This study was supported by funding from a US Department of Agriculture Evans-Allen Grant (No. 1005761). The author thanks staff members at the TSU&#39;s Main Campus AREC in Nashville, Tennessee for their assistance. Maggie Syversen helped by reading the early version of the manuscript. The author appreciates the anonymous reviewers for their constructive comments and suggestions.

1. Clustered Sampling Design in a Plot
<ol>
	<li>Identify sampling zones within a research plot. Determine the number of square grids with equal length (i.e., Figure 1; Figure 3). Based on the size and shape of the research plot, the target number of square grids is expected to be six to ten so that the total number of soil samples is controlled below 30 within a plot (see step 1.3).</li>
	<li>Mark the center of each square grid (i.e., centroid) and create a circular sampling area with a diameter equal to the side length of the square grid.</li>
	<li>Stand on the centroid in the circular zone with closed eyes and throw a small stone (or another object with weight) in a random direction and distance from the centroid.
	<ol>
		<li>If the stone is dropped outside of the circular area, do it again until the first sampling location is identified.</li>
	</ol>
	</li>
	<li>Repeat step 1.3 until three random sampling locations are obtained in the circular zone.</li>
	<li>Put flags on the three sampling locations and number each flag (i.e., 1, 2, and 3).</li>
	<li>Repeat steps 1.3 - 1.5 in all other circular sampling zones until all locations are determined and numbered in a sequential order (i.e., 4, 5, 6, etc.).</li>
</ol>
2. Distance Measurements and Soil Collection in a Plot
<ol>
	<li>Choose one corner point and identify it as the origin for the sampling area in the plot.</li>
	<li>Measure horizontal and vertical distances of each flagged location relative to the origin and record them in a field notebook as x and y coordinates.</li>
	<li>Use a soil auger to take a soil core (0 - 15 cm) from each flagged location and label the bag based on the flag number. Repeat this step until soil cores are taken at all flagged locations.</li>
	<li>To minimize the influence of sampling (e.g., trampling on plants and soil in the plot), ensure that the bags with the soil samples inside stay with their respective flag until assembling all bags in the plot at once at the end of the collection.</li>
	<li>Transport the soil samples in coolers to the laboratory and process each soil core on the same day.</li>
	<li>Remove roots from each core, sieve it through a 2 mm soil sieve, and thoroughly homogenize each core sample prior to any analysis.</li>
	<li>Determine soil moisture content in each sample by oven-drying subsamples for 24 h at 105 &#176;C and ground the air-dried soil subsamples to a fine powder for a total carbon (C) analysis using an elemental analyzer4. SOC is derived based on the moisture and C contents.</li>
	<li>Weigh fresh soil subsamples (of 10 g each) and quantify the soil MBC by chloroform fumigation-K2SO4 extraction and potassium persulfate digestion methods5.</li>
	<li>Combine the SOC and MBC dataset with x and y coordinates based on the flag numbers in the plot.</li>
</ol>
3. Descriptive and Geostatistical Analyses in a Plot
<ol>
	<li>For each variable of SOC and MBC, calculate the minimum, maximum, mean, median, and standard deviation, as well as the coefficient of variation (CV).</li>
	<li>For each variable, conduct a set of geospatial analysis (i.e., trend surface analysis, autocorrelation, and kriging map) to depict the primary surface pattern, fine-scale variability, and spatial distribution. The details of the approaches of geostatistical analyses can be found in former publications4,5.</li>
</ol>
4. Exploration of SSR and the Associated Sampling Accuracy in a Plot
<ol>
	<li>Plot the SSR and relative error (&#947;) based on the CV obtained in a plot. Within each plot, the log-transformed SSR and relative error (&#947;) have a negative linear relationship (equations 1 - 3). Based on the relationship (equation 3), the number of samples required for the specified accuracy can be determined: 
	<img alt="Equation 1" src="/files/ftp_upload/58519/58519eq2.jpg" />&#160; 
	<img alt="Equation 2" src="/files/ftp_upload/58519/58519eq3.jpg" />&#160; 
	<img alt="Equation 3" src="/files/ftp_upload/58519/58519eq4.jpg" /> 
	Here, CI, <img alt="Equation 6" src="/files/ftp_upload/58519/58519eq6.jpg" />, s, n, N, CV, and denote the confidence interval, plot mean, plot standard deviation, sample number, coefficient of variation, and relative error, respectively; t0.975 = 1.96. The log-transformed sample size requirement (N) has a negative linear relationship (i.e., slope = -2) with the log-transformed relative error (&#947;).</li>
	<li>Apply the above relationship for future sampling in a plot by calculating N in equation 3 under a desired accuracy (e.g., relative error [&#947;]). Or, for a given number of soil samples already collected in a plot, apply the relationship to derive at the associated accuracy.</li>
</ol>

<ol>
	<li>Young, I. M., Crawford, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interactions+and+Self-Organization+in+the+Soil-Microbe+Complex.">Interactions and Self-Organization in the Soil-Microbe Complex.</a> Science. 304 (5677), 1634-1637 (2004).</li><li>Masoom, 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=Soil+Organic+Matter+in+Its+Native+State:+Unravelling+the+Most+Complex+Biomaterial+on+Earth.">Soil Organic Matter in Its Native State: Unravelling the Most Complex Biomaterial on Earth.</a> Environmental Science and Technology. 50 (4), 1670-1680 (2016).</li><li>Tan, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Soil Sampling, Preparation, and Analysis. , (2005).</li><li>Li, J. W., Richter, D. D., Mendoza, A., Heine, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+land-use+history+on+soil+spatial+heterogeneity+of+macro-+and+trace+elements+in+the+Southern+Piedmont+USA.">Effects of land-use history on soil spatial heterogeneity of macro- and trace elements in the Southern Piedmont USA.</a> Geoderma. 156 (1-2), 60-73 (2010).</li><li>Li, 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=Nitrogen+Fertilization+Elevated+Spatial+Heterogeneity+of+Soil+Microbial+Biomass+Carbon+and+Nitrogen+in+Switchgrass+and+Gamagrass+Croplands.">Nitrogen Fertilization Elevated Spatial Heterogeneity of Soil Microbial Biomass Carbon and Nitrogen in Switchgrass and Gamagrass Croplands.</a> Scientific Reports. 8 (1), 1734 (2018).</li><li>Chung, C. K., Chong, S. K., Varsa, E. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sampling+Strategies+for+Fertility+on+a+Stoy+Silt+Loam+Soil.">Sampling Strategies for Fertility on a Stoy Silt Loam Soil.</a> Communications in Soil Science and Plant Analysis. 26 (5-6), 741-763 (1995).</li><li>Luo, Y. Q., 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=Toward+more+realistic+projections+of+soil+carbon+dynamics+by+Earth+system+models.">Toward more realistic projections of soil carbon dynamics by Earth system models.</a> Global Biogeochemical Cycles. 30 (1), 40-56 (2016).</li><li>Li, J., Wang, G., Allison, S., Mayes, M., Luo, Y. <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+sensitivity+to+temperature+and+carbon+use+efficiency+compared+across+microbial-ecosystem+models+of+varying+complexity.">Soil carbon sensitivity to temperature and carbon use efficiency compared across microbial-ecosystem models of varying complexity.</a> Biogeochemistry. 119 (1-3), 67-84 (2014).</li><li>Conant, R. T., Ogle, S. M., Paul, E. A., Paustian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+and+monitoring+soil+organic+carbon+stocks+in+agricultural+lands+for+climate+mitigation.">Measuring and monitoring soil organic carbon stocks in agricultural lands for climate mitigation.</a> Frontiers in Ecology and the Environment. 9 (3), 169-173 (2011).</li><li>Wieder, W. R., Bonan, G. B., Allison, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+soil+carbon+projections+are+improved+by+modelling+microbial+processes.">Global soil carbon projections are improved by modelling microbial processes.</a> Nature Climate Change. 3 (10), 909-912 (2013).</li><li>Swenson, L. J., Dahnke, W. C., Patterson, D. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Sampling for Soil Testing. , (1984).</li><li>Jones, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Laboratory Guide for Conducting Soil Tests and Plant Analysis. , (2001).</li></ol>

The author has nothing to disclose.

The traditional soil sampling method lacked a quantitative basis and led to unknown accuracy, whereas the more advanced sampling strategies involved intensive soil collections and induced unaffordable costs for most soil research at the field plot scale. A simple, efficient, and reliable sampling design should be a useful tool to balance both aforementioned methods and, more importantly, inform a quantitative way to determine the number required under certain accuracy for the sake of future sampling needs. However, such a sampling design is still missing. Here, a method for manipulating a clustered sampling procedure to quantify soil spatial heterogeneity was presented and, relying on this design, to inform the number of soil samples required for future sampling under specific accuracy. There are two critical steps within the protocol. The first is to determine the sampling area and identify the sampling zone in a given plot area. Because the dimension and shape of a specific research plot can vary from one study to another, the number and length of the square grid representing the sampling zone should be modified to best fit the plot characteristics and cover the plot area as much as possible. In general, the number of square grids should be limited to eight to ten so that 24 - 30 soil samples will be collected in a given plot. This less intensive sampling requirement is acceptable for a pilot study in a plot. The second critical step is to determine the sample number required under specific accuracy. Although the number of soil samples under a desired accuracy can be derived at based on the pilot sampling strategy, other available resources need to be accounted for (e.g., labor, cost, and personnel). If the number of soil samples required for a desired accuracy exceeds the affordability, the desired accuracy should be lowered so that the number of soil samples can be recalculated. The recalculations should be repeated until the best fit is achieved to balance the desired accuracy and the available resources.
The protocol can be easily modified to fit the specific shape, area, and location of a research plot. Even within an irregular plot or a very large or small plot area, the procedure can be performed by controlling the size of the square grid to cover most of the plot area. On the other hand, when soil samples are collected beyond the circular sampling zone in the plot, they can be still accounted for in the descriptive and geostatistical analysis. The flexibility of the protocol in this regard is advantageous as it can, thus, reduce the cost of sampling.
An important limitation of this method is that the number of soil samples required for certain accuracy will depend upon the plot level CV determined by a group of 24 - 30 soil samples in the pilot soil sampling. For a highly heterogeneous plot, 30 samples or less can produce a larger CV than that based on a greater number of samples (&#62; 30). As a result, the number of soil samples calculated with the same accuracy will be larger. That is, the number of soil samples required for the same accuracy will be overestimated in the plot. For a highly homogenous plot, a smaller number of samples will produce a plot level CV similar to 30 samples, thus, resulting in an overestimation of the resource need. Therefore, for these extremely heterogeneous or homogeneous plots, the soil sample number (i.e., 30 or less) proposed in the pilot sampling design may cause unnecessary investment either in the pilot sampling stage or in future sampling.
We demonstrate significant advantages of the clustered soil sampling strategy. It provides a reliable and affordable soil sampling strategy to obtain soil spatial heterogeneity and offers a quantitative way to derive the number of soil samples required for a certain desired accuracy. Although the intensive strip or stratified sampling may provide a better description of spatial variation, the cost of conducting such sampling is too high for most soil studies. The traditional sampling is arbitrary and lacks any quantitative basis for sampling accuracy. The current protocol is superior due to its less intensive sampling requirement, ease in operating it in the field, power to reveal spatial patterns using rigorous geostatistical analysis methods, and capacity to quantitatively determine the sample size given any desired accuracy. The knowledge of the sample size required for a specific sampling accuracy will allow researchers to strategize their investment in soil sampling efforts.
Employing the efficient clustered sampling procedure allows rigorous testing of soil spatial heterogeneity and improves scientists&#39; capacity to conduct soil sampling with accuracy. The less intensive and quantitative nature of the soil sampling strategy will enable its wide application in soil research communities. Given the likely altered soil spatial heterogeneity under rapid global changes, the soil sample requirement for the same sampling accuracy in a research plot may vary over time. The proposed sample number in the pilot sampling design may vary with different soils and ecosystems. Future applications that could emerge from this work include determining the sample number for specific soils or ecosystems. Thus, further empirical work is needed on the application and identification of the method in various soils and ecosystems. Long-term and wide applications may help identify a generic sample size requirement for specific ecosystems, which can be recommended for soil researchers.

Soils are highly heterogeneous biomaterials1,2. Soil sampling is conducted to collect the most representative samples and characterize the nutrient status of a field as accurately and inexpensively as possible. Variability in a soil lies in soil spatial heterogeneity and accuracy of quantification. When spatial variation in soil is not taken into consideration, typical soil sampling can result in a substantial departure from the true mean value of a soil variable, even if the soil analysis itself is highly accurate3. For a heterogeneous research plot, variability is frequently of more importance than means3; that is, a sampling design that can accurately measure both variability and mean will be preferred.
When soil spatial variation is further altered due to land management practices4,5,6, it is more difficult to conduct soil sampling in an accurate manner. Nevertheless, concerns also arise with regard to the large variations in key soil variables (e.g., SOC and MBC)7 that are propagated to cause poor constraints of key model parameters which are critical for long-term global soil model projections under climate change8,9,10. As the cost of soil sampling to characterize field variability is a key problem, a simple, reliable, and efficient soil sampling strategy is sought.
There are many different approaches to collecting representative soil samples in a research plot, and their advantages and disadvantages are summarized in Table 1. In a traditional soil sampling (i.e., simple and random sampling), a random collection of a few to more than 10 soil samples is performed in a research plot. In particular, the number of samples in a traditional soil sampling design is always determined arbitrarily and the associated sampling error (i.e., accuracy) remains unknown.
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Sampling design</td>
			<td>Advantage</td>
			<td>Disadvantage</td>
		</tr>
		<tr>
			<td>Simple and random sampling</td>
			<td>Cost effective, quick and inexpensive, widely adopted, easy operation, optimal in homogenous site</td>
			<td>Low accuracy and high variation, &#60;5 samples</td>
		</tr>
		<tr>
			<td>Systematic sampling</td>
			<td>High accuracy and known variation, optimal in large scale heterogeneous site</td>
			<td>Cost ineffective, large sample number</td>
		</tr>
		<tr>
			<td>Stratified sampling</td>
			<td>&#160;Accurate mean estimate, relatively easy operation, optimal for clustered and stratified region</td>
			<td>Cost ineffective, large sample number (usually less than systematic/grid sampling)</td>
		</tr>
		<tr>
			<td>Compositing</td>
			<td>Cost effective, accurate mean estimate, easy operation, optimal in heterogeneous site</td>
			<td>Unknown field variation, &#62;3 samples for composite</td>
		</tr>
	</tbody>
</table>
Table 1: The advantages and disadvantages of major soil sampling designs adopted in the soil research community. The table has been summarized from Tan et al.3, Jones12, and Swenson et al.11
As compared with simple and random sampling or compositing, systematic and stratified sampling designs can achieve means with high accuracy along with associated variability (Table 1). However, they will require intensive soil sampling (e.g., a few 100 samples). Although the accuracy of, and confidence in, a soil test level increases with more soil samples collected per plot11, the requirement for a large number of soil samples is generally only applicable for a large-scale study5,11; it is well beyond the affordability of most soil research projects conducted at the scale of field plots due to constraints in resources. A sampling design is preferred to balance the tradeoffs of these different methods.
A key issue for a soil sampling design is to determine the number of soil samples required and the associated accuracy given the research questions and field conditions. For instance, a reduction in the number of soil samples is possible in less disturbed sites while still achieving the same level of precision6, suggesting a need to explicitly quantify the spatial heterogeneity (i.e., nature and occurrence of soil variability) prior to soil sampling3. In fact, no such pilot sampling is recommended in most soil sampling designs. Field scientists frequently fail to recognize the importance of estimating statistical power when they design experiments.
To improve the experimental rigor in soil sampling, a simple and efficient sampling method is presented in this study. The new design shall not only enable the accurate characterization of soil nutrient levels and variability but also, by accounting for soil spatial heterogeneity, provide a quantitative way to inform the number of soil samples and the associated sampling accuracy for future research. The new soil sampling design should help researchers identify optional strategies that fit their sampling and research needs. The overall goal of this method is to provide soil biogeochemists and ecologists with a quantitative and manipulative approach to optimize soil sampling strategies in the context of field research.

<table border="0" cellpadding="0" cellspacing="0" style="width:632px;" width="632">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Soil auger</td>
			<td style="width:71px;">AMS</td>
			<td style="width:119px;">350.05</td>
			<td style="width:227px;">For soil collection</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Screwdriver</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td style="width:119px;">19-313-447</td>
			<td>For soil collection</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Rope</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td style="width:119px;">19-313-429</td>
			<td>For delineating sampling zone</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">FatMax 35 ft. Tape Measure</td>
			<td style="width:71px;">Home Depot</td>
			<td style="width:119px;">#215880</td>
			<td>For measuring distances</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Marking flag</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td>S99537</td>
			<td>For marking sampling locations</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">Plastic Zipper Seal Storage Bag</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td>09-800-16</td>
			<td>For soil collection</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Sharpie</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td>50-111-3135</td>
			<td>For soil collection</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Marking pencil</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td>50-294-45</td>
			<td>For recording data in field</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Lab notebook</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td>11-903&#160;</td>
			<td>For recording data in field</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">ArcGis 10.3</td>
			<td style="width:71px;">ESRI</td>
			<td style="width:119px;"></td>
			<td>For producing kriging map</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Sieve</td>
			<td style="width:71px;">Fisher Scientific</td>
			<td>04-881G&#160;</td>
			<td>For sieving soil sample</td>
		</tr>
	</tbody>
</table>

sampling soils in a heterogeneous research plot

Soils are highly heterogeneous. In general, the number of soil samples required for soil research has always been determined arbitrarily and the associated accuracy is unknown. Here, we present a detailed protocol for efficient and clustered soil sampling in a research plot and, relying on a pilot sampling using this design, for demonstrating soil spatial heterogeneity and informing reasonable sample sizes and associated accuracy for future study. The protocol mainly comprises four steps: sampling design, field collection, soil analysis, and geostatistical analysis. The step-by-step procedure is modified according to former publications. Two examples will be presented to demonstrate contrasting spatial distributions of soil organic carbon (SOC) and soil microbial biomass carbon (MBC) under different management practices. In addition, we present a strategy to determine the sample size requirement (SSR) given a certain level of accuracy based on the plot-level coefficient of variation (CV). The field sampling protocol and the quantitative determination of the sample size will assist researchers in seeking feasible sampling strategies to meet research needs and resources' availability.

The above approach has been employed in two case studies, one in a Southern US rural region and another in Middle Tennessee.
In the rural Southern Piedmont region, three land-use types were selected, including 1) uncultivated oak-hickory hardwood forests, 2) cultivated fields where conventional tillage and fertilization are used annually to produce wheat, sorghum, and corn, and 3) old-field pine forests that are each about 50 years in age since the last cultivation4. Three independently replicated 30 x 30 m plots were identified from the area for each land use. In each plot, a cluster soil sampling design was applied (Figure 1). Each circular zone had a 5 m radial distance from each centroid. Twenty-seven cores were collected from each of the nine plots, 81 cores per land use, and 243 cores in total. SOC was quantified by a CHN analyzer. The major finding was that cultivated land substantially homogenizes the spatial heterogeneity of SOC and other variables4. The SSR differed among land uses with a generally ascending order as old-field forest &#62; regenerated pine forest &#62; cultivated cropland (Figure 2). Exceptions are that one hardwood forest plot had an SSR as small as the cultivated plot, and one pine plot had an SSR as large as the hardwood plot (Figure 2). Taking &#947; = 0.1 or 10% as an example, SSR was 4, 10, and 30 (cultivated cropland), 80, 85, and 300 (pine forest), and 25, 200, and 350 (hardwood). If only three soil samples were collected in all plots, the relative error would have been ~10% - 30% (cultivated cropland), ~50% - 80% (pine forest), and ~28% - 100% (hardwood).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58519/58519fig1.jpg" /> 
Figure 1: An illustration of a clustered random sampling design within a 30 x 30 m research plot at the Calhoun Experimental Forest, SC, USA4. The filled circles represent centroids (n = 9). The large dashed circle represents the sampling area around one centroid (radius = 5 m). Xs represent sample locations determined from randomly chosen directions and distances from a centroid. This figure has been modified from Li et al.4. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58519/58519fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58519/58519fig2.jpg" /> 
Figure 2: Plot of the sample size requirement (SSR) and relative error (&#947;) for SOC of hardwood forest, pine forest, and cultivated cropland. The log scale was applied on both axes. The dotted lines represent cultivated soils, the grey lines pine-forest soils, and the dark lines hardwood forest soils. Three different lines for each land use correspond to three replicate plots. This figure has been modified from Li et al.4 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58519/58519fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In the Tennessee State University (TSU) Main Campus Agriculture Research and Extension Center (AREC) in Nashville, TN, USA (36.12&#176; N, 36.98&#176; W, elevation 127.6 m) in 2011, a field switchgrass experiment was established with three nitrogen (N) fertilization treatments in a randomized block design5. The crop type is of the &#39;Highlander&#39; variety of eastern &#39;Alamo&#39; switchgrass (Panicum virgatum L.). The three N treatments included no N fertilizer input (NN), low N fertilizer input (LN: 84 kg of N ha-1 in urea), and high N fertilizer input (HN: 168 kg of N ha-1 in urea). Within each plot, a rectangular area of 2.75 x 5.5 m zone was identified and further divided into eight square grids of 1.375 x 1.375 m. Within each circular zone, a centroid was identified, and three cores were collected with a random direction and distance relative to each centroid (Figure 3). A total of 24 cores were thus collected from each of 12 plots, yielding 288 soil cores. The MBC in each core was quantified by chloroform fumigation-K2SO4 extraction and potassium persulfate digestion methods. The major finding was that the N fertilization generally enhanced the spatial heterogeneity of MBC in the switchgrass cropland. The SSR was generally greater with fertilization (Figure 4). One exception is that the SSR for an HN plot was lower than that of the NN plot (Figure 4). Taking &#947; = 0.1 or 10% as an example, SSR was 10 and 20 in two replicated plots (NN), 30 and 50 (LN), and 15 and 70 (HN). If only three soil samples were collected in all plots, the relative error would have been ~20% - 25% (NN), ~26% - 35% (LN), and ~20% - 40% (hardwood).
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58519/58519fig3.jpg" /> 
Figure 3: Illustration of a clustered random sampling design within a 2.75 x 5.5 m plot in a fertilization experimental site at the Tennessee State University (TSU) Agricultural Research Center in Nashville, TN, USA. The filled circles represent centroids (n = 8) and each plot consisted of eight centroids in each square grid (of 1.375 x 1.375 m). In each subplot, a circular area was determined for soil sampling. Xs represent sample locations determined from random directions and distances from a centroid within each circular sampling area (dashed circle). This figure has been modified from Li et al.5 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58519/58519fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58519/58519fig4.jpg" /> 
Figure 4: Plot of the sample size requirement (SSR) and relative error (&#947;) for MBC under three fertilization treatments. The log scale was applied on both axes. The dotted lines represent cultivated soils, the grey lines pine-forest soils, and the dark lines hardwood forest soils. NN = no N fertilizer input; LN = low N fertilizer input; and HN = high N fertilizer input. Two different lines for each land use correspond to two replicate plots. This figure has been modified from Li et al.5. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58519/58519fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Sampling Soils in a Heterogeneous Research Plot at JoVE.com

Sampling Soils in a Heterogeneous Research Plot

The traditional soil-sampling procedure determines the number of soil samples arbitrarily. Here, we provide a simple yet efficient clustered soil-sampling design to demonstrate soil spatial heterogeneity and quantitatively determine the number of soil samples required and the associated sampling accuracy.

Soils are highly heterogeneous. In general, the number of soil samples required for soil research has always been determined arbitrarily and the ...

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34.3K Views.  Tennessee State University. The traditional soil-sampling procedure determines the number of soil samples arbitrarily. Here, we provide a simple yet efficient clustered soil-sampling design to demonstrate soil spatial heterogeneity and quantitatively determine the number of soil samples required and the associated sampling accuracy.

Video: Sampling Soils in a Heterogeneous Research Plot

Design and Construction of an Urban Runoff Research Facility

As the urban population increases, so does the area of irrigated urban landscape. Summer water use in urban areas can be 2-3x winter base line water use due to increased demand for landscape irrigation. Improper irrigation practices and large rainfall events can result in runoff from urban landscapes which has potential to carry nutrients and sediments into local streams and lakes where they may contribute to eutrophication. A 1,000 m2 facility was constructed which consists of 24 individual 33.6 m2 field plots, each equipped for measuring total runoff volumes with time and collection of runoff subsamples at selected intervals for quantification of chemical constituents in the runoff water from simulated urban landscapes. Runoff volumes from the first and second trials had coefficient of variability (CV) values of 38.2 and 28.7%, respectively. CV values for runoff pH, EC, and Na concentration for both trials were all under 10%. Concentrations of DOC, TDN, DON, PO4-P, K+, Mg2+, and Ca2+ had CV values less than 50% in both trials. Overall, the results of testing performed after sod installation at the facility indicated good uniformity between plots for runoff volumes and chemical constituents. The large plot size is sufficient to include much of the natural variability and therefore provides better simulation of urban landscape ecosystems.

This paper describes the design, construction, and function of a 1,000 m2 facility containing 24 individual 33.6 m2 field plots equipped for measuring total runoff volumes with time and collection of runoff subsamples at selected intervals for quantification of chemical constituents in the runoff water from simulated home lawns.

As the urban population increases, so does the area of irrigated urban landscape. Summer water use in urban areas can be 2-3x winter base line water use ...

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Potentially toxic chemicals are routinely applied to land to meet growing demands on waste management and food production, but the fate of these chemicals is often not well understood. Here we demonstrate an integrated field lysimetry and porewater sampling method for evaluating the mobility of chemicals applied to soils and established vegetation. Lysimeters, open columns made of metal or plastic, are driven into bareground or vegetated soils. Porewater samplers, which are commercially available and use vacuum to collect percolating soil water, are installed at predetermined depths within the lysimeters. At prearranged times following chemical application to experimental plots, porewater is collected, and lysimeters, containing soil and vegetation, are exhumed. By analyzing chemical concentrations in the lysimeter soil, vegetation, and porewater, downward leaching rates, soil retention capacities, and plant uptake for the chemical of interest may be quantified. Because field lysimetry and porewater sampling are conducted under natural environmental conditions and with minimal soil disturbance, derived results project real-case scenarios and provide valuable information for chemical management. As chemicals are increasingly applied to land worldwide, the described techniques may be utilized to determine whether applied chemicals pose adverse effects to human health or the environment.

Field lysimetry and porewater sampling allow researchers to evaluate the fate of chemicals applied to soils and established vegetation. The goal of this protocol is to demonstrate how to install required instrumentation and collect samples for chemical analysis during integrated field lysimetry and porewater sampling experiments.

Potentially toxic chemicals are routinely applied to land to meet growing demands on waste management and food production, but the fate of these chemicals ...

Soil Sampling and Isolation of Entomopathogenic Nematodes (Steinernematidae, Heterorhabditidae)

Entomopathogenic nematodes (a.k.a. EPN) represent a group of soil-inhabiting nematodes that parasitize a wide range of insects. These nematodes belong to two families: Steinernematidae and Heterorhabditidae. Until now, more than 70 species have been described in the Steinernematidae and there are about 20 species in the Heterorhabditidae. The nematodes have a mutualistic partnership with Enterobacteriaceae bacteria and together they act as a potent insecticidal complex that kills a wide range of insect species.
Herein, we focus on the most common techniques considered for collecting EPN from soil. The second part of this presentation focuses on the insect-baiting technique, a widely used approach for the isolation of EPN from soil samples, and the modified White trap technique which is used for the recovery of these nematodes from infected insects. These methods and techniques are key steps for the successful establishment of EPN cultures in the laboratory and also form the basis for other bioassays that consider these nematodes as model organisms for research in other biological disciplines. The techniques shown in this presentation correspond to those performed and/or designed by members of S. P. Stock laboratory as well as those described by various authors.

Soil Sampling and Isolation of Entomopathogenic Nematodes Steinernematidae, Heterorhabditidae

Entomopathogenic nematodes are soil-inhabiting roundworms that parasitize a wide range of insects. We demonstrate sampling methods used for the isolation of these nematodes from the soil using two techniques: the insect baiting and the modified White trap, for the recovery of nematodes from soil samples and infected insect cadavers, respectively.

Entomopathogenic nematodes (a.k.a. EPN) represent a group of soil-inhabiting nematodes that parasitize a wide range of insects. These nematodes belong to ...

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 ...

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

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

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

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

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

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies

Most of the trace metal concentrations in ambient waters obtained a few decades ago have been considered unreliable owing to the lack of contamination control. Developments of some techniques aiming to reduce trace metal contamination in the last couple of decades have resulted in concentrations reported now being orders of magnitude lower than those in the past. These low concentrations often necessitate preconcentration of water samples prior to instrumental analysis of samples. Since contamination can appear in all phases of trace metal analyses, including sample collection (and during preparation of sampling containers), storage and handling, pretreatments, and instrumental analysis, specific care needs to be taken in order to reduce contamination levels at all steps. The effort to develop and utilize "clean techniques" in trace metal studies allows scientists to investigate trace metal distributions and chemical and biological behavior in greater details. This advancement also provides the required accuracy and precision of trace metal data allowing for environmental conditions to be related to trace metal concentrations in aquatic environments.
This protocol that is presented here details needed materials for sample preparation, sample collection, sample pretreatment including preconcentration, and instrumental analysis. By reducing contamination throughout all phases mentioned above for trace metal analysis, much lower detection limits and thus accuracy can be achieved. The effectiveness of "clean techniques" is further demonstrated using low field blanks and good recoveries for standard reference material. The data quality that can be obtained thus enables the assessment of trace metal distributions and their relationships to environmental parameters.

Special care using "clean techniques" is required to properly collect and process water samples for trace metal studies in aquatic environments. A protocol for sampling, processing, and analytical procedures with the aim of obtaining reliable environmental monitoring data and results with high sensitivity for detailed trace metal studies is presented.

Most of the trace metal concentrations in ambient waters obtained a few decades ago have been considered unreliable owing to the lack of contamination ...

A Bending Test for Determining the Atterberg Plastic Limit in Soils

The thread rolling test is the most commonly used method to determine the plastic limit (PL) in soils. It has been widely criticized, because a considerable subjective judgment from the operator that carries out the test is involved during its performance, which may affect the final result significantly. Different alternative methods have been put forward, but they cannot compete with the standard rolling test in speed, simplicity and cost.
In an earlier study by the authors, a simple method with a simple device to determine the PL was presented (the &#34;thread bending test&#34; or simply &#34;bending test&#34;); this method allowed the PL to be obtained with minimal operator interference. In the present paper a version of the original bending test is shown. The experimental basis is the same as the original bending test: soil threads which are 3 mm in diameter and 52 mm long are bent until they start to crack, so that both the bending produced and its related moisture content are determined. However, this new version enables the calculation of PL from an equation, so it is not necessary to plot any curve or straight line to obtain this parameter and, in fact, the PL can be achieved with only one experimental point (but two experimental points are recommended).
The PL results obtained with this new version are very similar to those obtained through the original bending test and the standard rolling test by a highly experienced operator. Only in particular cases of high plasticity cohesive soils, there is a greater difference in the result. Despite this, the bending test works very well for all types of soil, both cohesive and very low plasticity soils, where the latter are the most difficult to test via the standard thread rolling method.

The traditional standardized test for determining the plastic limit in soils is performed by hand, and the result varies depending on the operator. An alternative method based on bending measurements is presented in this study. This allows the plastic limit to be obtained with a clear and objective criterion.

The thread rolling test is the most commonly used method to determine the plastic limit (PL) in soils. It has been widely criticized, because a ...

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about the overall structure of terrestrial microbial communities. PLFA profiling has been extensively used in a range of ecosystems as a biological index of overall soil quality, and as a quantitative indicator of soil response to land management and other environmental stressors.
The standard method presented here outlines four key steps: 1. lipid extraction from soil samples with a single-phase chloroform mixture, 2. fractionation using solid phase extraction columns to isolate phospholipids from other extracted lipids, 3. methanolysis of phospholipids to produce fatty acid methyl esters (FAMEs), and 4. FAME analysis by capillary gas chromatography using a flame ionization detector (GC-FID). Two standards are used, including 1,2-dinonadecanoyl-sn-glycero-3-phosphocholine (PC(19:0/19:0)) to assess the overall recovery of the extraction method, and methyl decanoate (MeC10:0) as an internal standard (ISTD) for the GC analysis.

Phospholipid fatty acids provide information about the structure of soil microbial communities. We present methods for extraction from soil samples with a single-phase chloroform mixture, fractionation of extracted lipids using solid phase extraction columns, and methanolysis to produce fatty acid methyl esters, which are analyzed by capillary gas chromatography.

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about ...

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 ...

A New Portable In Vitro Exposure Cassette for Aerosol Sampling

This protocol introduces a new in vitro exposure system, capable of being worn, including its characterization and performance. Air-liquid interface (ALI) in vitro exposure systems are often large and bulky, making transport to the field and operation at the source of emission or within the breathing zone difficult. Through miniaturization of these systems, the lab can be brought to the field, expediting processing time and providing a more appropriate exposure method that does not alter the aerosol prior to contacting the cells. The Portable In vitro Exposure Cassette (PIVEC) adapts a 37 mm filter cassette to allow for in vitro toxicity testing outside of a traditional laboratory setting. The PIVEC was characterized using three sizes of copper nanoparticles to determine deposition efficiency based on gravimetric and particle number concentration analysis. Initial cytotoxicity experiments were performed with exposed lung cells to determine the ability of the system to deposit particles while maintaining cell viability. The PIVEC provides a similar or increased deposition efficiency when comparing to available perpendicular flow in vitro exposure devices. Despite the lower sample throughput, the small size gives some advantages to the current in vitro ALI exposure systems. These include the ability to be worn for personal monitoring, mobility from the laboratory to the source of emission, and the option to set-up multiple systems for spatial resolution while maintaining a lower user cost. The PIVEC is a system capable of collecting aerosols in the field and within the breathing zone onto an air-interfaced, in vitro model.

A New Portable In Vitro Exposure Cassette for Aerosol Sampling

Here, we present a protocol to perform portable cellular aerosol exposures and measure cellular response. The method uses cells, grown at the air-liquid interface, mimicking in vivo physiology. Cellular response to copper nanoparticle aerosols was observed as oxidative stress through reactive oxygen species generation and cytotoxicity as lactate dehydrogenase release.

This protocol introduces a new in vitro exposure system, capable of being worn, including its characterization and performance. Air-liquid interface (ALI) ...