This research was supported by the USDA Natural Resources Conservation Service in association with the Wetland Component of the National Conservation Effects Assessment Project (NRCS 67-3A75-13-177).

1. Topographic Analyses
<ol>
	<li>Digital data preprocess
	<ol>
		<li>Collect LiDAR data from the GeoTREE LiDAR mapping project website. Select &#34;boundary type&#34; and &#34;region&#34; to zoom into a specific area. Draw a polygon to download LiDAR tiles for the selected study area.</li>
		<li>Convert the raw LiDAR data to a LAS file using the geographic information system (GIS) mapping tool.</li>
		<li>Generate DEMs with a 3-m spatial resolution using inverse distance weighted interpolation.</li>
		<li>Filter the 3-m DEMs twice with a 3-kernel low pass filter to reduce noises associate with local variation.</li>
	</ol>
	</li>
	<li>Topographic metric generation
	<ol>
		<li>To generate topographic metrics, first download the latest version of the System for Automated Geoscientific Analysis (SAGA)23. Click &#34;Import Raster&#34; in the Import/Export section to import the filtered 3-m DEMs into SAGA.</li>
		<li>Click the &#34;Slope, Aspect, Curvature&#34; module of SAGA with the default settings to generate the slope and curvature-related [profile curvature (P_Cur), plan curvature (Pl_Cur), and general curvature (G_Cur)] metrics using the filtered DEMs (Figure 1).</li>
		<li>Click the &#34;Flow Accumulation (Top-Down)&#34; module of SAGA and select &#34;Deterministic infinity&#34; as the method to generate flow accumulation (FA) metric using the filtered DEMs.</li>
		<li>Click the &#34;SAGA Topographic Openness&#34; module with the default settings to generate the positive openness (POP) metric using a filtered z-axis amplified image.</li>
		<li>Click the &#34;LS- factor (Field Based)&#34; module of SAGA with the default settings to generate the upslope slope (Upsl) and slope length factor (LS_FB) metrics using the filtered DEMs.</li>
		<li>Click the &#34;Flow Path Length&#34; module of SAGA with the default settings to generate the flow path length (FPL) metric using the filtered DEMs.</li>
		<li>Click the &#34;Downslope Distance Gradient&#34; module of SAGA with the default settings to generate the downslope index (DI) metric using the filtered DEMs.</li>
		<li>Click the &#34;SAGA Wetness Index&#34; module and select &#34;absolute catchment area&#34; as the Type of Area to generate the catchment area (CA) and topographic wetness index (TWI) metrics using the filtered DEMs.</li>
		<li>Click the &#34;Stream Power Index&#34; module of SAGA and select &#34;pseudo specific catchment area&#34; as the Area Conversion to generate the stream power index (SPI) metric using the filtered DEMs.</li>
		<li>Generate maximum elevation maps with multiple radiuses. Filter the maximum elevation maps twice through a 3-kernel low pass filter. Subtract the filtered 3-m DEM from the filtered maximum elevation maps to obtain a series of relief maps. Extract a series of relief variables to a number of locations.</li>
		<li>Perform principal component analysis (PCA) on the relief variables to convert the reliefs into topographic relief components. Select principal components that explain more than 90% variance of the relief dataset as the topographic relief metrics.</li>
	</ol>
	</li>
</ol>
2. Field Data Collection
<ol>
	<li>Field sampling
	<ol>
		<li>Select a number of cropland field locations that can adequately represent the landscape characteristics of the study area and several representative small-scale cropland fields that can be intensively sampled. 
		NOTE: The soil samples collected from the two cropland fields were used for model calibration. Soil samples collected from the entire study area were used for model validation.</li>
		<li>Upload all the sample location coordinates to a code-based geographic positioning system (GPS) and physically locate them in the fields.</li>
		<li>Collect 3 samples for each sampling location from the top 30 cm soil layer using a push probe (3.2 cm in diameter). 
		&#8203;NOTE: Soil samples from 30-50 cm layers were collected at sites where sediment deposition was expected. The volume of each sample was 241 cm3.</li>
		<li>Record geographic coordinate information of sampling locations using GPS.</li>
		<li>Weigh the soil samples after drying them at 90 &#176;C for 48 h. Calculate soil density using information of total sample volumes at sampling locations and weights. Mix the three samples from the same location to get a composite soil sample.</li>
	</ol>
	</li>
	<li>Soil sample preparation
	<ol>
		<li>Sieve the composite soil samples with a 2-mm screen.</li>
		<li>Grind a 10 g subsample of the sieved soil to a very fine powder with a roller mill.</li>
	</ol>
	</li>
	<li>Soil sample analyses
	<ol>
		<li>Measure soil total carbon (C) content in roller milled samples through combustion on a CN elemental analyzer at a temperature of 1350 &#176;C. Estimate calcium carbonate C content by analyzing the remaining C after baking soil organic matter at a temperature of 420 &#176;C for 16 h in a furnace.</li>
		<li>Calculate SOC content (%) by subtracting calcium carbonate C content from total soil C content. Convert SOC content (%) to SOC density (kg m-2) using soil density.</li>
		<li>Put the bulk 2-mm sieved soil samples in Marinelli beakers and seal them. Measure 137Cs concentration of each sample through gamma-ray analysis using a spectroscopy system that receives inputs from three high purity coaxial germanium crystals (HpCN30% efficiency) into 8192-channel analyzers (see Table of Materials).</li>
		<li>Calibrate the system using an analytic mixed radionuclide standard11. Convert 137Cs concentration to 137Cs inventory using soil density.</li>
		<li>Calculate soil redistribution rate using 137Cs inventory by applying the Mass Balance Model II (MBMII) in a spreadsheet add-in program developed by Walling et al.24.</li>
	</ol>
	</li>
</ol>
3. Topography-Based Model Development
<ol>
	<li>Topographic principal component estimation
	<ol>
		<li>Extract the topographic metrics for sampling locations in the entire study area and the small-scale cropland fields.</li>
		<li>Standardize the topographic metrics of the sampling locations in the entire study area by using mean and standard deviation. Estimate the topographic metric loadings in each component based on the standardized topographic metrics using PCA with statistical software package. Collect the topographic metric loadings in each topographic principal component (TPC) and select the top TPCs that explain 90% variance of all metrics.</li>
		<li>Standardize the topographic metrics of the sampling locations in the small-scale cropland fields. Calculate the top TPCs for each location by sum of the standardized topographic metrics weighted by the corresponding loadings from the sampling locations in WCW.</li>
	</ol>
	</li>
	<li>Model calibration
	<ol>
		<li>Perform stepwise ordinary least square regression (SOLSR) to develop topography-based SOLSRf models for SOC density and soil redistribution rates based on all topographic metrics at the small-scale cropland fields. Use Akaike information criterion (AIC) and leave-one-out cross-validation to select the optimal combination of topographic metrics for the best-fitted SOLSRf models.</li>
		<li>Check the collinearity among the topographic variables using the variance inflation factor (VIF). Remove the variables with the largest VIF (VIF &#8805; 7.525), and check VIF again. Remove the variables until the VIFs of all variables are &#60; 7.5. Perform SOLSR to develop topography-based SOLSRr models for SOC density and soil redistribution rates based on topographic metrics that were removed high collinearity variables. Use the AIC and leave-one-out cross-validation to select the optimal combination for the best-fitted SOLSRr models.</li>
		<li>Perform stepwise principal component regression (SPCR) to develop topography-based SPCR models for SOC density and soil redistribution rates based on the TPCs at the small-scale cropland fields. Use the AIC and leave-one-out cross-validation to select the optimal combination of TPCs for the best-fitted SPCR models.</li>
		<li>Calculate the adjusted coefficient of determination (Radj2), Nash-Sutcliffe efficiency (NSE), and ratio of the root mean square error to the standard deviation of measured data (RSR) to assess model efficiencies.</li>
	</ol>
	</li>
	<li>Model evaluation
	<ol>
		<li>Estimate SOC density and soil redistribution rates in the entire study area by applying the estimated models.</li>
		<li>Validate the developed model by comparing prediction with measured dataset of SOC density and soil redistribution rates in the entire study area. Evaluate the model performances using Radj2, NSE, and RSR values.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

The SOLSRf models had slightly better performances than the SPCR models in calibration at the field scale. However, some of the topographic metrics, such as SPI and CA (r &#62; 0.80), are closely correlated with each other. The collinearity may add uncertainties to model predictions. Because of the multicollinearity among predictors, small changes in the input variables can significantly affect the model predictions41. Therefore, the SOLSRf models tended to be unstable and showed low efficiencies in simulations of SOC density and soil redistribution rate at the watershed scale. The SPCR models substantially outperformed the SOLSRf models in prediction of SOC distribution at the watershed scale. TPCs eliminate the multicollinearity by converting the fifteen topographic metrics into mutually independent (orthogonal) components. The conversion also uncovered underlying relationships among topographic metrics. As indicated by the high loadings (&#62; 0.35) of topographic metrics to the components, the TPC1, TPC2, TPC3, TPC6, and TPC7 were associated with runoff velocity, soil water content, runoff volume, flow divergence, and flow acceleration, respectively. Spatial patterns of soil redistribution rates and SOC distribution were highly correlated with soil water content and runoff divergence in the WCW, which is consistent with the study of Fox and Papanicolaou2, which demonstrated that eroded soil from upland could be impacted by flow divergence in a low-relief agricultural watershed.
Moreover, fewer predictor variables in the SPCR models than the SOLSRf and SOLSRr models reduced the risk of over-fitting the prediction models42,43. There were more than six variables in all the SOLSR models, which may increase the difficulty of data interpretation and induce high variance in model simulations41,44,45. This may account for the lower prediction efficiencies in WCW by the SOLSR models than by the SPCR models.
Topography- based SPCR models have advantages in simulating soil redistribution and associated SOC dynamics. First, topographic information can be easily derived from DEMs. Recent increased accessibility of the high spatial resolution LiDAR data can help improve the accuracy of DEM-derived landscape topography and benefit investigations in regions with limited field observations. Second, using a set of topographic metrics and statistical analyses, the topography-based models can efficiently quantify soil redistribution and SOC distribution patterns. Third, the application of principal component can effectively reduce biases associated with multicollinearity of topographic metrics and increase the stability of the stepwise regression models when applied to multiple spatial scales.
However, the SPCA models may be limited by variables during model development. Although application of the LiDAR data increased in ecological studies, the methods to derive useful topographic information have not yet been fully explored. In this study, the TWI and LsRe showed the highest correlations with SOC density and soil redistribution rates, respectively. However, additional topographic variables that are not considered may be equally or more important in explaining soil erosion and C dynamics. Additionally, other factors such as management practices, which may cause soil erosion variability, were not included in this study. For example, when tillage was parallel to the direction of maximum slope, soil erosion may double relative to the erosion in slantwise tillage turning soil upslope46. Therefore, different tillage practices may also be a reason for the reduced prediction efficiencies of the SPCR models.
The study is based on the paper published in Catena17. Instead of a mechanistic-based analysis of topographic influences on soil movement and soil properties as performed in the Catena paper, here we focused on the methods for quantifying topographic metrics and developing topography-based models. We discussed the feasibility and advantages of using topography-based models in studies of the spatial structure of soil properties. Meanwhile, we improved our models by updating algorithms of slope length factor and flow accumulation. The scale of slope length factor measurement was limited to field&#39;s area. Additionally, the deterministic infinity algorithm was used for flow accumulation generation. Compared with the method reported in Li et al.17 which generated flow accumulation with a deterministic eight-node algorithm, the infinity algorithm adopted in this study reduces loops in the flow direction angles and proved to be a better algorithm for low relief areas47.
In conclusion, our results demonstrate the feasibility of topography-based SPCR models in simulating SOC distribution and soil redistribution patterns in agriculture fields. As a cost-effective method to estimate SOC stocks and soil redistribution rates, it is applicable to sites with limited observational data and private lands lacking public access. In future studies, the prediction models could be improved with further refinement and availability of LiDAR data and inclusion of additional topographic metrics. The large-scale soil property maps that were developed based on the models will lead to further understanding of the mechanisms underlying the topographic impacts on soil movement in agricultural landscapes and the fate of SOC at the watershed and regional scales.

Soil redistribution (erosion and deposition) exerts significant impacts on soil organic carbon (SOC) stocks and dynamics. Increasing efforts have been devoted to investigating how SOC is detached, transported, and deposited over the landscape1,2,3. Carbon (C) sequestration and SOC distribution are influenced by gravity-driven soil movement induced by water erosion4,5,6. In cultivated fields, soil translocation by tillage is another important process contributing to C redistribution7,8,9. Tillage erosion causes a considerable net downslope movement of soil particles and leads to a within-field soil variation10. Both water and tillage erosion are significantly affected by landscape topography, which determines the locations of erosional and depositional sites11. Therefore, effective soil erosion regulation and C dynamic investigation in agricultural lands calls for a better understanding of topographic controls on soil erosion and movements.
Several studies have investigated the impacts of topography on soil redistribution and associated SOC dynamics9,12,13,14,15,16,17. Van der Perk et al.12 reported that topographic factors explained 43% of variability in soil redistribution. Rezaei and Gilkes13 found higher SOC in soils on a shady aspect, due to lower temperatures and less evaporation when compared to other aspects in rangelands. Topography may have more significant impacts on soil redistribution in agricultural lands with traditional tillage treatment than those with minimum tillage, due to the interactions between landforms and tillage practices9. However, these findings were primarily derived from field observations, which present difficulties in investigating soil properties at a broader spatial scale. There is a pressing need to develop new strategies to effectively understand spatial patterns of soil properties at watershed and regional scales.
The objective of this study is to develop efficient models to simulate soil redistribution and SOC distribution. Topography-based models using topographic metrics as predictors have been developed to quantify soil erosion and deposition processes. Compared with empirical- or process-based erosion models that employed discrete field samplings to simulate soil erosion18,19, topography-based models could be developed based on topographic information derived from digital elevation models (DEMs) with high resolutions. This approach allows for continuous soil property simulations at the watershed or regional scale. In the past several decades, accuracy of topographic information has substantially improved, with increasing availability of high resolution remotely sensed data. Although previous studies have employed topography-based models to simulate soil properties12,20,21,22, most of these investigations used a single topographic metric or single category of topographic metrics (local, non-local, or combined topographic metrics), which may not have sufficiently explored topographic impacts on soil microbial activity. Therefore, to gain a better understanding of topography controls on soil erosion and C dynamics, we examined a comprehensive set of topographic metrics including local, non-local, and combined topographic metrics and developed multi-variable topography-based models to simulate soil property dynamics. Applications of these models are expected to provide scientific support for better soil erosion control and agricultural land management.
Topographic metrics are generally categorized into one of three categories: a) local topographic metrics, b) non-local topographic metrics, or c) combined topographic metrics. Local topographic metrics refer to local features of one point on the land surface. Non-local topographic metrics refer to the relative locations of selected points. Combined topographic metrics integrate local and non-local topographic metrics. A set of topographic metrics affecting soil erosion and deposition were used in this study to investigate the topographic controls on soil movement and C stocks (Table 1). Specifically, we used four local topographic metrics [slope, profile curvature (P_Cur), plan curvature (Pl_Cur), general curvature (G_Cur)], seven non-local topographic metrics [flow accumulation (FA), topographic relief, positive openness (POP), upslope slope (UpSl), flow path length (FPL), downslope index (DI), catchment area (CA)], and three combined topographic metrics [topographic wetness index (TWI), stream power index (SPI), and slope length factor (LS)].

<table border="0" cellpadding="0" cellspacing="0" style="width:1151px;" width="1150">
	<tbody>
		<tr height="21">
			<td>Light Detection and Ranging (LiDAR) data&#160;</td>
			<td style="width:233px;"></td>
			<td style="width:256px;">http://www.geotree.uni.edu/lidar/</td>
			<td style="width:355px;">Collected from the GeoTREE LiDAR mapping project&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">LECO CNS 2000 elemental analyzer&#160;</td>
			<td>LECO Corp., St. Joseph, MI</td>
			<td style="width:256px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Canberra Genie-2000 Spectroscopy System</td>
			<td>CANBERRA Industries</td>
			<td style="width:256px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">Geographic positioning system</td>
			<td>Trimble&#160;</td>
			<td style="width:256px;">RTK 4700 GPS</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">ArcGIS</td>
			<td>ESRI, Redlands, CA</td>
			<td style="width:256px;">10.2.2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">Statistical Analysis System&#160;</td>
			<td>SAS Institute Inc</td>
			<td style="width:256px;"></td>
			<td></td>
		</tr>
		<tr height="23">
			<td height="23" style="height:23px;">System for Automated Geoscientific Analysis&#160;</td>
			<td>University of G&#246;ttingen, Germany&#160;</td>
			<td>v. 2.2.5, http://www.saga-gis.org/</td>
			<td>GNU General Public License</td>
		</tr>
	</tbody>
</table>

use of principal components for scaling up topographic models to map soil redistribution and soil organic carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Watch this Scientific Journal Video about Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon at JoVE.com

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

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10.1K Views.  University of Maryland. Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Video: Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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

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

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

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

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

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

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

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

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

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 ...

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 ...

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 ...

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic bonds that constitute the organic component of soils. However, the relatively low organic matter content of soils (commonly &#60; 5% by mass) and absorbance overlap of mineral and organic functional groups in the mid-infrared (MIR) region (4,000-400 cm-1) engenders substantial interference by dominant mineral absorbances, challenging or even preventing interpretation of spectra for SOM characterization. Spectral subtractions, a post-hoc mathematical treatment of spectra, can reduce mineral interference and enhance resolution of spectral regions corresponding to organic functional groups by mathematically removing mineral absorbances. This requires a mineral-enriched reference spectrum, which can be empirically obtained for a given soil sample by removing SOM. The mineral-enriched reference spectrum is subtracted from the original (untreated) spectrum of the soil sample to produce a spectrum representing SOM absorbances. Common SOM removal methods include high-temperature combustion (&#39;ashing&#39;) and chemical oxidation. Selection of the SOM removal method carries two considerations: (1) the amount of SOM removed, and (2) absorbance artifacts in the mineral reference spectrum and thus the resulting subtraction spectrum. These potential issues can, and should, be identified and quantified in order to avoid fallacious or biased interpretations of spectra for organic functional group composition of SOM. Following SOM removal, the resulting mineral-enriched sample is used to collect a mineral reference spectrum. Several strategies exist to perform subtractions depending on the experimental goals and sample characteristics, most notably the determination of the subtraction factor. The resulting subtraction spectrum requires careful interpretation based on the aforementioned methodology. For many soil and other environmental samples containing substantial mineral components, subtractions have strong potential to improve FTIR spectroscopic characterization of organic matter composition.

SOM underlies many soil functions and processes, but its characterization by FTIR spectroscopy is often challenged by mineral interferences. The described method can increase the utility of SOM analysis by FTIR spectroscopy by subtracting mineral interferences in soil spectra using empirically obtained mineral reference spectra.

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic ...

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.

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.

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. ...

Electrostatic Method to Remove Particulate Organic Matter from Soil

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

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

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