Funding sources used to support the research include a US National Science Foundation (NSF) HBCU&#8722;EiR (No. 1900885), a US Department of Agriculture (USDA) Agricultural Research Service (ARS) 1890s Faculty Research Sabbatical Program (No. 58-3098-9-005), a USDA NIFA grant (No. 2021-67020-34933), and a USDA Evans&#8722;Allen Grant (No. 1017802). We thank assistance received from staff members at the TSU&#39;s Main Campus Agriculture Research and Extension Center (AREC) in Nashville, Tennessee.

1. Temperature monitoring and programming
<ol>
	<li>Identify a sampling zone within a research plot. Install one or a few automatic temperature probes in soils at 10 cm depth. Connect the weather station to a computer via the data transmission cable and open the software on the computer.</li>
	<li>Click on the Launch/ Properties toolbar button to configure the logger for the external sensors being used.</li>
	<li>On the Properties screen, set the logger/station name (i.e., Soil incubation exp.) and the data collection interval (i.e., 60 min). Then, on the Properties screen, click Enabled on the external sensor ports being used and select the sensor/unit from the dropdown button for each sensor port (i.e., Port A; &#34;Enabled&#34;: Temperature &#730;C). Finally, click on OK to save the settings.</li>
	<li>Monitor the probes&#39; reading weekly to avoid malfunction and download the dataset once a month. Obtain a complete record for several months covering the growing season (i.e., April to September).</li>
	<li>Conduct data analysis of the temperature records. Obtain the mean hourly temperature of the growing season by averaging all observations.
	<ol>
		<li>Obtain the mean temperature of each hour on a daily basis by averaging temperatures of the same hour across all days during the growing season.</li>
	</ol>
	</li>
	<li>In the sophisticated chamber, launch the software and click on the Profile button on the main menu screen to create a new file. In the file name input line, enter &#34;SW low&#34;. By clicking on the Instant Change option, enter 15.9 &#176;C as an initial temperature as obtained in step 1.5, and enter 2 on the Minutes row to maintain the temperature for 2 min and click on the Done button. Then, under the Ramp Time option, enter 15.9 &#176;C as the target set point and on the Hours row enter 850 h to sustain the temperature. Fianlly, click on the Done button.
	<ol>
		<li>Repeat the above step in the second chamber by adding 5 &#176;C to each temperature node and create a new file name &#34;SW high&#34;.</li>
		<li>Repeat step 1.4 in the third chamber by adding 23 additional steps corresponding to 23 observed hourly soil temperatures as obtained in step 1.5.1. At the last step, called JUMP, set 42 repeated loops (Jump Count 42). This leads to the scenario of gradual warming or GW low.</li>
		<li>Repeat the above step in the fourth chamber with 5 &#176;C added to each temperature node. This will allow a simulation of varying temperatures for 42 days at a higher temperature level (i.e., GW high).</li>
	</ol>
	</li>
	<li>Conduct a preliminary run for 24 h and output the temperatures recorded by the four chambers. Plot the temperatures recorded by the chambers against those as programmed (Figure 2A-D).
	<ol>
		<li>If the temperatures achieved in the chamber match the temperatures as programmed by a temperature difference &#60;0.1 &#176;C during the 24 h (Figure 2A,B,E,F), the chambers are suitable for the soil incubation experiment.</li>
		<li>If the criteria were not satisfied in any of these chambers, repeat another 24 h test or seek a new chamber.</li>
	</ol>
	</li>
</ol>
2. Soil collection and homogenizing
<ol>
	<li>Near the temperature probe area, collect five soil samples at 0-20 cm depth and put them into one plastic bag after removing the surface litter layer.</li>
	<li>Mix the sample thoroughly by twisting, pressing, and mingling the materials in the bag until no individual soil sample is visible.</li>
	<li>Store the samples in a cooler filled with ice packs and transport the samples to the lab immediately.</li>
	<li>Remove the roots in each core, sieve it through a soil sieve of 2 mm, and thoroughly mix and homogenize the sample prior to the following analysis.</li>
</ol>
3. Laboratory incubation
<ol>
	<li>Prior to incubation, weigh 10.0 g of fresh soil, oven-dry it for 24 h at 105 &#176;C, and weigh the dry soil. Derive the difference between fresh and dry soil samples and calculate the ratio of difference over dry soil weight to determine the soil moisture content in a spreadsheet.</li>
	<li>Use the derived moisture content to calculate the soil microbial biomass carbon (MBC), extracellular enzyme activity (EEA), and soil heterotrophic respiration as described in the following steps. These data will help understand the treatment effects on soil respiration and the underlying microbial mechanisms.</li>
	<li>Prior to incubation, weigh the field moist soil subsample (10 g) and quantify the soil MBC by chloroform fumigation-K2SO4 extraction and potassium persulfate digestion methods18.</li>
	<li>Prior to incubation, weigh the subsample of field moist soil (1.0 g) and measure soil hydrolytic and oxidative EEA19.</li>
	<li>Weigh 16 field moist soil subsamples (15.0 g equivalent of dry weight) in 16 polyvinyl chloride (PVC) cores (5 cm diameter, 7.5 cm tall) sealed with glass fiber paper on the bottom.</li>
	<li>Place the PVC cores in Mason jars (~1 L) lined with a bed of glass beads to ensure that the cores do not absorb moisture.</li>
	<li>Place four jars in each of the four chambers as described in step 1.4. Turn on the chambers and launch the program simultaneously in four chambers.</li>
	<li>During the incubation, at 2 h, days 1, 2, 7, 14, 21, 28, 35, and 42, take all jars in each of four chambers and use a portable CO2 gas analyzer to measure soil respiration rate (Rs) by putting the analyzer&#39;s collar to the top of each jar.</li>
	<li>Destructively collect all jars at the end of incubation (i.e., day 42) and quantify soil MBC as described in step 3.3.</li>
	<li>Destructively collect all jars at the end of incubation (i.e., day 42) and quantify soil enzyme activity as described in step 3.4.</li>
</ol>
4. Warming effect comparison
<ol>
	<li>By assuming a constant respiration rate (Rs) between two consecutive collections, use the respiration rate times the duration to derive the cumulative respiration (Rc).</li>
	<li>Conduct a three-way repeated measures analysis of variance (ANOVA) to test the main and interactive effects of time, temperature (warming), and temperature mode (warming scenario) on Rs and Rc. In addition, conduct a two-way ANOVA to test warming and warming scenario effects on MBC and EEA.</li>
</ol>

The author has nothing to disclose.

The constant temperature control method has been applied widely (Table 1). However, the magnitude and temporal pattern of temperature implemented in these procedures poorly simulate soil temperature observed in the field condition. Despite the emerging efforts imitating the diurnal pattern in the past, such studies were scarce and failed to clarify the equipment and procedure; neither did they validate the temperature simulation regarding accuracy and reliability16,17. As the community strived to improve its understanding of soil warming responses, optimizing the soil incubation procedure with realistic temperature and feasible control is imperative. Nevertheless, such new methods have not been developed, and thus, a standard method for future incubation experiments is still out of reach. In the face of the increasing complexity of global temperature change in magnitude, amplitude, seasonality, duration, and extremality, a comprehensive procedure is in high demand.
Here, a method for manipulating a diurnal temperature change procedure was presented, relying upon the sophisticated chamber, to offer the capacity to establish constant, linear, and nonlinear temperature change and subsequently various warming scenarios for meeting future research needs. There are four critical steps within the protocol. The first is to determine soil temperature in the field condition. Because the soil type and depth of interest as well as the land use type can vary from one study to another, the number of temperature probes needed for the specific research site should be modified to best fit the actual conditions as much as possible.&#160;In general, soil depth for temperature probes shall meet the most research needs at 0-20 cm, and the number of probes to represent the soil temperature should be limited to one to three. The key is to achieve a long-term continuous and consecutive temperature record in at least one typical soil location.
The second critical step is to set up the program to achieve the targeted temperature magnitude and pattern in the chamber. Because of the high sensitivity and accuracy of chamber (Figure 4), it is feasible to program for an accurate representation of temperature as observed in the field condition. Although the current protocol only presented the observed hourly temperature as targeted in the chamber, a more frequent soil temperature monitoring, such as 30 min, 15 min, or even shorter, can be achieved through this procedure. Nevertheless, a test of the target and chamber temperatures must be conducted over 24 h, and prior to experiment, the test results must meet the criteria of less than 0.1 &#176;C between the target and chamber temperatures at all time points. The more frequent the temperature observation is selected to simulate, the more steps are needed to set up the program in the chamber prior to the experiment.
The third critical step is to conduct the incubation itself. To reduce the influence of soil heterogeneities63, homogenizing soil samples is key, and at least three replicates for each treatment are recommended. Prior to incubation, a pre-incubation treatment is required, and the current procedure can facilitate pre-treatment by programming the temperature and duration before the official start of the experiment. This is advantageous for one to reduce the experimental disturbance and orchestrate the entire incubation seamlessly. The last critical step is to include both constant temperature and varying temperature treatments so that a comparison can be made as to the soil warming responses.
This protocol can be easily modified to allow one to manipulate the magnitude, amplitude, and duration of temperature change. For example, extreme temperatures during a heat wave in summer and sudden frost in early spring due to climate change, can be represented using this procedure, in addition to its capacity to account for their varying duration and intensity. Simulating the regular and irregular temperatures in combination also allow one to simulate long-term complex temperature change effects as projected in the future. As summarized in Table 2, those warming scenarios that have been studied in many distinct studies can be accomplished collectively in one study. This protocol is expected to provide a sophisticated method to simulate temperature in soil incubation studies. With hope for a wide application, the adoption of this protocol will help identify or validate a more accurate method for future soil warming studies based on laboratory incubation.
An important limitation of the procedure is that the chamber used in the current protocol has a relatively small volume, thus is only able to accommodate nine incubation jars in each chamber. Though a smaller jar will increase the capacity of the chamber, a big volume of chamber is recommended. A new model (e.g., TestEquity 1007) will offer eight times more capacity and is thus recommended for large scale experiments. Despite the improvement of temperature control procedure in soil incubations, the potential complications with moisture and soil homogenization will not be relieved by adopting the current protocol.
We demonstrate significant advantages of the sophisticated temperature control procedure. It provides a reliable and affordable temperature control strategy to obtain accurate temperature simulation and offers a feasible way to improve soil incubation experiment required for a better understanding of soil warming responses. Although the constant temperature control is widely accepted and logistically easy to operate, the artifacts of long-term constant temperature on soil microbial communities may divert efforts to capture the genuine soil responses. The other reported laboratory warming methods are largely less controllable and replicable. The current protocol is superior due to its easy operation, high accuracy and replicability of temperature simulation, explicit programing, and capacity to combine various temperature change scenarios in a single experiment. The feasibility of temperature control with high accuracy will allow researchers to explore various temperature change scenarios.

Global surface temperature is expected to increase this century by 1.8-6.4 &#176;C1,2. Global warming may increase CO2 flux from soil to the atmosphere, resulting in positive feedback with warming3,4,5,6. Because microbial communities play a critical role in regulating soil respiratory responses to warming7,8, the changes in microbial respiration and the underlying microbial mechanisms with warming have been a research focus. Though soil warming experiments deployed in the field condition, via a heating cable9 and an open top chamber10, were advantageous in capturing natural soil features such as temperature11, their high cost for installation and maintenance have limited their application. Alternatively, soil incubation experiments subject to different temperatures are a favorable choice. The primary advantage of soil incubation in a laboratory is that the well-controlled environmental conditions (e.g., temperature) are able to disentangle the one-factor effect from other confounding factors in a field experimental setting12,13. Despite differences between growth chamber and field experiments (e.g., plant growth), translation from lab results to the field are readily available14. Incubating soil samples in a laboratory setting could help improve our mechanistic understanding of soil response to warming15.
Our literature review identified several temperature control methods and, consequently, distinct temperature change modes in past soil incubation studies (Table 1). First, instruments used to control temperature are mostly through an incubator, growth chamber, water bath, and in a rare case, heating cable. Given these instruments, three typical temperature change patterns have been generated (Figure 1). These include the most implemented mode, constant temperature (CT), linear change (LC) with a non-zero constant temperature change rate, and nonlinear change (NC) featured with a diurnal type of temperature. For a case of CT pattern, the temperature may vary in magnitude over time, though constant temperature remains for a certain time period during the incubation (Figure 1B). For LC, the rate of temperature change could vary in different studies at more than two orders of magnitude (e.g., 0.1 &#176;C/day vs. 3.3 &#176;C/h; Table 1); For NC cases, most relied upon the intrinsic capacity of instruments used, thus leading to various modes. Despite that a type of diurnal temperature change was claimed through a heating cable or incubator16,17; however, the chamber temperatures in these experiments were not validated. Other major review results in Table 1 include the range of incubation temperature of 0-40 &#176;C, with most between 5-25 &#176;C; the duration of experiments ranged from a few hours (&#60;1 day) to nearly 2 years (~725 days). Also, soils subjected to incubations were collected from forest, grassland, and cropland ecosystems, with dominant mineral horizon, organic horizon, and even contaminated soil, located mostly in the US, China, and Europe (Table 1).
Given the three major temperature change modes, several distinct warming scenarios achieved in the past studies were summarized in Table 2. They include stepwise warming (SW), SW with varying magnitude (SWv), gradual warming linearly (GWl), gradual warming nonlinearly (GWn), and gradual warming diurnally (GWd).
In summary, past soil incubations usually captured the average air or soil temperature in a site. In many cases, as shown in Table 1, incubators or chambers were manually programmed at a fixed temperature but incapable of automatically adjusting temperature as desired, lacking the ability to control the mode and rate of temperature change with time (Eq. 1), and thus leading to difficulty to imitate diurnal temperature of the local soil. On the other hand, though attempted in two experiments16,17, we identified no studies that explicitly imitated gradual warming diurnally (GWd) in their incubation experiments (Table 1). Based on the literature review, the major obstacle lies in poor experimental design, particularly lacking a sophisticated instrument that enables implementation and validation of diurnal or other gradual warming scenarios.
<img alt="Equation 1" src="/files/ftp_upload/64081/64081eq01.jpg" style="margin: auto;" />&#160; &#160;(Eq. 1)
Where &#916;T is the quantity of temperature change, m is the mode of temperature change, r is the rate of temperature change, and t is the duration of change.
To improve the experimental rigor in soil incubation, an accurate and sophisticated temperature control method is presented in this study. Adopting a state-of-the-art environmental chamber, increasingly available and economically viable, the new design shall not only enable the accurate simulation of in situ soil temperature (e.g., diurnal pattern) but also, by accounting for possible temperature change extremes, provide a reliable way to minimize the artefacts of instrumental bias. The current soil incubation design should assist researchers to identify optimal strategies that meet their incubation and research needs. The overall goal of this method is to present soil biogeochemists with a highly operational approach to reform soil incubation design.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>10 mL-Syringe</td>
			<td>Fisher Scientific</td>
			<td>14-826-13</td>
			<td>for soil respiration measurement</td>
		</tr>
		<tr>
			<td>Composer Software</td>
			<td>TestEquity</td>
			<td>Model #107</td>
			<td>for incubation temperature setup</td>
		</tr>
		<tr>
			<td>Environmental chamber</td>
			<td>TestEquity</td>
			<td>Model #107</td>
			<td>for soil incubation</td>
		</tr>
		<tr>
			<td>Environmental gas analyzer</td>
			<td>PP Systems</td>
			<td>EGM5</td>
			<td>for soil respiration measurement</td>
		</tr>
		<tr>
			<td>Filter paper</td>
			<td>Fisher Scientific</td>
			<td>1005-125</td>
			<td>for soil incubation</td>
		</tr>
		<tr>
			<td>Mason jar</td>
			<td>Ball</td>
			<td>15381-3</td>
			<td>for soil incubation</td>
		</tr>
		<tr>
			<td>Oven</td>
			<td>Fisher Scientific</td>
			<td>15-103-0520</td>
			<td>for soil moisture measurement</td>
		</tr>
		<tr>
			<td>Plastic Zipper Seal Storage Bag</td>
			<td>Fisher Scientific</td>
			<td>09-800-16</td>
			<td>for soil collection</td>
		</tr>
		<tr>
			<td>Plate reader</td>
			<td>Molecular devices</td>
			<td>FilterMax F5</td>
			<td>for soil extracellular enzyme analysis</td>
		</tr>
		<tr>
			<td>R Software</td>
			<td>The R Foundation</td>
			<td>R version 4.1.3 (2022-03-10)</td>
			<td>For statistical computing</td>
		</tr>
		<tr>
			<td>Refrigerator/Freezer</td>
			<td>Fisher Scientific</td>
			<td>13-991-898</td>
			<td>for soil storation</td>
		</tr>
		<tr>
			<td>Screwdriver</td>
			<td>Fisher Scientific</td>
			<td>19-313-447</td>
			<td>for soil collection</td>
		</tr>
		<tr>
			<td>Sharpie</td>
			<td>Fisher Scientific</td>
			<td>50-111-3135</td>
			<td>for soil collection</td>
		</tr>
		<tr>
			<td>Sieve</td>
			<td>Fisher Scientific</td>
			<td>04-881G&#160;</td>
			<td>for sieving soil sample</td>
		</tr>
		<tr>
			<td>Silicone Septa</td>
			<td>Duran Wheaton kimble</td>
			<td>224100-070</td>
			<td>for mason jars used for soil incubation</td>
		</tr>
		<tr>
			<td>Soil auger</td>
			<td>AMS</td>
			<td>350.05</td>
			<td>for soil collection</td>
		</tr>
		<tr>
			<td>SpecWare Software</td>
			<td>Spectrum Technologies</td>
			<td>WatchDog E2700 (3340WD2)</td>
			<td>for temperature collection interval setup</td>
		</tr>
		<tr>
			<td>Temperature probe</td>
			<td>Spectrum Technologies</td>
			<td>WatchDog E2700 (3340WD2)</td>
			<td>for soil temperature measurements</td>
		</tr>
		<tr>
			<td>TOC/TN analyzer</td>
			<td>Shimadzu</td>
			<td>TOC-L series</td>
			<td>for soil microbial biomass analysis</td>
		</tr>
	</tbody>
</table>

simulating temperature in a soil incubation experiment

The study of warming impact on soils requires a realistic and accurate representation of temperature. In laboratory incubation studies, a widely adopted method has been to render constant temperatures in multiple chambers, and via comparisons of soil responses between low- and high-temperature chambers, to derive the warming impact on soil changes. However, this commonly used method failed to imitate both the magnitude and amplitude of actual temperatures as observed in field conditions, thus potentially undermining the validity of such studies. With sophisticated environmental chambers becoming increasingly available, it is imperative to examine alternative methods of temperature control for soil incubation research. This protocol will introduce a state-of-the-art environmental chamber and demonstrate both conventional and new methods of temperature control to improve the experimental design of soil incubation. The protocol mainly comprises four steps: temperature monitoring and programming, soil collection, laboratory incubation, and warming effect comparison. One example will be presented to demonstrate different methods of temperature control and the resultant contrasting warming scenarios; that is, a constant temperature design referred to as stepwise warming (SW) and simulated in situ temperature design as gradual warming (GW), as well as their effects on soil respiration, microbial biomass, and extracellular enzyme activities. In addition, we present a strategy to diversify temperature change scenarios to meet specific climate change research needs (e.g., extreme heat). The temperature control protocol and the recommended well-tailored and diversified temperature change scenarios will assist researchers in establishing reliable and realistic soil incubation experiments in the laboratory.

The selected state-of-the-art chambers replicated the target temperature with high precision (Figure 2A,B,E,F) and met the technical requirement of the incubation experiment. Given the easy use and operation, this signified the technique to improve the temperature simulation in soil warming studies and in other applications such as plant studies. The procedure has been employed in our recent case study based on a switchgrass cropland in Middle-Tennessee.
Research results showed that relative to control treatment, warming led to significantly greater respiratory losses (Rs and Rc) in both warming scenarios (SW and GW), and GW doubled the warming-induced respiratory loss (Rc) relative to SW, 81% vs. 40% (Figure 3). On day 42, MBC and EEA were also significantly different between SW and GW, such that MBC was higher in SW than in GW (69% vs. 38%; Figure 4) and glycosidases and peroxidase (e.g., AG, BG, BX, CBH, NAG, AP, LAP) were significantly higher in GW than in SW scenarios (Figure 5).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64081/64081fig01.jpg" /> 
Figure 1: The illustration of temperature change mode in a soil warming experiment as conceptualized from Table 1. (A) Constant temperature (CT) adopted by most studies. (B) Constant temperature with varying magnitude (CTv). (C,D) Linear change (LC) with positive and negative rates. (E,F) Nonlinear change (NC) with irregular pattern and diurnal pattern. <a href="https://www.jove.com/files/ftp_upload/64081/64081fig01large.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/64081/64081fig02.jpg" /> 
Figure 2: Temperature targeted via programming and chamber temperature during a 24-h testing period. (A,B) Target temperature (grey line) and chamber temperature records (dashed line) under control and warming treatments of stepwise warming (SW); (C,D) Target temperature (grey line) and chamber temperature records (dashed line) under control and warming treatments of gradual warming (GW); (E, F) The temperature difference derived for records in panels C and D. <a href="https://www.jove.com/files/ftp_upload/64081/64081fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64081/64081fig03.jpg" /> 
Figure 3: Mean (&#177; SE) cumulative soil respiration rate (Rc, &#181;g CO2-C&#183;gsoil-1) under control (hollow) and warming (dark) treatments in SW and GW in a 42-day soil incubation experiment. The insets show soil respiration rates (Rs, &#181;g CO2-C&#183;h-1&#183;gsoil-1) applied to estimate cumulative respiration, assuming Rs was constant until the following measurement. (A) Stepwise warming (SW) and (B) gradual warming (GW). N = 4 in each collection. <a href="https://www.jove.com/files/ftp_upload/64081/64081fig03large.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/64081/64081fig04.jpg" /> 
Figure 4: Mean (&#177; SE) MBC under control and warming treatments in SW and GW in a 42-day soil incubation experiment. MBC = microbial biomass carbon;&#160;N = 4 in each collection. S denotes significant effect of warming scenario (SW vs. GW), at p &#60; 0.05, based on a three-way repeated measures ANOVA. <a href="https://www.jove.com/files/ftp_upload/64081/64081fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/64081/64081fig05.jpg" /> 
Figure 5: Mean (&#177; SE) glycosidases and peroxidase (&#181;mol activity h-1&#183;gsoil-1) under control and warming treatments in SW and GW in a 42-day incubation experiment. BX =&#946;1,4-xylosidase; AP = Acid Phosphatase; LAP = Leucine Aminopeptidase; NAG =&#946;-1,4-N-acetyl-glucosaminidase; OX = Oxidative enzymes; PHO = Phenol oxidase; PER = Peroxidase. N = 4 in each collection. S denotes significant effect of warming scenario (SW vs. GW), at p &#60; 0.05, based on a three-way repeated measures ANOVA. <a href="https://www.jove.com/files/ftp_upload/64081/64081fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Literature review of temperature control methods and temperature change modes in soil incubation studies12,13,16,17,20,21,22,23,24,25,26,27,28,29,30,31,32, 
33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51, 
52,53,54,55,56,57,58,59,60,61,62. 
In total, 46 studies were included in the review. <a href="https://www.jove.com/files/ftp_upload/64081/Table1-64081R2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Major temperature change modes and the corresponding warming scenarios based on a literature review (Table 1). Five modes and scenarios were established to represent a wide range of possible temperature change and warming conditions. <a href="https://www.jove.com/files/ftp_upload/64081/Table 2-64081R2.xlsx" target="_blank">Please click here to download this Table.</a>

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Simulating Temperature in a Soil Incubation Experiment

Laboratory soil warming experiments usually employ two or more constant temperatures in multiple chambers. By presenting a sophisticated environmental chamber, we provide an accurate temperature control method to imitate the magnitude and amplitude of in situ soil temperature and improve the experimental design of soil incubation studies.

The study of warming impact on soils requires a realistic and accurate representation of temperature. In laboratory incubation studies, a widely adopted ...

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2.7K Views.  Tennessee State University. Laboratory soil warming experiments usually employ two or more constant temperatures in multiple chambers. By presenting a sophisticated environmental chamber, we provide an accurate temperature control method to imitate the magnitude and amplitude of in situ soil temperature and improve the experimental design of soil incubation studies.

Video: Simulating Temperature in a Soil Incubation Experiment

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 ...

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Coexistence theory has often treated environmental heterogeneity as being independent of the community composition; however biotic feedbacks such as plant-soil feedbacks (PSF) have large effects on plant performance, and create environmental heterogeneity that depends on the community composition. Understanding the importance of PSF for plant community assembly necessitates understanding of the role of heterogeneity in PSF, in addition to mean PSF effects. Here, we describe a protocol for manipulating plant-induced soil heterogeneity. Two example experiments are presented: (1) a field experiment with a 6-patch grid of soils to measure plant population responses and (2) a greenhouse experiment with 2-patch soils to measure individual plant responses. Soils can be collected from the zone of root influence (soils from the rhizosphere and directly adjacent to the rhizosphere) of plants in the field from conspecific and heterospecific plant species. Replicate collections are used to avoid pseudoreplicating soil samples. These soils are then placed into separate patches for heterogeneous treatments or mixed for a homogenized treatment. Care should be taken to ensure that heterogeneous and homogenized treatments experience the same degree of soil disturbance. Plants can then be placed in these soil treatments to determine the effect of plant-induced soil heterogeneity on plant performance. We demonstrate that plant-induced heterogeneity results in different outcomes than predicted by traditional coexistence models, perhaps because of the dynamic nature of these feedbacks. Theory that incorporates environmental heterogeneity influenced by the assembling community and additional empirical work is needed to determine when heterogeneity intrinsic to the assembling community will result in different assembly outcomes compared with heterogeneity extrinsic to the community composition.

Understanding the role of environmental heterogeneity in species coexistence has typically focused on types of heterogeneity that are extrinsic to the community&#8217;s species composition. We provide novel detailed methods for creating soil heterogeneity treatments using soils subject to plant-soil feedback conditioning, or heterogeneity intrinsic to the community composition.

Coexistence theory has often treated environmental heterogeneity as being independent of the community composition; however biotic feedbacks such as ...

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 ...

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 ...

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Studying co-evolution of hydrological and biogeochemical processes in the subsurface of natural landscapes can enhance the understanding of coupled Earth-system processes. Such knowledge is imperative in improving predictions of hydro-biogeochemical cycles, especially under climate change scenarios. We present an experimental method, designed to capture sub-surface heterogeneity of an initially homogeneous soil system. This method is based on destructive sampling of a soil lysimeter designed to simulate a small-scale hillslope. A weighing lysimeter of one cubic meter capacity was divided into sections (voxels) and was excavated layer-by-layer, with sub samples being collected from each voxel. The excavation procedure was aimed at detecting the incipient heterogeneity of the system by focusing on the spatial assessment of hydrological, geochemical, and microbiological properties of the soil. Representative results of a few physicochemical variables tested show the development of heterogeneity. Additional work to test interactions between hydrological, geochemical, and microbiological signatures is planned to interpret the observed patterns. Our study also demonstrates the possibility of carrying out similar excavations in order to observe and quantify different aspects of soil-development under varying environmental conditions and scale.

This study presents an excavation method for investigating subsurface hydrological, geochemical, and microbiological heterogeneity of a soil lysimeter. The lysimeter simulates an artificial hillslope which was initially under homogeneous condition and had been subjected to approximately 5,000 mm of water over eight cycles of irrigation in an 18-month period.

Studying co-evolution of hydrological and biogeochemical processes in the subsurface of natural landscapes can enhance the understanding of coupled ...

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

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

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

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

A Gusseted Thermogradient Table to Control Soil Temperatures for Evaluating Plant Growth and Monitoring Soil Processes

Thermogradient tables were first developed in the 1950s primarily to test seed germination over a range of temperatures simultaneously without using a series of incubators. A temperature gradient is passively established across the surface of the table between the heated and cooled ends and is lost quickly at distances above the surface. Since temperature is only controlled on the table surface, experiments are restricted to shallow containers, such as Petri dishes, placed on the table. Welding continuous aluminum vertical strips or gussets perpendicular to the surface of a table enables temperature control in depth via convective heat flow. Soil in the channels between gussets was maintained across a gradient of temperatures allowing a greater diversity of experimentation. The gusseted design was evaluated by germinating oat, lettuce, tomato, and melon seeds. Soil temperatures were monitored using individual, battery-powered dataloggers positioned across the table. LED lights installed in the lids or along the sides of the gradient table create a controlled temperature chamber where seedlings can be grown over a range of temperatures. The gusseted design enabled accurate determination of optimum temperatures for fastest germination rate and the highest percentage germination for each species. Germination information from gradient table experiments can help predict seed germination and seedling growth under the adverse soil conditions often encountered during field crop production. Temperature effects on seed germination, seedling growth, and soil ecology can be tested under controlled conditions in a laboratory using a gusseted thermogradient table.

Traditional thermogradient tables create a range of temperatures across the surface. Welding gussets perpendicular to the surface of a thermogradient table will control temperature in depth increasing possible research applications.

Thermogradient tables were first developed in the 1950s primarily to test seed germination over a range of temperatures simultaneously without using a ...

Protocols for Quantifying Transferable Pesticide Residues in Turfgrass Systems

Plant canopies in established turfgrass systems can intercept an appreciable amount of sprayed pesticides, which can be transferred through various routes onto humans. For this reason, transferable pesticide residue experiments are required for registration and re-registration by the United States Environmental Protection Agency (USEPA). Although such experiments are required, limited specificity is required pertaining to experimental approach. Experimental approaches used to assess pesticide transfer to humans including hand wiping with cotton gloves, modified California roller (moving a roller of known mass over cotton cloth) and soccer ball roll (ball wrapped with sorbent strip) over three treated turfgrass species (creeping bentgrass, hybrid bermudagrass and tall fescue maintained at 0.4, 5 and 9 cm, respectively) are presented. The modified California roller is the most extensively utilized approach to date, and is best suited for use at low mowing heights due to its reproducibility and large sampling area. The soccer ball roll is a less aggressive transfer approach; however, it mimics a very common occurrence in the most popular international sport, and has many implications for nondietary pesticide exposure from hand-to-mouth contact. Additionally, this approach may be adjusted for other athletic activities with limited modification. Hand wiping is the best approach to transfer pesticides at higher mowing heights, as roller-based approaches can lay blades over; however, it is more subjective due to more variable sampling pressure. Utility of these methods across turfgrass species is presented, and additional considerations to conduct transferable pesticide residue research in turfgrass systems are discussed.

Transferable pesticide residue experiments in turfgrass systems are integral components of human risk exposure assessments. Experimental approaches to measure transferable residues should be adjusted to the human interaction of interest and turfgrass system dynamics. Three transferable pesticide residue protocols are presented and the suitability across three turfgrass systems is discussed.

Plant canopies in established turfgrass systems can intercept an appreciable amount of sprayed pesticides, which can be transferred through various routes ...

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

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

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

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

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.

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 ...