 12), soil mapping unit, and erosional class (EDU): erosional (&#916;z &#60; -5 cm), depositional (&#916;z &#62; 5 cm), or unchanged (-5 cm &#60; &#916;z &#60; 5 cm) (from Sherrod et al.)6.
Interpolation of the surface CaCO3 point samples in 2001 and 2012 results in the maps shown (Figures 14a, 14b), and a difference map is created from these to show interpolated &#916;CaCO3 (Figure 14c). Relatively small, concentrated areas of high and low CaCO3 seen in the 2001 map (Figure 14a) are no longer seen in 2012 where the spatial patterns are less complex (Figure 14b). This &#34;smoothing&#34; of the 2001 map to the 2012 map indicates an increase in CaCO3 in previously lower CaCO3 areas and a decrease in CaCO3 where CaCO3 was high. Patterns of &#916;CaCO3 (Figure 14c) show most increases in the far west, and some relationship to the land surface topography.
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/56064/56064fig14.jpg" /> 
Figure 14. Kriged Interpolation Maps of CaCO3 Concentrations. Kriged interpolation maps of CaCO3 concentrations in the depth interval 0 to 30 cm for (a) 2001, (b) 2012, and (c) change from 2001 to 2012. Soil sample locations are shown by circles (a, b) and elevation contours with 1-m intervals are shown in (c) (from Sherrod et al.)6.

Watch this Scientific Journal Video about Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management at JoVE.com

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes ...

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11.7K Views.  USDA Agricultural Research Service (ARS). Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.

Video: Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes and soil organic matter formation to small-scale stable-isotope biomarker probing. Coupling multiple stable isotopes such as 13C with 15N, 18O or&#160;2H has the potential to reveal even more information about complex stoichiometric relationships during biogeochemical transformations. Isotope labeled plant material has been used in various studies of litter decomposition and soil organic matter formation1-4. From these and other studies, however, it has become apparent that structural components of plant material behave differently than metabolic components (i.e. leachable low molecular weight compounds) in terms of microbial utilization and long-term carbon storage5-7. The ability to study structural and metabolic components separately provides a powerful new tool for advancing the forefront of ecosystem biogeochemical studies. Here we describe a method for producing 13C and 15N labeled plant material that is either uniformly labeled throughout the plant or differentially labeled in structural and metabolic plant components.
Here, we present the construction and operation of a continuous 13C and 15N labeling chamber that can be modified to meet various research needs. Uniformly labeled plant material is produced by continuous labeling from seedling to harvest, while differential labeling is achieved by removing the growing plants from the chamber weeks prior to harvest. Representative results from growing Andropogon gerardii Kaw demonstrate the system&#39;s ability to efficiently label plant material at the targeted levels. Through this method we have produced plant material with a 4.4 atom%13C and 6.7 atom%15N uniform plant label, or material that is differentially labeled by up to 1.29 atom%13C and 0.56 atom%15N in its metabolic and structural components (hot water extractable and hot water residual components, respectively). Challenges lie in maintaining proper temperature, humidity, CO2 concentration,&#160;and light levels in an airtight 13C-CO2 atmosphere for successful plant production. This chamber description represents a useful research tool to effectively produce uniformly or differentially multi-isotope labeled plant material for use in experiments on ecosystem biogeochemical cycling.

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

This method explains how to build and operate a continuous 13C and 15N isotope labeling chamber for uniform or differential plant tissue labeling. Representative results from metabolic and structural labeling of Andropogon gerardii are discussed.

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes ...

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Rainfall is a driving force for the transport of environmental contaminants from agricultural soils to surficial water bodies via surface runoff. The objective of this study was to characterize the effects of antecedent soil moisture content on the fate and transport of surface applied commercial urea, a common form of nitrogen (N) fertilizer, following a rainfall event that occurs within 24&#160;hr&#160;after fertilizer application. Although urea is assumed to be readily hydrolyzed to ammonium and therefore not often available for transport, recent studies suggest that urea can be transported from agricultural soils to coastal waters where it is implicated in harmful algal blooms. A rainfall simulator was used to apply a consistent rate of uniform rainfall across packed soil boxes that had been prewetted to different soil moisture contents. By controlling rainfall and soil physical characteristics, the effects of antecedent soil moisture on urea loss were isolated. Wetter soils exhibited shorter time from rainfall initiation to runoff initiation, greater total volume of runoff, higher urea concentrations in runoff, and greater mass loadings of urea in runoff. These results also demonstrate the importance of controlling for antecedent soil moisture content in studies designed to isolate other variables, such as soil physical or chemical characteristics, slope, soil cover, management, or rainfall characteristics. Because rainfall simulators are designed to deliver raindrops of similar size and velocity as natural rainfall, studies conducted under a standardized protocol can yield valuable data that, in turn, can be used to develop models for predicting the fate and transport of pollutants in runoff.

A rainfall simulator was used to apply a consistent rate of uniform rainfall to packed soil boxes in a study of the fate and transport of urea, a nonpoint source environmental contaminant. Under uniform soil and rainfall conditions, antecedent soil moisture content exerted strong control over urea loss in surface runoff.

Rainfall is a driving force for the transport of environmental contaminants from agricultural soils to surficial water bodies via surface runoff. The ...

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

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

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

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

A Protocol for Collecting and Constructing Soil Core Lysimeters

Leaching of nutrients from land applied fertilizers and manure used in agriculture can lead to accelerated eutrophication of surface water. Because the landscape has complex and varied soil morphology, an accompanying disparity in flow paths for leachate through the soil macropore and matrix structure is present. The rate of flow through these paths is further affected by antecedent soil moisture. Lysimeters are used to quantify flow rate, volume of water and concentration of nutrients leaching downward through soils. While many lysimeter designs exist, accurately determining the volume of water and mass balance of nutrients is best accomplished with bounded lysimeters that leave the natural soil structure intact. 
Here we present a detailed method for the extraction and construction of soil core lysimeters equipped with soil moisture sensors at 5 cm and 25 cm depths. Lysimeters from four different Coastal Plain soils (Bojac, Evesboro, Quindocqua and Sassafras) were collected on the Delmarva Peninsula and moved to an indoor climate controlled facility. Soils were irrigated once weekly with the equivalent of 2 cm of rainfall to draw down soil nitrate-N concentrations. At the end of the draw down period, poultry litter was applied (162 kg TN ha-1) and leaching was resumed for an additional five weeks. Total recovery of applied irrigation water varied from 71% to 85%. Nitrate-N concentration varied over the course of the study from an average of 27.1 mg L-1 before litter application to 40.3 mg L-1 following litter application. While greatest flux of nutrients was measured in soils dominated by coarse sand (Sassafras) the greatest immediate flux occurred from the finest textured soil with pronounced macropore development (Quindocqua).

A detailed method for extraction and assembly of intact soil core lysimeters and their use for study of leachate and associated loss of nutrients from surface applied poultry litter is demonstrated.

Leaching of nutrients from land applied fertilizers and manure used in agriculture can lead to accelerated eutrophication of surface water. Because the ...

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental changes. Repeated sampling to monitor these changes in forest soils is a relatively new practice that is not well documented in the literature and has only recently been broadly embraced by the scientific community. The objective of this protocol is therefore to synthesize the latest information on methods of soil resampling in a format that can be used to design and implement a soil monitoring program. Successful monitoring of forest soils requires that a study unit be defined within an area of forested land that can be characterized with replicate sampling locations. A resampling interval of 5 years is recommended, but if monitoring is done to evaluate a specific environmental driver, the rate of change expected in that driver should be taken into consideration. Here, we show that the sampling of the profile can be done by horizon where boundaries can be clearly identified and horizons are sufficiently thick to remove soil without contamination from horizons above or below. Otherwise, sampling can be done by depth interval. Archiving of sample for future reanalysis is a key step in avoiding analytical bias and providing the opportunity for additional analyses as new questions arise.

Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring programs.

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental ...

Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events

The purpose of this study is to describe the methods used to capture flow-weighted water and suspended particulates from farm canals during drainage discharge events. Farm canals can be enriched by nutrients such as phosphorus (P) that are susceptible to transport. Phosphorus in the form of suspended particulates can significantly contribute to the overall P loads in drainage water. A settling tank experiment was conducted to capture suspended particulates during discrete drainage events. Farm canal discharge water was collected in a series of two 200 L settling tanks over the entire duration of the drainage event, so as to represent a composite subsample of the water being discharged. Imhoff settling cones are ultimately used to settle out the suspended particulates. This is achieved by siphoning water from the settling tanks via the cones. The particulates are then collected for physico-chemical analyses.

Nutrients present in particulate form can contribute significantly to the overall loads in agricultural drainage waters. This study describes a novel method to capture flow-weighted water and suspended particulates from farm canal drainage over the entire duration of the drainage event.

The purpose of this study is to describe the methods used to capture flow-weighted water and suspended particulates from farm canals during drainage ...

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 ...

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 ...

Electrostatic Method to Remove Particulate Organic Matter from Soil

Estimations of soil organic carbon are dependent on soil processing methods including removal of undecomposed plant material. Inadequate separation of roots and plant material from soil can result in highly variable carbon measurements. Methods to remove the plant material are often limited to the largest, most visible plant materials. In this manuscript we describe how electrostatic attraction can be used to remove plant material from a soil sample. An electrostatically charged surface passed close to dry soil naturally attracts both undecomposed and partially decomposed plant particles, along with a small quantity of mineral and aggregated soil. The soil sample is spread in a thin layer on a flat surface or a soil sieve. A plastic or glass Petri dish is electrostatically charged by rubbing with polystyrene foam or nylon or cotton cloth. The charged dish is passed repeatedly over the soil. The dish is then brushed clean and recharged. Re-spreading the soil and repeating the procedure eventually results in a diminishing yield of particulates. The process removes about 1 to 5% of the soil sample, and about 2 to 3 times that proportion in organic carbon. Like other particulate removal methods, the endpoint is arbitrary and not all free particulates are removed. The process takes approximately 5 min and does not require a chemical process as do density flotation methods. Electrostatic attraction consistently removes material with higher than average C concentration and C:N ratio, and much of the material can be visually identified as plant or faunal material under a microscope.

Removing recently deposited and incompletely decomposed plant material from soil samples reduces the influence of temporary seasonal inputs on soil organic carbon measurements. Attraction to an electrostatically charged surface can be used to quickly remove a substantial amount of particulate organic matter.

Estimations of soil organic carbon are dependent on soil processing methods including removal of undecomposed plant material. Inadequate separation of ...

Estimation of Crystalline Cellulose Content of Plant Biomass using the Updegraff Method

Cellulose is the most abundant polymer on Earth generated by photosynthesis and the main load-bearing component of cell walls. The cell wall plays a significant role in plant growth and development by providing strength, rigidity, rate and direction of cell growth, cell shape maintenance, and protection from biotic and abiotic stressors. The cell wall is primarily composed of cellulose, lignin, hemicellulose and pectin. Recently plant cell walls have been targeted for the second-generation biofuel and bioenergy production. Specifically, the cellulose component of the plant cell wall is used for the production of cellulosic ethanol. Estimation of cellulose content of biomass is critical for fundamental and applied cell wall research. The Updegraff method is simple, robust, and the most widely used method for the estimation of crystalline cellulose content of plant biomass. The alcohol insoluble crude cell wall fraction upon treatment with Updegraff reagent eliminates the hemicellulose and lignin fractions. Later, the Updegraff reagent resistant cellulose fraction is subjected to sulfuric acid treatment to hydrolyze the cellulose homopolymer into monomeric glucose units. A regression line is developed using various concentrations of glucose and used to estimate the amount of the glucose released upon cellulose hydrolysis in the experimental samples. Finally, the cellulose content is estimated based on the amount of glucose monomers by colorimetric anthrone assay.

The Updegraff method is the most widely used method for the cellulose estimation. The main purpose of this demonstration is to provide a detailed Updegraff protocol for estimation of cellulose content in plant biomass samples.

Cellulose is the most abundant polymer on Earth generated by photosynthesis and the main load-bearing component of cell walls. The cell wall plays a ...