The authors acknowledge the support of the Minnesota Corn Research &#38; Promotion Council, the Hueg-Harrison Fellowship, and the Minnesota&#39;s Discovery, Research and InnoVation Economy (MnDRIVE) Fellowship.

1. Field site description
NOTE: When performing 15N tracer field trials, selected sites should minimize variation due to soil, topography, and physical features5. Cross-contamination may occur following lateral soil movement due to slope, wind or water translocation, or tillage while the vertical distribution of soil N may be impacted by subsurface water flow and tile-drainage6.
<ol>
	<li>Describe the experimental field site including past management (e.g., previous crops and tillage), latitude and longitude, soil physical and chemical properties (e.g., soil textural analysis, initial fertility conditions, pH, and soil bulk density).</li>
	<li>Record GPS coordinates for the research site and the field corners.</li>
	<li>Describe growing season management including pest and disease management (herbicide, insecticide, or fungicide use), soil fertility management (including rate, source, placement, and application timing), tillage, irrigation events and amounts, and residue management.</li>
	<li>As crop growth and microbe mediated N transformations are affected by soil moisture, soil temperature, and air temperature, record climate information including daily high and low temperatures, daily precipitation, and soil moisture and temperatures at several depths that reflect the soil sampling depths.</li>
</ol>
2. Plot design
<ol>
	<li>Plant six corn rows (~86,000 plants ha-1) on 76 cm spacing with a final plot dimension of 15.2 m by 4.6 m.
	<ol>
		<li>Establish border areas 1.5 m from each end of the lengthwise dimension (0-1.5 m, 13.7-15.2 m) and an additional border area 1.5 m long (9.8-11.3 m) adjoining the sampling and harvest areas (Figure 1).</li>
		<li>Designate rows 2 and 3 as the in-season plant and soil sampling area (1.5-9.8 m) and rows 4 and 5 as the harvest area (1.5-9.8 m) for corn grain yield.</li>
		<li>Establish a microplot area (11.3-13.7 m) with dimensions of 2.4 m by 3.8 m centered on the width dimension. Collect all 15N enriched plant and soil samples from this area, leaving 0.38 m of unsampled border on the length and width dimensions to minimize edge effects (Figure 2).</li>
	</ol>
	</li>
	<li>Delineate the treatment plot and microplot corners with different colored flags.</li>
</ol>
3. Soil and plant sample precautions
<ol>
	<li>Use dedicated equipment and processing areas for unenriched and enriched materials. Contamination of unenriched materials (fertilizer, soil, or plant) by enriched materials and vice versa can drastically affect results.</li>
	<li>Collect and process 15N enriched soil and plant samples in order of lowest to highest 15N expected enrichment to minimize cross-contamination. Ensure that work surfaces, gloves, utensils, and machinery are thoroughly cleaned between each sample to minimize cross-contamination from sample carryover.</li>
	<li>Minimize foot traffic in microplots to prevent contamination of unenriched sampling areas. Wear protective shoe coverings when accessing microplots and remove them when exiting the microplot area.</li>
</ol>
4. 15N enriched fertilizer preparation and application
<ol>
	<li>Following guidelines put forth by Ref. 2 for fertilizer 15N use efficiency (F15NUE) studies, dilute 10 atom % 15N enriched urea to 5 atom % 15N enriched urea and dissolve in 2 L of deionized water to ensure uniform enrichment of urea fertilizer. 
	NOTE: The required concentration of 15N enriched fertilizer is dependent on the goals of the agronomic study. If the concentration of stock 15N enriched fertilizer exceeds the researcher&#39;s requirements, the stock fertilizer concentration may be diluted with similar conventional fertilizer using the following formula3. 
	X2 = [(C1/C2) - 1] &#215; X1 
	X2 is the mass of the conventional unenriched fertilizer, X1 is the mass of the tracer fertilizer, C1 is the isotopic concentration [expressed as atom % excess (measured atom % enrichment minus the natural background concentration assumed to be 0.3663 atom %)] of the original tracer fertilizer, and C2 is the isotopic concentration of the final mixture. As an example, given 100 g of 10 atom % enriched urea, 92.7 g of conventional unenriched fertilizer would be required for a final isotopic concentration of 5 atom %; 
	X2 = {[(10-0.3663)/5] - 1} &#215; 100.</li>
	<li>Analyze the solution for 15N concentration to verify enrichment. The authors utilized the analytical services provided by UC Davis Stable Isotope Facility. 
	NOTE: Reactions of the soil-plant-microbe regime to fertilizer additions may be affected by the physical form of fertilizer. Depending on the goals of the study, the urea solution may be applied as a liquid or dehydrated to reform crystals. The crystals may be compacted into a cake using a Carver press at 10,000 psi, followed by crushing the cake and screening the particles to the desired size3.</li>
	<li>Evenly apply the 15N enriched urea solutions to the microplots using a calibrated backpack CO2 sprayer (Figure 3A). If multiple N rates or enrichment levels are used, consider using designated CO2 sprayers for each enrichment level or use a single sprayer and apply solutions from the lowest to the highest enrichment to minimize treatment cross-contamination.</li>
	<li>Incorporate urea-containing fertilizers with light tillage, hand rakes, or at least 0.6&#160;cm of irrigation within 24 h of application to minimize volatilization loss potential.</li>
	<li>No additional 15N enriched urea fertilizer is applied to the microplot during the second growing season. Apply conventional unenriched urea to the entire treatment to avoid a differential response in corn growth due to nitrogen.</li>
</ol>
5. Field sample processing: aboveground corn biomass
<ol>
	<li>At each sampling stage, collect a six-aboveground corn plant composite sample from within the sampling area (15N unenriched) and a six-aboveground corn plant composite sample from the 15N enriched microplot. At least two plants should separate each sampled plant to avoid significantly altering plant growth dynamics. The authors collected plant samples at the V8 and R1 corn physiological development stages11 and at physiological maturity (Figure 2).</li>
	<li>Following the principles described in steps 3.1 and 3.2, chop V8 and R1 aboveground biomass (&#8804;5 cm by &#8804;5 cm); a yard waste chipper is a satisfactory option. Place chopped biomass in labeled fabric or paper bags and dry in a forced-air oven at 60 &#176;C until constant mass. Record the biomass dry weight (Figure 3B).</li>
	<li>Partition physiologically mature corn plants into stover (all vegetative tissues including leaves, husks, and stalks), grain, and cob fractions. Chop and dry in a forced-air oven at 60 &#176;C until constant mass. Record the biomass dry weight.</li>
	<li>Within the microplot, cut all corn stalks at the soil surface, tie into a bundle, label according to plot, and remove from the field (Figure 3C). Adjust microplot corner flags to be nearly flush with the soil surface to minimize the risk of removal by the combine during harvest or tillage post-harvest.</li>
	<li>Harvest grain from the harvest area and report yield at 15.5% moisture content12. Harvest remaining research areas with a plot combine.</li>
	<li>Rake unenriched biomass from off the microplot area. Chop and reapply microplot aboveground biomass to the correct plot (Figure 3D).</li>
	<li>Incorporate residue into the soil surface with tillage taking care to minimize soil and corn residue transport into or out of the microplot area. Replace any microplot corner flags removed due to tillage.</li>
	<li>Plant second-year corn on the same rows as the first-year corn.</li>
	<li>Collect second-year aboveground corn biomass only at physiological maturity and process like first-year corn samples as described in step 5.3. Collect microplot samples from the center of the microplot area (1.52 m by 0.76 m) to avoid any potential signal dilution following tillage (Figure 2). Harvest grain from the harvest area and report yield at 15.5% moisture content.</li>
	<li>Following the principles of steps 3.1 and 3.2, thoroughly mix and grind 100 to 200 g of dried plant material to pass through a 2 mm sieve. Thoroughly mix the ground material and store a subsample in a labeled coin envelope for further processing. 
	NOTE: A Thomas Wiley mill is a satisfactory option for plant tissue grinding while a Perten Laboratory Mill 3610 is a satisfactory option for grinding grain. 
	CAUTION: People grinding plant samples should wear ear protection and be protected from inhaling dust by wearing a National Institute for Occupational Safety and Health approved N95 Particulate Filtering Facepiece Respirator.</li>
</ol>
6. Field sample processing: soil
<ol>
	<li>Collect first-year soil samples 8 days after 15N enriched fertilizer application, V8, R1, and post-harvest before tillage. Collect second-year soil samples at pre-plant and post-harvest. Due to logistical sampling constraints, the authors collected in-season soil samples at 0- to 15-, 15- to 30-, and 30- to 60-cm depths, post-harvest soil samples at 0- to 15-, 15- to 30-, 30- to 60-, and 60- to 90-cm depths, and second-year pre-plant soil samples at 0- to 30-, 30- to 60-, 60- to 120- cm depths. 
	NOTE: If a soil probe is unable to collect a soil core to the deepest desired depth as a single core, collect deeper depth cores from the same boreholes as the upper depths discarding the top 1-cm of soil to avoid contamination from soil falling from upper depths.
	<ol>
		<li>Collect a four-core (1.8-cm diameter) composite soil sample from the unenriched sampling area at V8 and R1 using a hand probe. Collect one core in the corn row and three cores between the corn rows.</li>
		<li>Collect a two-core (5-cm diameter) composite soil sample from the unenriched sampling area at pre-plant and post-harvest using a hydraulic probe.</li>
		<li>Collect a 15-core (1.8-cm diameter) composite soil sample from the microplot area 8 days after 15N enriched fertilizer application, V8, and R1 using a hand probe. Collect three to four cores in the corn row and 11 to 12 cores between the corn rows. 
		NOTE: Soils are extremely heterogeneous. The greater number of cores collected from within the enriched microplot provides a better estimate of the true 15N enrichment of soil N13.</li>
		<li>Collect a three-core (5-cm diameter) composite soil sample from the microplot area at pre-plant and post-harvest using a hydraulic probe.</li>
		<li>Homogenize each composite soil sample in a bucket and place it in a pre-labeled paper bag.</li>
	</ol>
	</li>
	<li>Dry soil samples at 35 &#176;C in a forced-air oven until constant mass. Grind each sample to pass through a 2 mm sieve. A mechanical soil grinder is satisfactory if it can be thoroughly cleaned between each sample. 
	NOTE: Soil samples may be air-dried by spreading samples on trays in a thin layer. Trays should be in an area free from contamination by outside N sources. Unenriched and enriched samples should be physically separated to prevent cross-contamination. 
	CAUTION: People grinding soil samples should wear ear protection and be protected from inhaling dust by wearing a National Institute for Occupational Safety and Health approved N95 Particulate Filtering Facepiece Respirator.</li>
</ol>
7. Lab sample processing: grind soil and plant samples
<ol>
	<li>Dry ground plant samples (2 mm) overnight in an oven at 60 &#176;C.</li>
	<li>Following the principles described in step 3, grind dried plant samples or soil material to a fine, flour-like consistency. A roller jar mill is a satisfactory option. 
	NOTE: The authors&#39; jar mill is a custom-built conveyor belt system that can process 54 roller jars at a time.
	<ol>
		<li>Fill each roller jar (250 mL borosilicate glass jar with a screw-top lid) with 10 to 20 g of ground plant or soil sample and seven stainless steel rods (8.5 cm long, 0.7 cm diameter).</li>
		<li>Roll roller jars at 0.4 x g for 6-24 h or until samples have a fine, flour-like consistency.</li>
		<li>Transfer the finely ground material into a clean, labeled 20 mL scintillation vial.</li>
		<li>Between each sample, wash roller jars, stainless steel rods, and lids with soap and water to remove any residue.
		<ol>
			<li>Immerse roller jars and lids in a 5% HCl acid bath (prepared from 36-38% concentrated stock) overnight14. 
			CAUTION: Hydrochloric acid is corrosive. It can cause severe skin burns, eye damage, and is harmful if inhaled. Always wear protective clothing, gloves, and eye and face protection. Flush contacted tissue thoroughly with water. Always use a secondary container when transporting acids. Always add acid to water as this reaction is exothermic. Immediately neutralize acid spills with baking soda. 
			NOTE: A large acid bath may be prepared as 100 L of 5% HCl in a 208 L plastic container. Prepare several smaller volumes in a fume hood and then transfer the solutions to the plastic container. Replace the solution every three months.</li>
			<li>Triple rinse roller jars and lids with deionized water and air dry.</li>
			<li>Immerse stainless steel rods in a 0.05 M NaOH bath (prepared by dissolving 2 g of NaOH in 1 L of deionized water) overnight14. Prepare a new 0.05 M NaOH bath each day. 
			CAUTION: Sodium hydroxide can cause severe skin burns and eye damage. Always wear protective clothing and eye protection. Immediately remove contaminated clothing and rinse skin or eyes with water for several minutes.</li>
			<li>Rinse the rods under running hot tap water for 5 minutes. Decant and triple rinse the rods with deionized water. Allow the rods to air dry on a paper towel-lined tray.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
8. Weigh ground plant and soil samples for total N and 15N analysis
<ol>
	<li>Analyze a few representative plant and soil samples for total N content (e.g., combustion analysis15). Calculate the sample mass that provides adequate N content for 15N analysis according to the analyzer specifications. 
	NOTE: The authors utilized the analytical services provided by UC Davis Stable Isotope Facility. Enriched sample weights were optimized for 20 &#181;g of N with a maximum of 100 &#181;g of N.</li>
	<li>Organize like-samples from lowest to highest expected 15N enrichment. Duplicate every eighth to twelfth sample in each run to check sample precision. Include at least one check sample per run16.</li>
	<li>Label a clean 96-well plate and fitted lid with individual well evaporation rings. Cut a clean index card to fit just inside the lid to prevent sample movement between wells during transport.</li>
	<li>Wearing nitrile gloves, clean the microscale, work surfaces, spatula, and forceps with laboratory wipes and ethanol. Place cleaned utensils on a Kimwipe on the lab bench. 
	NOTE: Unenriched and enriched samples should be processed using separate scales and utensils to prevent cross-contamination.</li>
	<li>Use forceps to place a pre-formed 5 mm x 9 mm tin capsule on a clean work surface, such as a stainless steel block with 5 mm x 8 mm well. Gently tap the capsule into the well to reform the cylindrical shape and flatten the bottom of the capsule if needed. 
	NOTE: Because sample masses will be very small, the risk of sample contamination is high. Never touch the capsules with gloves. Discard the capsule if it touches any surface other than the forceps, clean work surface, scale weigh pan, or 96-well plate.</li>
	<li>Use forceps to gently flare out the top 1 mm of the capsule to facilitate manipulation. To avoid scale damage when taring the weight of the capsule, hover and release the capsule 1 to 2 mm above the microscale weigh pan. Tare the capsule. Use forceps to return the capsule to the clean work surface.</li>
	<li>Use a spatula to carefully add the required mass of finely ground sample material to the capsule. Avoid spilling sample material on the outside surface of the capsule or the work surfaces.</li>
	<li>Using forceps, slowly crimp the top third of the capsule and fold over to seal. Using forceps, continue to fold and compress the capsule into a spherical shape taking care not to puncture or tear the tin. 
	NOTE: Samples with low N content may require sample volumes that exceed the capacity of the 5x9 mm capsule. Larger capsules (e.g., 9 mm x 10 mm) may be used in these instances.</li>
	<li>Use forceps to drop the wrapped capsule several times from a height of 1 cm onto a clean, dark surface or mirror to check for leaks. If no dust appears, weigh the sample using the same technique as described in step 8.6. Record the sample weight. Place the capsule in a 96-well plate and record the well placement.
	<ol>
		<li>If dust appears on the dark surface, record the sample weight. Wrap the sample in a second tin capsule, recheck for leaks, and place it in a clean 96-well plate. 
		NOTE: If the wrapped capsule is too large to fit in a 96-well plate, use a 24- or 48-well plate.</li>
	</ol>
	</li>
	<li>Between samples, clean each of the utensils and surfaces with ethanol and laboratory wipes paying especial attention to the spatula and forceps edges.</li>
	<li>Secure the lid to the 96-well plate using tape and store in a desiccator.</li>
</ol>
9. Calculations
<ol>
	<li>Calculate the mass of N (kg&#8729;ha-1) contained in the plant or soil samples using the following equations. 
	<img src="/files/ftp_upload/61191/61191eq1.jpg" /> 
	<img src="/files/ftp_upload/61191/61191eq2.jpg" /></li>
	<li>Calculate the fertilizer N fraction (Nf), fertilizer derived N (FDN), and soil derived N (SDN) for plant and soil samples17. 
	<img src="/files/ftp_upload/61191/61191eq3.jpg" /> 
	where A is the atom % 15N enrichment.</li>
	<li><img src="/files/ftp_upload/61191/61191eq4.jpg" /> 
	<img src="/files/ftp_upload/61191/61191eq5.jpg" /></li>
	<li>Calculate fertilizer 15N use efficiency17. 
	<img src="/files/ftp_upload/61191/61191eq6v2.jpg" /></li>
</ol>

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S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fate+of15N-labelled+fertilizer+applied+to+spring+barley+grown+on+soils+of+contrasting+nutrient+status.">Fate of15N-labelled fertilizer applied to spring barley grown on soils of contrasting nutrient status.</a> Plant and Soil. 195 (1), 83-98 (1997).</li><li>Khanif, Y. M., Cleemput, O., Baert, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Field+study+of+the+fate+of+labelled+fertilizer+nitrate+applied+to+barley+and+maize+in+sandy+soils.">Field study of the fate of labelled fertilizer nitrate applied to barley and maize in sandy soils.</a> Fertilizer Research. 5 (3), 289-294 (1984).</li></ol>

The authors have nothing to disclose.

Stable isotope research is a useful tool for tracking and quantifying FDN through the soil-crop system. However, there are three main assumptions associated with N tracer studies that if violated may invalidate conclusions drawn from using this methodology. They are 1) the tracer is uniformly distributed throughout the system, 2) processes under the study occur at the same rates, and 3) N leaving the 15N enriched pool does not return3. Because this study is interested in the distribution of total FDN throughout the soil-crop system, assumptions 2 and 3 are of minimal concern21.
The high cost of 15N enriched material generally limits the size of 15N tracer studies. Therefore, prior to initiating a N tracer study, the researcher should carefully plan the research project&#39;s objectives considering: the number of sampling events, the length of the study (days to years), the N fertilizer application rate, and the 15N enrichment concentration required to measure differences from natural abundance (0.366 atom %) following 15N enriched fertilizer dilution by bulk soil2. Commonly used 15N enrichment levels and application rates are reported for different types of agronomic research in Ref. 2. After determining the study objectives, the microplot must be sufficiently large to accommodate soil and plant sampling and avoid edge effects. The plot design described in this protocol uses a non-confined plot requiring that non-sampled border areas be employed6. The 15N concentration in border areas is diluted by mass flow across the microplot boundary and N uptake from outside the microplot by lateral corn roots growing in rows 1 and 6. Confined plots, where physical barriers are driven into the soil, do not require border areas but do require additional work during microplot establishment and may limit routine field operations6. References 3, 6, 22-25 provide additional guidance on selecting microplot sizes, border widths, and when confined or non-confined plots may be most appropriate.
The plant and soil sampling scheme of this study is designed to allow for multiple sampling events over two consecutive growing seasons. Early season plant and soil samples are taken near the outside edges of the microplot. Each successive sampling event moves closer to the center of the microplot to avoid sampling previously sampled areas. At least two corn plants separate each sampled plant to minimize changes in corn physiological development. One challenge with this study&#39;s soil sampling technique is that the soil core sampling method may not accurately intercept the heterogeneous distribution of 15N in the soil profile3. Spatial variability of soil total N is high with an estimated coefficient of variation of 15%3. Complete microplot excavation would improve 15N quantification accuracy but requires processing significant volumes of soil and limits sampling to a single event3 that is not in line with the objectives of this study. Subdividing the microplot into smaller sampling units allows for multiple excavation events but may increase the required microplot size to ensure non-sampled units are unaffected by modifications to the crop canopy and soil water dynamics. Despite the potential reduction in accuracy, many studies use the soil core technique for microplots &#8805;1 m2 9,22,26,27,28. Sample precision may be increased by increasing the number of soil cores collected and composited per microplot using the following formula13:
n = (Z2)(CV2)/(d2)
where n is the number of soil cores, Z is the standardized normal variate for the corresponding alpha level (1.96 for 0.05 and 1.65 for 0.10), CV is the coefficient of variation, and d is the margin of error in the plot mean (as a decimal). Based on this formula, the authors expect that 15 cores per microplot would estimate total N to &#177;7.6% on 95% of the plots (n = 15; Z = 1.96; CV = 15%; d = 0.076). Reference 25 used a similar number of cores but subdivided the microplot into 32 sampling units collecting plant and soil samples from four units at each sampling event.
Others have shown that the microplot data can be extrapolated to the entire plot29. However, for this assumption to be valid, the treatment plot and microplot must be similarly managed. If possible, fertilizer N should be applied in the same chemical and physical forms (e.g., urea dissolved in water) as these properties impact fertilizer-soil dynamics including N loss mechanisms, immobilization, and availability to soil microbes and plants3.
The roller jar grinding method described in this protocol is capable of pulverizing large volumes of plant and soil samples, ideal for ensuring a representative, homogenized sample. However, the technique requires significant manual labor and time to load, unload, roll, and clean the roller jars. Sample processing is limited by the available number of roller jars, the capacity of the conveyor belt unit, and the size of the acid bath. Commercial grinding vials may be an alternative to roller jars but may limit the volume of plant and soil samples processed. Lab-made, single-use grinding vials may be constructed that potentially serve as both the grinding and sample storage vessel. The main consideration of any of these grinding methods is to minimize cross-contamination between samples.
Finally, because 15N enriched fertilizer material is expensive, 15N enriched aboveground biomass and soil samples may be retained and homogenized for use in future studies. These products may be especially useful when investigating residue decomposition, mineralization potential, or other nutrient cycling processes21.

Fertilizer nitrogen (N) use is essential in agriculture to meet the food, fiber, feed, and fuel demands of a growing global population, but N losses from agricultural fields can negatively impact environmental quality. Because N undergoes many transformations in the soil-crop system, a better understanding of N cycling, crop utilization, and the overall fate of fertilizer N are necessary to improve management practices that promote N use efficiency and minimize environmental losses. Traditional N fertilizer studies primarily focus on the effect of a treatment on end-of-season measurements such as crop yield, crop N uptake relative to the N rate applied (apparent fertilizer use efficiency), and residual soil N. While these studies quantify the overall system N inputs, outputs, and efficiencies, they cannot identify nor quantify N in the soil-crop system derived from fertilizer sources or the soil. A different approach using stable isotopes must be used to track and quantify the fate of fertilizer derived N (FDN) in the soil-crop system.
Nitrogen has two stable isotopes, 14N and 15N, that occur in nature at a relatively constant ratio of 272:1 for 14N/15N1 (concentration of 0.366 atom % 15N or 3600 ppm 15N2,3). The addition of 15N enriched fertilizer increases the total 15N content of the soil system. As 15N enriched fertilizer mixes with unenriched soil N, the measured change of 14N/15N ratio allows researchers to trace FDN in the soil profile and into the crop3,4. A mass balance can be calculated by measuring the total amount of 15N tracer in the system and each of its parts2. Because 15N enriched fertilizers are significantly more expensive than conventional fertilizers, 15N enriched microplots are often embedded within the treatment plots. The purpose of this methods paper is to describe a small-plot research&#160;design utilizing microplots for multiple in-season soil and plant sampling events for corn (Zea mays L.) and to present protocols for preparing plant and soil samples for total 15N analysis. These results can then be used to estimate N fertilizer use efficiency and create a partial N budget accounting for FDN in the bulk soil and the crop.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>20 mL scintillation vial</td>
			<td>ANY; Fisher Scientific is one example</td>
			<td>0334172C</td>
			<td></td>
		</tr>
		<tr>
			<td>250 mL borosilicate glass bottle</td>
			<td>QORPAK</td>
			<td>264047</td>
			<td></td>
		</tr>
		<tr>
			<td>48-well plate</td>
			<td>EA Consumables</td>
			<td>E2063</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well plate</td>
			<td>EA Consumables</td>
			<td>E2079</td>
			<td></td>
		</tr>
		<tr>
			<td>Cloth parts bag (30x50 cm)</td>
			<td>ANY</td>
			<td>NA</td>
			<td>For corn ears</td>
		</tr>
		<tr>
			<td>CO2 Backpack Sprayer</td>
			<td>ANY; Bellspray Inc is one example</td>
			<td>Model T</td>
			<td></td>
		</tr>
		<tr>
			<td>Coin envelop (6.4x10.8 cm)</td>
			<td>ANY; ULINE is one example</td>
			<td>S-6285</td>
			<td>For 2-mm ground plant samples</td>
		</tr>
		<tr>
			<td>Corn chipper</td>
			<td>ANY; DR Chipper Shredder is one example</td>
			<td>SKU:CS23030BMN0</td>
			<td>For chipping corn biomass</td>
		</tr>
		<tr>
			<td>Corn seed</td>
			<td>ANY</td>
			<td>NA</td>
			<td>Hybrid appropriate to the region</td>
		</tr>
		<tr>
			<td>Disposable shoe cover</td>
			<td>ANY; Boardwalk is one example</td>
			<td>BWK00031L</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethanol 200 Proof</td>
			<td>ANY; Decon Laboratories Inc. is one example</td>
			<td>2701TP</td>
			<td></td>
		</tr>
		<tr>
			<td>Fabric bags with drawstring (90x60 cm)</td>
			<td>ANY</td>
			<td>NA</td>
			<td>For plant sample collection</td>
		</tr>
		<tr>
			<td>Fertilizer Urea (46-0-0)</td>
			<td>ANY</td>
			<td>NA</td>
			<td>~0.366 atom % 15N</td>
		</tr>
		<tr>
			<td>Hand rake</td>
			<td>ANY; Fastenal Company is one example</td>
			<td>5098-63-107</td>
			<td></td>
		</tr>
		<tr>
			<td>Hand sickle</td>
			<td>ANY; Home Depot is one example</td>
			<td>NJP150</td>
			<td>For plant sample collection</td>
		</tr>
		<tr>
			<td>Hand-held soil probe</td>
			<td>ANY; AMS is one example</td>
			<td>401.01</td>
			<td></td>
		</tr>
		<tr>
			<td>Hydraulic soil probe</td>
			<td>ANY; Giddings is one example</td>
			<td>GSPS</td>
			<td></td>
		</tr>
		<tr>
			<td>Hydrochloric acid, 12N</td>
			<td>Ricca Chemical</td>
			<td>R37800001A</td>
			<td></td>
		</tr>
		<tr>
			<td>Jar mill</td>
			<td>ANY; Cole-Parmer is one example</td>
			<td>SI-04172-50</td>
			<td></td>
		</tr>
		<tr>
			<td>Laboratory Mill</td>
			<td>Perten</td>
			<td>3610</td>
			<td>For grinding grain</td>
		</tr>
		<tr>
			<td>Microbalance accurate to four decimal places</td>
			<td>ANY; Mettler Toledo is one example</td>
			<td>XPR2</td>
			<td></td>
		</tr>
		<tr>
			<td>N95 Particulate Filtering Facepiece Respirator</td>
			<td>ANY, ULINE is one example</td>
			<td>S-9632</td>
			<td></td>
		</tr>
		<tr>
			<td>Neoprene or butyl rubber gloves</td>
			<td>ANY</td>
			<td>NA</td>
			<td>For working in HCl acid bath</td>
		</tr>
		<tr>
			<td>Paper hardware bags (13.3x8.7x27.8 cm)</td>
			<td>ANY; ULINE is one example</td>
			<td>S-8530</td>
			<td>For soil samples and corn grain</td>
		</tr>
		<tr>
			<td>Plant grinder</td>
			<td>ANY; Thomas Wiley Model 4 Mill is one example</td>
			<td>1188Y47-TS</td>
			<td>For grinding chipped corn biomass to 2-mm particles</td>
		</tr>
		<tr>
			<td>Plastic tags</td>
			<td>ULINE</td>
			<td>S-5544Y-PW</td>
			<td>For labeling fabric bags and microplot stalk bundles</td>
		</tr>
		<tr>
			<td>Sodium hydroxide pellets, ACS</td>
			<td>Spectrum Chemical</td>
			<td>SPCM-S1295-07</td>
			<td></td>
		</tr>
		<tr>
			<td>Soil grinder</td>
			<td>ANY; AGVISE stainless steel grinder with motor is one example</td>
			<td>NA</td>
			<td>For grinding soil to pass through a 2-mm sieve</td>
		</tr>
		<tr>
			<td>Tin capsule 5x9 mm</td>
			<td>Costech Analytical Technologies Inc.</td>
			<td>041061</td>
			<td></td>
		</tr>
		<tr>
			<td>Tin capsule 9x10 mm</td>
			<td>Costech Analytical Technologies Inc.</td>
			<td>041073</td>
			<td></td>
		</tr>
		<tr>
			<td>Urea (46-0-0)</td>
			<td>MilliporeSigma</td>
			<td>490970</td>
			<td>10 atom % 15N</td>
		</tr>
	</tbody>
</table>

microplot design and plant and soil sample preparation for 15nitrogen analysis

Many&#160;nitrogen fertilizer studies evaluate the overall effect of a treatment on end-of-season measurements such as grain yield or cumulative N losses. A stable isotope approach is necessary to follow and quantify the fate of fertilizer derived N (FDN) through the soil-crop system. The purpose of this paper is to describe a small-plot research&#160;design utilizing non-confined 15N enriched microplots for multiple soil and plant sampling events over two growing seasons and provide sample collection, handling, and processing protocols for total 15N analysis. The methods were demonstrated using a replicated study from south-central Minnesota planted to corn (Zea mays L.). Each treatment consisted of six corn rows (76 cm row-spacing) 15.2 m long with a microplot (2.4 m by 3.8 m) embedded at one end. Fertilizer-grade urea was applied at 135 kg N&#8729;ha-1 at planting, while the microplot received urea enriched to 5 atom % 15N. Soil and plant samples were taken several times throughout the growing season, taking care to minimize cross-contamination by using separate tools and physically separating unenriched and enriched samples during all procedures. Soil and plant samples were dried, ground to pass through a 2 mm screen, and then ground to a flour-like consistency using a roller jar mill. Tracer studies require additional planning, sample processing time and manual labor, and incur higher costs for 15N enriched materials and sample analysis than traditional N studies. However, using the mass balance approach, tracer studies with multiple in-season sampling events allow the researcher to estimate FDN distribution through the soil-crop system and estimate unaccounted-for FDN from the system.

The results presented in this paper come from a field site established in 2015 at the University of Minnesota Southern Outreach and Research Center located near Waseca, MN. The site was managed as a corn-soybean [Glycine max (L.) Merr] rotation prior to 2015 but was managed as a corn-corn rotation during the 2015 and 2016 growing seasons. The soil was a Nicollet clay loam (fine-loamy, mixed, superactive, mesic Aquic Hapludolls)-Webster clay loam (fine-loamy, mixed, superactive, mesic Typic Endoaquolls) complex. Soil fertility was managed according to university guidelines except for N18. Several N fertilizer treatments were arranged in a randomized complete block design with four replications but only the 135 kg N&#8729;ha-1 rate applied as urea at planting is presented in this paper. Soil bulk density was measured at the center of 0- to 15-, 15- to 30-, 30- to 60-, 60- to 90-, and 60- to 120-cm depth layers from two 5-cm deep samples per replication using the intact core method19. Bulk density was averaged within depth across replications and assumed to be constant across the field. Plot setup and plant and soil samples were collected and processed as described in the protocol section.
Total (FDN + SDN) aboveground biomass N increased with each successive sampling event over the first growing season (Figure 4). Fertilizer derived N concentration was greatest earlier in the growing season accounting for 44 &#177; 4% (mean &#177; standard error) of the total aboveground biomass N at V8 and decreased with each successive sampling period (Figure 4A). However, SDN consistently was the greatest fraction of aboveground biomass N illustrating the importance of soil N supply for optimal corn growth. At physiological maturity in the first year, 27 &#177; 1% of aboveground biomass N was from FDN with similar proportions in grain, stover, and cob fractions (Figure 4B). At physiological maturity in the second year, only 2 &#177; 0.1% of first-year FDN was recovered in the aboveground biomass with 1.6 &#177; 0.2 kg of first-year FDN ha-1 exported in the grain (Figure 4A).
The soil-crop FDN budget is useful for quantifying FDN cycling within the system over time. Within 8 d of fertilizer application, the majority of FDN was in the top 15 cm of the soil profile, as expected (Figure 5). However, 22.2 &#177; 4.4 kg N ha-1 had already moved into the deeper depths while 4 &#177; 10% of the FDN was unaccounted for. Unaccounted-for FDN is likely primarily driven by N loss mechanisms including leaching, denitrification, and volatilization that either move FDN below the soil sampling depths or remove the FDN from the system entirely. At V8 and R1, unaccounted-for FDN increased to 60.4 &#177; 4.7 kg N ha-1 on average while soil N (0-15 cm) was 31.6 &#177; 6.8 kg N ha-1 on average. Corn&#39;s rapid growth and high N demand from V8 to R1 resulted in an increase of 19.0 &#177; 4.4 kg FDN ha-1 in aboveground plant biomass mirroring the 17.7 &#177; 5.2 kg FDN ha-1 reduction from the 15- to 60-cm soil depths. Soil temperature and moisture conditions between these corn development stages tend to favor microbial growth resulting in rapid turnover of organic residues and re-utilization of mineralized N. These results suggest that corn roots mined inorganic FDN from the 15- to 60-cm depths while FDN in the 0- to 15-cm depth was primarily cycled between soil organic matter and microbial fractions. Additional isotopic analysis of soil inorganic and organic N pools is necessary to validate this hypothesis and provide greater detail and insight into FDN cycling dynamics10. By post-harvest year 1, 59 &#177; 2% of the original FDN was unaccounted for while 18.1 &#177; 3.9 kg FDN ha-1 was in the top 30 cm of the soil (Figure 5) and 22.1 &#177; 2.3 kg FDN ha-1 was exported in the grain (Figure 4B). Fertilizer 15N use efficiency was 24% (Equation 7) and is at the low end of commonly reported F15NUE measures (25-45%) reported by other studies20. Although equipment was thoroughly cleaned between each sample, the lower F15NUE measures of the study could be an artifact of enriched sample dilution by processing enriched samples in order of lowest to highest expected enrichment. The amount of FDN in the top 30 cm doubled (36.0 &#177; 5.2 kg FDN ha-1) from post-harvest year 1 to pre-plant year 2 due to partial residue breakdown since the previous fall but by post-harvest year 2 only 17.3 &#177; 3.3 kg FDN ha-1 was still found within the soil-corn system (Figure 5). This study indicates that by the end of the first and second years, only 41 and 29%, respectively of first-year FDN was accounted for within the soil-corn system (including FDN exported in the grain) while the remainder was either lost to the environment or leached below the 90 cm soil sampling depth.
Spurious results may be obtained when samples are cross-contaminated affecting calculations of Nf, FDN, and SDN. For example, suppose a 15N enriched plant sample with an actual enrichment of 3.000 atom % 15N is contaminated with unenriched material diluting the 15N concentration to 2.500 atom % 15N. Further, assume Total NPlant is 100 kg N ha-1, the atom % 15N enrichment of the fertilizer was 5.000, and the atom % 15N enrichment of the unenriched plant sample was 0.366. The 15N enriched plant sample Nf would be reduced from 0.568 (actual) to 0.461 (contaminated sample) underestimating the true FDN by 10.7 kg N ha-1. Overestimations of FDN may occur when samples with low 15N enrichment are contaminated with additional 15N. Thus, extreme care should be taken in all steps of sample collection and processing to minimize sample contamination, but most especially when sample masses are reduced (e.g., grinding and weighing procedures).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61191/61191fig1.jpg" /> 
Figure 1: Plot design for the treatment plot and microplot. The figure illustrates the dimensions and relative placements of the border areas, unenriched sampling area, harvest area, and microplot area within the treatment plot. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61191/61191fig1large.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/61191/61191fig2v2.jpg" /> 
Figure 2: Microplot plant and soil sampling diagram. The figure illustrates the relative plant and soil sampling positions at each sampling stage that avoids altering corn N uptake patterns of later sampled corn plants. Sampling events occurred 8 days following the 15N enriched fertilizer application, at the V8 and R1 corn physiological development stages, at physiological maturity in the year of 15N enriched fertilizer application (PMY1) and the following year (PMY2), and prior to planting the second year (PPY2). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61191/61191fig2v2large.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/61191/61191fig3.jpg" /> 
Figure 3: Chronological depiction of microplot management. (A) Dissolve 15N enriched urea into 2 L of deionized water and spray onto the microplot at planting. (B) Collect and chop a six-aboveground corn plant composite sample from within the sampling area (15N unenriched) and a six-aboveground corn plant composite sample from the 15N enriched microplot at the pre-determined sampling times. (C) Following sample collection at physiological maturity, remove all remaining aboveground biomass from within the microplot. (D) Post-harvest, rake unenriched aboveground corn biomass from the microplot area. Chip and reapply the microplot corn aboveground biomass to the microplot area. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61191/61191fig3large.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/61191/61191fig4.jpg" /> 
Figure 4: Example of aboveground biomass N partitioned into fertilizer derived N (FDN) and soil derived N (SDN) fractions. Total aboveground biomass N was separated into its individual sources of FDN (solid color) and SDN (hashed color) in (A) and (B). Error bars represent the standard error of the mean. (A) Aboveground biomass N was measured at the V8 and R1 corn physiological development stages and at physiological maturity in the year of 15N fertilizer application (PMY1) and the year following 15N fertilizer application (PMY2). The value above each column represents the percentage of the total N that was FDN. (B) Aboveground biomass N measured at PMY1 and PMY2 is shown in its individual parts of cob (only Year 1), stover (stalk and leaves; includes cob for PMY2), and grain for FDN and SDN. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61191/61191fig4large.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/61191/61191fig5.jpg" /> 
Figure 5: Example of the soil-corn fertilizer derived N (FDN) budget. The mass of FDN recovered in aboveground (Abvgd) corn biomass and at various soil sampling depths is reported for six sampling events over two growing seasons. Sampling events occurred 8 days following the 15N enriched fertilizer application (PA), at the V8 and R1 corn physiological development stages, at physiological maturity in the year of 15N enriched fertilizer application (PMY1) and the following year (PMY2), and prior to planting the second year (PPY2). The difference between the applied fertilizer rate (135 kg N ha-1) and the mass of FDN recovered in the soil-corn portions is the unaccounted for FDN fraction. The total mass of FDN for PPY2 and PMY2 was 113 kg FDN ha-1 because 22 kg FDN ha-1 was exported out of the soil-corn system as first-year grain. Error bars represent the standard error of the mean. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61191/61191fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis at JoVE.com

Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis

A microplot design for 15N tracer research is described to accommodate multiple in-season plant and soil sampling events. Soil and plant sample collection and processing procedures, including grinding and weighing protocols, for 15N analysis are put forth.

Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis

Many&#160;nitrogen fertilizer studies evaluate the overall effect of a treatment on end-of-season measurements such as grain yield or cumulative N losses. ...

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6.7K Views.  University of Minnesota. A microplot design for 15N tracer research is described to accommodate multiple in-season plant and soil sampling events. Soil and plant sample collection and processing procedures, including grinding and weighing protocols, for 15N analysis are put forth.

Video: Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis

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 ...

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 ...

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 ...

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 ...

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Microplastic pollution in the marine environment is a scientific topic that has received increasing attention over the last decade. The majority of scientific publications address microplastic pollution of the sea surface. The protocol below describes the methodology for sampling, sample preparation, separation and chemical identification of microplastic particles. A manta net fixed on an &#187;A frame&#171; attached to the side of the vessel was used for sampling. Microplastic particles caught in the cod end of the net were separated from samples by visual identification and use of stereomicroscopes. Particles were analyzed for their size using an image analysis program and for their chemical structure using ATR-FTIR and micro FTIR spectroscopy. The described protocol is in line with recommendations for microplastics monitoring published by the Marine Strategy Framework Directive (MSFD) Technical Subgroup on Marine Litter. This written protocol with video guide will support the work of researchers that deal with microplastics monitoring all over the world.

The protocol below describes the methodology for: microplastics sampling on the sea surface, separation of microplastic and chemical identification of particles. This protocol is in line with the recommendations for microplastics monitoring published by the MSFD Technical Subgroup on Marine Litter.

Microplastic pollution in the marine environment is a scientific topic that has received increasing attention over the last decade. The majority of ...

Nanopore DNA Sequencing for Metagenomic Soil Analysis

This article describes the steps for construction of a DNA library from soil, preparation and use of the nanopore flow cell, and analysis of the DNA sequences identified using computer software. Nanopore DNA sequencing is a flexible technique that allows for rapid microbial genome sequencing to identify bacterial and viral species, to characterize bacterial strains, and to detect genetic mutations that confer resistance to antibiotics. The advantages of nanopore sequencing (NS) for life sciences include its low complexity, reduced cost, and rapid real-time sequencing of purified genomic DNA, PCR amplicons, cDNA samples, or RNA. NS is an example of "strand sequencing" which involves sequencing DNA by guiding a single stranded DNA molecule through a nanopore that is inserted into a synthetic polymer membrane. The membrane has an electrical current applied across it, so as the individual bases pass through the nanopore the electrical current is disrupted to varying degrees by the four nucleotide bases. The identification of each nucleotide occurs by detecting the characteristic modulation of the electrical current by the different bases as they pass through the nanopore. The NS system consists of a handheld, USB powered portable device and a disposable flow cell that contains a nanopore array. The portable device plugs into a standard laptop computer that reads and records the DNA sequence using computer software.

Nanopore technology for sequencing biomolecules has wide applications in the life sciences, including identification of pathogens, food safety monitoring, genomic analysis, metagenomic environmental monitoring, and characterization of bacterial antibiotic resistance. In this article, the procedure for metagenomic soil DNA sequencing for species identification using the nanopore sequencing technology is demonstrated.

This article describes the steps for construction of a DNA library from soil, preparation and use of the nanopore flow cell, and analysis of the DNA ...

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses (Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture (A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula). Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

Excavation of plant roots from the field as well as processing of samples into endosphere, rhizosphere, and soil are described in detail, including DNA extraction and data analysis methods. This paper is designed to enable other laboratories to use these techniques for the study of soil, endosphere, and rhizosphere microbiomes.

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The ...

Sample Preparation Method of Scanning and Transmission Electron Microscope for the Appendages of Woodboring Beetle

This report described sample preparation methods that scanning and transmission electron microscope observations, demonstrated by preparing appendages of the woodboring beetle, Chlorophorus caragana Xie &#38; Wang (2012), for both types of electron microscopy. The scanning electron microscopy (SEM) sample preparation protocol was based on sample chemical fixation, dehydration in a series of ethanol baths, drying, and sputter-coating. By adding Tween 20 (Polyoxyethylene sorbitan laurate) to the fixative and the wash solution, the insect body surface of woodboring beetle was washed more cleanly in SEM. This study&#39;s transmission electron microscopy (TEM) sample preparation involved a series of steps including fixation, ethanol dehydration, embedding in resin, positioning using fluorescence microscopy, sectioning, and staining. Fixative with Tween 20 enabled penetrate the insect body wall of woodboring beetle more easily than it would had been without Tween 20, and subsequently better fixed tissues and organs in the body, thus yielded clear transmission electron microscope observations of insect sensilla ultrastructures. The next step of this preparation was determining the positions of insect sensilla in the sample embedded in the resin block by using fluorescence microscopy to increase the precision of target sensilla positioning. This improved slicing accuracy.

In order to observe ultrastructure of insect sensilla, scanning and transmission electron microscopy (SEM and TEM, respectively) sample preparation protocol were presented in the study. Tween 20 was added into the fixative to avoid sample deformation in SEM. Fluorescence microscopy was helpful for improving slicing accuracy in TEM.

This report described sample preparation methods that scanning and transmission electron microscope observations, demonstrated by preparing appendages of ...

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment

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

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

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

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. However, at the soil surface these variables are highly dynamic and standard sensors do not explicitly measure temperature or moisture in the upper few millimeters of the soil profile. This paper describes methods for manufacturing simple, inexpensive sensors that simultaneously measure the temperature and moisture of the upper 5 mm of the soil surface. In addition to sensor construction, steps for quality control, as well as for calibration for various substrates, are explained. The sensors incorporate a Type E thermocouple to measure temperature and assess soil moisture by measuring the resistance between two gold-plated metal probes at the end of the sensor at a depth of 5 mm. The methods presented here can be altered to customize probes for different depths or substrates. These sensors have been effective in a variety of environments and have endured months of heavy rains in tropical forests as well as intense solar radiation in deserts of the southwestern U.S. Results demonstrate the effectiveness of these sensors for evaluating warming, drying, and freezing of the soil surface in a global change experiment.

Accurately measuring temperature and water content of the upper 5 mm of the soil surface can improve our understanding of environmental controls on biological, chemical, and physical processes. Here we describe a protocol for manufacturing, calibrating, and conducting measurements with soil surface temperature and moisture sensors.

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. ...