Peak flow rates are a critical hydrologic variable. They are used to size urban drainage infrastructure, predict flooding, serve as inputs to erosion and sediment transport models, size and gauge the effectiveness of treatment systems such as stormwater control measures, and estimate daily runoff simulations in support of regulations. Therefore, use of peak flow models that produce large errors can result in severe consequences, regardless of whether peak flows are over- or under- estimated. The two most popular peak flow models in the United States are the rational method (RM) and the Natural Resources Conservation Service (NRCS) Technical Release 55 (TR-55) method. In order to reduce errors from modified versions of these models across storm sizes and intensities that are more frequently observed, multiple low-cost, simple adjustment and calibration approaches were incorporated into the models, and their outputs were compared to observed peak flow rates in two adjacent watersheds with similar attributes. Tested approaches included (1) adjustments by iteratively increasing the resolution of soil, land use, and topographical data to better calculate model input variables; (2) direct calibration using experimentally determined ratios (ratio of sum of modeled peak flows to sum of observed peak flows) from rainfall-runoff data collected within the watershed of interest from a variety of events that were randomly subsampled; and (3) indirect calibration using experimentally determined ratios from field collected rainfall-runoff data from a similar neighboring watershed for a variety of events that were randomly subsampled. Peak flow estimates from modified versions of the RM (QRAT-MOD) and NRCS TR-55 method (QNRCS-MOD) parametrized with higher resolution data did not necessarily result in better model performance and did not produce any suitable model fits as defined by a series of goodness-of-fit metrics. Both the QRAT-MOD and QNRCS-MOD models were substantially improved after applying direct calibration ratios, with the resulting calibrated models providing suitable accuracy in most scenarios. Furthermore, many QNRCS-MOD models were also considered suitable after applying indirect calibration ratios calculated from the adjacent watershed. However, this was not the case for QRAT-MOD models when applying indirect calibration ratios, though all goodness-of-fit metrics improved substantially. Results suggest local peak flow estimations resulting from more frequent events can be greatly improved by implementing a single flow monitoring station to collect peak flow calibration data for a relatively small number of events. Though not explored in this analysis, it is likely that these approaches can also improve peak flow estimations for larger, design-sized events.

Soil erosion computation technology plays an important role in planning to prevent and mitigate soil loss and non-point source pollution from agricultural fields. In the US, the RUSLE2 erosion model is extensively used by conservationists to support efforts for adoption of new farm management practices and implementation of conservation alternatives. Within RUSLE2, the impact of precipitation is described by average annual rainfall erosivity (R) which is represented by a smoothly and spatially varying surface that covers the entire US, assuring consistency in erosion predictions for conservation planning. In the current RUSLE2 erosivity database, these surfaces were developed by a laborious process of analyzing and processing data by hand, so this had not been updated since 2001. In this study, a protocol to generate isoerodent surfaces for the continental US is proposed and evaluated. The methodology describes steps that integrate the official RUSLE2 calculations with proposed new methods. The newly generated surfaces were compared to official RUSLE2 erosivity surfaces and evaluated for smoothness. Results indicate agreement with RUSLE2 surfaces for absolute values but with slightly higher spatial and temporal smoothness. Further refinements include the inclusion of small events, determination of spatially varying recurrence intervals, and consideration of two-axis trend interpolation enhanced with additional weighting accounting for data gaps, which gives more weight to weather stations that have more complete datasets. The protocol provides the means for capturing long-term climatic variations impacting soil erosion in a consistent way. This protocol supports forthcoming updates to the RUSLE2 climate database and serves as a baseline for future enhancements in the characterization of changing climatological drivers impacting soil erosion.

Soil erosion is one of the major processes of land degradation. Climate change, marked by alterations in the precipitation spatial and temporal patterns as well as rainfall amounts projected to increase, is expected to exacerbate soil erosion and loss of soil in the agricultural landscape. Understanding soil loss using physically-based water erosion prediction models and improving knowledge of soil erosion of agricultural lands under future climate change scenarios is critical to developing best management practices for the conservation of soil resources as well as to inform decision and policy makers. This study aims at investigating the impacts of future climate changes on soil erosion in the United States. By integrating up-to-date climate datasets this study characterized differences and current trends in precipitation with respect to climate change and applied a climate model ensemble based on the CMIP6 climate scenarios to predict the future climate. These data are downscaled with machine learning algorithms. It also estimates soil erosion in different soil-climate-agricultural management systems from predicted precipitation under future climate change scenarios using the Revised Universal Soil Loss Equation, Version 2 (RUSLE2). Research findings on the impacts of future climate change scenarios on soil erosion in agricultural landscapes will allow the development of climate-driven best management practices and conservation agriculture techniques as well as inform decision and policy makers to reduce soil loss, therefore protecting the limited soil and water resources, and contributing to a sustainable agricultural production and food security.

The Revised Universal Soil Loss Equation, Version 2 (RUSLE2) is the water erosion prediction tool for use by the USDA National Resources Conservation Service (NRCS) for all conservation planning in the United States. USDA NRCS utilizes the Integrated Erosion Tool (IET) that combines RUSLE2 with USDA data sets for soil, climate, and agricultural management. The Agricultural Research Service (ARS) is the USDA’s research agency charged with the development of the RUSLE2 model. RUSLE2 is an advanced computer model that estimates rill and interrill erosion by water, combining empirical and process-based science, for use on personal computers. This research aims at improving RUSLE2 science components, including the development of a web-based user interface for RUSLE2, for use by USDA NRCS. Advanced science components will be developed to quantify rainfall and land management effects on spatial and temporal variability of dynamic soil properties in agricultural watersheds in the United States, with emphasis on the assessment of soil erodibility and the risk of soil erosion under climate change. State-of-the-art technologies needed to measure, identify, and link dynamic soil erodibility to soil loss in the agricultural landscape, such as machine learning algorithms, remote sensing, and non-intrusive visualization and imaging technologies will provide advanced science components for RUSLE2. For the development of a web-based RUSLE2 modeling system, a new cloud based infrastructure is being deployed using the Amazon Web Services (AWS) platform, which will support online databases for climate, soil, and agricultural management data. A new database structure is being designed for RUSLE2, to support server based erosion calculations. AWS services will also provide web servers, spatial databases, geoprocessing capabilities, cooperative source code development and all compute and storage resources. These research findings and products will help understand how climate change and modern management practices impact soil erodibility dynamics. Improvements to RUSLE2 technology will lead to advances in determining soil loss across agricultural landscapes through improved physically based water erosion models. New web-based tools will provide best management practices for soil and water resources conservation under changing environments, contributing to sustainable agriculture and food security, while ensuring environmental health.

Ephemeral gullies (EGs) are channels that form in low parts of the field where runoff concentrates, and are often responsible for considerable soil loss from agriculture fields. Unfortunately, predicting the gradual development of gullies in response to storm events remains challenging. The United States Department of Agriculture has developed several modeling tools to predict the location and dimensions of ephemeral gullies and the resulting soil loss. The tool described herein combines precise geospatial determination of EG pathways with soil erosion and delivery calculations for both those pathways and the contributing hillslopes.Considering the vital importance of determining runoff concentration for EG development, high-resolution (0.5 ~ 3 m) terrain elevation data are processed with specialized geospatial tools to determine topography-driven surface runoff patterns and define swales where concentrated flows occur. This creates integrated surface drainage descriptions defining hillslope areas where sheet-and-rill erosion predominates, and swales where EG gullies may develop. This results in detailed flow maps covering entire fields, optionally considering oriented roughness created by crop rows on flow distribution. These data, along with topography-derived parameters and spatial distributions of soil types and vegetation cover, form the digital landscape description for erosion modeling.The magnitude, frequency, and seasonal distribution of storms are represented by a synthetic series of events derived from long-term climate databases created for the RUSLE2 (Revised Universal Soil Loss Equation, version 2) model. Runoff for each storm event is estimated with RUSLER (RUSLE2-Raster), a two-dimensional (2D) raster implementation of RUSLE2 technology that calculates runoff and sheet-and-rill soil loss for all flow paths covering a field. The RUSLER calculation provides the spatial and temporal distribution of incoming runoff and sediment loads necessary for the calculation of erosion and deposition in the EG channels.The channel flow and sediment transport model EphGEE (Ephemeral Gully Erosion Estimator) employs an excess shear stress approach to determine where flow erosive forces cause soil detachment and transport, and where deposition occurs. EphGEE also calculates the rates at which channels locally deepen and widen, thus predicting how channel geometry evolves during each storm, which depends strongly on knowledge of soil erodibility parameters. EphGEE attempts to estimate how erodibility parameters vary in time and with depth using management operations data available from RUSLE2 databases. In most cases, however, field data and parameter calibration are still necessary.This modeling approach has been applied to monitored fields in the United States. It was successful in determining runoff and concentrated flow paths, resulting in good predictions of locations where gullies form and how they connect to runoff-generating areas. For tilled fields where a less erodible soil layer exists, the model provides good approximations of gully depths and widths. For no-till and pasture-to-crop transitions, where EG cross-sectional shapes may be dependent on how erodibility varies with soil depth, better data or prediction methods are needed to improve model performance.

Two new courses have been developed at the University of Tennessee (UT) as part of the Engineering Fundamentals Division engage program.Each course is 6 semester hours and they are entitled EF 101 -Engineering Approaches to Physical Phenomena and EF 102 -Fundamentals of Engineering Mechanics, respectively.The courses are taken in sequence during the freshman year by students in all engineering majors.An overview of the entire program and details of the EF 101 course (which emphasizes problem solving and various computer skills such as programming and graphics) have been presented previously.The focus of this paper is the EF 102 course.In particular, this paper will outline how statics and particle dynamics are presented in an integrated, collaborative learning environment that includes traditional presentation techniques, hands-on practice in an open-access laboratory, and application through the use of design projects that are developed through the build and test stages.The philosophy of the new course can be summarized as: see the concept, feel the concept, practice the concept, and apply the concept.The "see the concept" phase is presented by a professor in a traditional lecture setting that includes all necessary background material and derivations.A simple example may also be presented.On the same day that a topic is introduced, all the students must go to a open-access laboratory and perform a "physical homework" to "feel the concept."In this open-access laboratory, students actually solve statics and dynamics problems by measuring forces, moments, velocities and accelerations, and comparing those results to calculated predictions.The assigning of traditional homework problems and attendance at recitation or problem sessions provide further "practice of the concept".Graduate teaching assistants run the recitation sessions.During the recitation sessions, the students work problems in small teams in a collaborative learning environment.Finally, team design projects are assigned to allow the students to "apply the concept."These projects usually require the integration of several concepts developed in the course as well as the use of computer tools developed in the EF 101.This paper provides the details of the engage strategy for the EF 102 course including examples of the integrated material and teaching methods.Details of how a concept is presented and reinforced through the four phases (see, feel, practice, and apply) will be outlined.Examples of how computer skills developed in the EF 101 course are integrated with the mechanics concepts presented in the EF 102 course are also presented.Preliminary results from a pilot Page 4.334.

Highlights Terminal velocity was measured for small, standardized sizes of corn stover stem fractions with a vertical wind tunnel built to aerodynamically suspend particles. Mean terminal velocity ranged from 2.84 m s -1 to 7.74 m s -1 for dry pith-internode and wet rind-node fractions, respectively. Anticipated separation of corn stover stem particles using terminal velocity differences was viable for dry (11% w.b.) particles of pith, rind, node, and internode. But, many wet (43% w.b.) fractions had similar terminal velocities, thereby reducing separation propensity. Abstract. Terminal velocity of corn stover stem fractions was determined for particles standardized to match particle sizes (1.3 cm long x 0.31 cm diameter) of switchgrass nodes and internodes. The practical application was to measure the potential aerodynamic conditions for sorting and separating size-reduced anatomical components of pith versus rind, node versus internode, and at two moisture contents (11% and 43%, wet basis). Terminal velocities grouped by dry pith, wet pith, dry rind, and wet rind resulted in a trend of increased mean terminal velocities of 3.28, 5.31, 6.38, and 7.68 m s-1, respectively, when averaged across node and internode. The increased moisture and the selection of the rind component had increased terminal velocity that was attributed to increased particle density. Terminal velocity for a node was generally statistically greater than that of an internode for a given condition, except for the statistically-equal terminal velocities for node and internode of wet rind. Also, terminal velocity for internode of dry pith and of wet pith were statistically equal. Thus, exceptions to the general trends were discovered. Mean terminal velocity ranged from 2.84 m s-1 to 7.74 m s-1 for dry pith-internode and wet rind—node particles, respectively. Practical separation of corn stover stem particles using terminal velocity differences was viable for dry (11% w.b.) particles of pith, rind, node, and internode. Many terminal velocities of wet (43% w.b.) fractions were statistically equal leaving only wet pith-internode available at this moisture for aerodynamic separation. Particle density varied almost 10-fold for the experiment, and this was attributed to the various anatomical component and range of moisture content. Highly significant correlations of particle density with terminal velocity may have represented a cause-and-effect factor. Keywords: Anatomical component, Biomass property, Corn Stover, Physical experiment, Separation, Sorting, Vertical wind tunnel.

A new modeling framework has been developed to extend RUSLE2 erosion prediction technology for large areas with the use of machine learning and improved geoprocessing tools. RUSLE2 has been traditionally used to estimate soil loss over one-dimensional hillslopes that would be representative of the erosion for an area. The new approach calculates sheet-and-rill erosion in two horizontal dimensions, producing detailed maps of soil loss that can be used to identify critical areas and guide the design of soil conservation measures for entire watersheds. Geoprocessing algorithms developed for this application utilize high-resolution gridded digital elevation models to analyze overland flow paths and create a drainage network and corresponding hillslopes, ensuring the correct representation of topography and runoff distribution for RUSLE2 calculations. Topographic attributes, soil types, and land management parameters are extracted for each hillslope from GIS layers and associated databases. Soil erosion is then inferred from a machine learning procedure that uses a sequential, densely connected artificial neural network (ANN). A total of 12 input parameters describe climate, topography, soil properties, and vegetation and agricultural management operations for each hillslope. The ANN determines the corresponding long-term average soil loss of the area. To illustrate the methodology, a map of average annual soil loss for the entire state of Iowa, USA, was created covering all areas typically planted with corn and soybeans that were assumed to be managed as two-year rotations, with a conservation tillage practice, which cover about 96% of the state. Elevation data was derived from the state‘s Lidar survey, resampled to 10-meter resolution. Land use data was obtained from the USDA-NASS Crop Data Layer for 2018. A set of independent, simple-profile RUSLE2 calculations were used to train the ANN. The training set included five climate definitions for different regions of the state, 7 soil types from the SSURGO database of varying erodibility, and 10 typical RUSLE2 management descriptions for corn and soybean rotations with varying yields. Topography was represented by uniform hillslopes with lengths varying from 25 to 300 ft (7.6 to 91 m) and slopes between 1% and 18%. All input parameters were combined to create a training set of 459,900 simulation results. Validation tests when compared with average values calculated with the original RUSLE2 model showed that ANN computed erosion values were within 0.27 tons per acre (0.61 Mg/ha). Application of the geoprocessing algorithms for the entire state required 20 hours of processing time across 12 cores using Message Passing Interface parallelization. The calculation of soil loss through the trained ANN for about 186 million hillslopes in 1712 HUC-12 watersheds covering the entire state was computed in about 30 minutes on a single core. A similar calculation using RUSLER-Distributed on a 192-core server (websim.rusle2.org) would take about 43 days. The new methodology shows that the introduction of machine learning allows RUSLE2 to be easily extended to watershed scales while maintaining a high level of spatial detail, making it a useful tool for prioritization of areas for erosion control, evaluating the impact of best management practices, or in the design of soil conservation practices.

Highlights Terminal velocities were measured for wheat stem nodes and internodes for similar particle dimensions to investigate the feasibility of aerodynamic separation. Mean measures of terminal velocities for wheat stem nodes and internodes were 4.91 and 3.35 m s -1 , respectively, that coincided with values of 4.92 and 3.37 m s -1 calculated for spherical particles (Mohsenin, 1970). Wheat stem particle mass ranged from 0.015 (internode) to 0.041 g (node) that significantly correlated with terminal velocity ranging from 3.13 to 5.14 m s -1 , respectively. Wheat stem particle density ranged from 112 to 297 kg m -3 that significantly correlated with terminal velocity ranging from 3.12 to 5.11 m s -1 , respectively. Abstract. Efficient separation of physiological plant components potentially improved the targeting of components to best uses. The terminal velocity property used an opposing air velocity to equilibrate particle weight with the sum of the drag and buoyancy forces. This study used particles of similar dimensions to ascertain the effect of particle mass and density on experimental measures of terminal velocity in a wind tunnel and as calculated by reliable equations. Similar particle diameters, lengths, and volumes of wheat stems ranged from 0.362 to 0.376 cm, 1.25 to 1.28 cm, and 0.131 to 0.141 cm3, respectively. Moisture content was 12% wet basis. Wheat stem internodes had individual particle mass and density ranging from 0.015 to 0.019 g and 113 to 144 kg m-3, respectively, and mean Terminal Velocity Wind Tunnel (TVWT) terminal velocities for wheat stem internodes that ranged from 3.13 to 3.58 m s-1. Nodes had individual particle mass and density ranging from 0.031 to 0.041 g and 236 to 297 kg m-3, respectively, and mean TVWT terminal velocities for wheat stem nodes that ranged from 4.62 to 5.14 m s-1. Thus, no overlap in values was observed for particle mass, particle density, and terminal velocity between wheat stem internode and wheat stem node. This observation supports the potential of using terminal velocity to separate node from internode for similar-sized wheat stems at a given moisture content. Keywords: Aerodynamic separation, Anatomical component, Biomass property, Physical experiment, Sorting, Terminal velocity, Vertical wind tunnel, Wheat stem particles.

Introduction Promoting sustainable crop production is enhanced by an effective method to assess soil health. However, soil health assessment is challenging due to multiple interactions among dynamic soil properties (i.e., soil health indicators) across management practices and agroecological regions. We tested several currently popular soil health assessment methods for cropping systems in Tennessee in the southeastern US and found that these methods failed to differentiate Tennessee soil health under long-term conservation and conventional management. Materials and methods This study developed a Tennessee weighted soil health index (WSHI) by: 1) selecting a set of management-sensitive soil health indicators, 2) assigning meaningful weights to indicators, and 3) normalizing the scores based on regionally relevant undisturbed natural reference sites. The tested cropping systems treatments were moldboard plow (MP) in continuous soybean (SS), no tillage (NT) in SS, NT with wheat cover (NTW) in SS, no cover and chisel plow (NCCT) in continuous cotton (CC), no cover and no tillage (NCNT) in CC, and hairy vetch cover and no tillage (VCNT) in CC. In addition, two woodlots and one grassland sites in the vicinity of the cropping systems were selected to represent undisturbed natural systems. Results and discussion Out of 22 indicators that proved to be management-sensitive, six were selected as a minimum dataset (MDS). These were particulate organic matter C (POM-C), soil respiration from 4-day incubation (4d CO 2 ), small macroaggregate (0.250-2mm)-associated C (SMA-C), surface hardness (PR15), microbial biomass N (MBN), and bulk density (BD). Measured values of the MDS indicators were transformed into unitless normalized scores (based on the regional range of the indicator), and finally integrated into WSHI scores using a weighted-addition approach. Additionally, the soil health gap (SHG) between the soil health of the regional reference system and different cropping systems was calculated. Results revealed that WSHI strongly differentiated soil health between long-term conservation and conventional managements practices. The WSHI scores for southeastern cropland soils varied as follows: VCNT = NTW > NT > NCNT ≥ NCCT ≥ MP. The SHGs under MP, NCCT, NCNT, NT, NTW, and VCNT were 85.5, 79.9, 68, 45.1, 25.2, and 24.3, respectively, relative to the average WSHI of three undisturbed systems. Results showed that the WSHI approach is effective in more meaningful regional assessment of soil health and SHG can be a potential metric for comparing soil health across agroecological regions.

Terrestrial LiDAR (light detection and ranging) has been used to quantify micro-topographic changes using high-density 3D point clouds in which extracting the ground surface is susceptible to off-terrain (OT) points. Various filtering algorithms are available in classifying ground and OT points, but additional research is needed to choose and implement a suitable algorithm for a given surface. This paper assesses the performance of three filtering algorithms in classifying terrestrial LiDAR point clouds: a cloth simulation filter (CSF), a modified slope-based filter (MSBF), and a random forest (RF) classifier, based on a typical use-case in quantifying soil erosion and surface denudation. A hillslope plot was scanned before and after removing vegetation to generate a test dataset of ground and OT points. Each algorithm was then tested against this dataset with various parameters/settings to obtain the highest performance. CSF produced the best classification with a Kappa value of 0.86, but its performance is highly influenced by the 'time-step' parameter. MSBF had the highest precision of 0.94 for ground point classification but the highest Kappa value of only 0.62. RF produced balanced classifications with the highest Kappa value of 0.75. This work provides valuable information in optimizing the parameters of the filtering algorithms to improve their performance in detecting micro-topographic changes.

Over the past 25 or so years soil health has been broadly studied, sometimes justly criticized, but commonly accepted and appreciated by stakeholders. Rather than follow the approach from over the last 10 to 15 years of concentrating on details of soil health indicators and agglomerated indices, recent publications have instead begun to analyze broader questions that have never really been satisfactorily answered, including questions about the rationale, limitations, and true meaning of soil health. This article joins the trend toward deeper analysis by examining the literature to propose the following: (1) why soil health is important, whether approached from a critical zone perspective or some other viewpoint; (2) more closely linking soil health and a critical zone approach to material fluxes, thereby informing how, where, and when soil health should be measured; (3) a clearer definition of the meaning of soil health in local relative terms; and (4) a stronger linkage between soil health and sustainability. The goal of this proposal is not to develop a specific conceptual model, but rather to spur further discussion leading to a more solid definition and description, which may in turn lead to more robust and broadly applicable approaches to understanding and measuring soil health.

The advances of remote sensing techniques allow for the generation of dense point clouds to detect detailed surface changes up to centimeter/millimeter levels. However, there is still a need for an easy method to derive such surface changes based on digital elevation models generated from dense point clouds while taking into consideration spatial varied uncertainty. We present a straightforward method, Las2DoD, to quantify surface change directly from point clouds with spatially varied uncertainty. This method uses a cell-based Welch’s t-test to determine whether each cell of a surface experienced a significant elevation change based on the points measured within the cell. Las2DoD is coded in Python with a simple graphic user interface. It was applied in a case study to quantify hillslope erosion on two plots: one dominated by rill erosion, and the other by sheet erosion, in southeastern United States. The results from the rilled plot indicate that Las2DoD can estimate 90% of the total measured sediment, in comparison to 58% and 70% from two other commonly used methods. The Las2DOD-derived result is less accurate (65%) but still outperforms the other two methods (30% and 48%) for the plot dominated by sheet erosion. Las2DoD captures more low-magnitude changes and is particularly useful where surface changes are small but contribute significantly to the total surface change when summed.

HighlightsA vertical wind tunnel was fabricated to aerodynamically suspend switchgrass particles to measure terminal velocity.Overall terminal velocity estimates for node and internode were 8.04 and 5.68 m s-1, respectively.Terminal velocity prediction equation for spherical particles (Mohsenin, 1970) produced values almost identical to experimental values unlike an equation for infinite-length cylindrical particles.Hollow switchgrass particle behavior was complex with particle bouncing, rolling, tumbling, and other dynamic behaviors during aerodynamic equilibrium. Abstract. Terminal velocity is a fundamental aerodynamic property often used to separate plant anatomical components. A vertical wind tunnel was fabricated to investigate whether terminal velocities of switchgrass stem internodes and nodes were distinctly different. Wind tunnel features included a clear observation section for suspended particles and fine adjustment of airflow. Each sample terminal velocity was measured with 45 anemometer readings from nine locations and five repeated measures. The experiment evaluated two sample moisture contents (52% and 15% wet basis, hereafter referred to as w.b.), three particle lengths (0.64, 1.27, and 2.54 cm), node versus internode, and 12 replications. The 6,480 anemometer readings provided significant differences between factors and interactions. The greatest magnitude in mean terminal velocity difference was 4.55 m s-1 (i.e. 9.62 to 5.07) that resulted in a velocity ratio of 190% [(9.62/5.07) x 100]. The least magnitude of mean terminal velocity difference, while significant, was 0.36 m s-1 (i.e. 6.59 to 6.23). This offered a limited difference and may indicate that separations may need to be confined within groups of moisture category and/or particle length. Terminal velocity measurements were most similar to those calculated using the Mohsenin (1970) spherical particle equation yielding a mean (6.86 vs. 6.91 m s-1), standard deviation (1.41 vs. 1.42 m s-1), minimum (4.65 vs. 4.69 m s-1), and maximum (9.95 vs. 10.03 m s-1) for measurement and spherical particle equation, respectively. Keywords: Anatomical component, Biomass property, Physical experiment, Separation, Sorting, Switchgrass, Vertical wind tunnel.

Soil health assessment is very important for evaluating agroecosystem sustainability from the adoption of conservation management practices. Unfortunately, a universal standardized soil health test is not yet available despite the development of several regional and commercial methods. Some regionally developed methods have been applied across diverse agroecological regions, but this practice requires further scrutiny. Therefore, this study was conducted to evaluate the feasibility of two regionally focused methods- the Comprehensive Assessment of Soil Health (CASH), a well-established method and the Alabama Soil Health Index (ASHI), a recently developed method-to assess soil health of row-cropping systems in the southeastern United States. By leveraging three ongoing cropping system experiments, that is (a) continuous soybean [Glycine max (L.) Merr.] (SS), (b) corn (Zea mays L.)-soybean (CS) rotation, and (c) continuous cotton (Gossypium hirsutum L.) (CC) systems, soil health indicators included in CASH and ASHI methods were analyzed, and soil health scores were calculated. Our test criteria depended on the sensitivity of CASH and ASHI methods to differentiate long-term management-induced changes in these systems. In general, we found that the overall soil health scores of both CASH and ASHI did not strongly differentiate diverse tillage, cover crops, or N rate treatments. The scores for both conventional and conservation management treatments were rated as low to medium (scores <60) with CASH method and medium to high (scores >50) with ASHI method. Overall, these soil health approaches were not found to be sensitive enough to detect management-induced changes in soil health in various cropping systems of the southeastern United States. Our results highlight the need for extensive calibration and validation of CASH, ASHI, and similar approaches prior to wider adoption across agroecological regions.

Climate extremes pose a global threat to crop security. Conservation agriculture is expected to offer substantial climate adaptation benefits. However, synergistic effects of conservation practices on yield during normal versus extreme climates and underlying regulatory mechanisms remain elusive. Here, we analyze 29-years of climate data, cotton (Gossypium hirsutum L.) yield, and soil data under 32 management practices in Tennessee, USA. We find that long-term no-tillage enhanced agroecosystem resilience and yield stability under climate extremes and maximized yield under favorable climate. We demonstrate that no-tillage benefits are tied with enhanced soil structural stability and organic carbon. No-tillage enhanced the effectiveness of legume cover crop in stabilizing cotton yield during relatively dry or wet, and dry years, while nitrogen fertilizer rate and precipitation timing, controlled yield stability in wetter years. Our findings provide evidence-based insights into how management strategies can enhance agroecosystem resilience and production stability in climate extremes. Long-term no-tillage systems enhance cotton yield resilience to climate extremes through improved soil quality in Tennessee, USA, according to a 29-year rain-fed plot-scale cotton experiment.

Quantifying the impacts of agricultural management on soil health is critical for making informed sustainable management decisions as soil resources inevitably undergo alterations due to management. One recently popular and soil biology-based method of soil health assessment is the Haney Soil Health Test (HSHT), whose most recent version is known as the "soil health tool", attempts to integrate soil health and fertility. Comprehensive evaluation of the HSHT and its underlying indicators in different agroecological regions is currently lacking. This study therefore evaluates the HSHT on three ongoing field experiments in southeastern United States: (a) 39 yr of continuous soybean [Glycine max (L.) Merr.] with different tillage treatments, (b) 4 yr of corn (Zea mays L.)-soybean rotation with different cover crop treatments, and (c) 37 yr of continuous cotton (Gossypium hirsutum L.) with tillage, cover crops, and nitrogen (N) rates. Soil samples (0- to 15-cm depth) were analyzed for HSHT indicators (i.e., Solvita CO2-C, water-extractable organic C [WEOC], water-extractable organic N [WEON], and WEOC/WEON), two versions of soil health score (SHS) calculations (SHS2015 and SHS2018), and potential N mineralization (Nmin) rates. Additionally, H3A- and Mehlich-1-based extractable soil nutrients were determined to test the fertility component of HSHT. The individual HSHT indicators, SHS2015, SHS2018, and Nmin showed inconsistent responses to management, where most variation in SHS was driven by WEON (0.68 R-2( )> 0.86; p < .001). Additionally, H3A solution extracted nutrients with higher variability (18.5 76) than Mehlich-1. This lack of consistent to response to management in southeastern U.S. croplands implies that comprehensive evaluation and/or modification of HSHT is required for broader applicability.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1326 Hands-On Teaching of Engineering Fundamentals Daniel C. Yoder, J. Roger Parsons, Chris D. Pionke, and Fred Weber Agricultural and Biosystems Engineering Department Department of Mechanical, Aerospace, and Engineering Science Department of Mechanical, Aerospace, and Engineering Science Chemical Engineering Department The University of Tennessee Abstract Driven by ABET2000 requirements, input from an industry-based Board of Advisors, and feedback from students and alumni, The University of Tennessee College of Engineering is well underway in a major renovation / reconstruction of its Freshman Engineering program. This effort is an integrated approach to the Freshman curriculum, with a 6-semester hour first- semester course emphasizing problem-solving, teamwork, design concepts, and computer tools (engineering graphics and computer programming), all based around the study of low-level introductory physics material. The second thrust is a second-semester 6-hour course integrating statics and dynamics, while assuming and using mastery of the material from the first semester. Following the lead of educational theorists, the effort is trying to include as many different forms of learning opportunities as possible. The learning cycle begins with a classroom lecture to introduce the concept, a hands-on laboratory “physical homework” experience to encourage student ownership of the concept, a recitation-style working session to provide practice with the tools available in using the concept, homework assignments to provide practice, and a team design project requiring mastery and application of several of the concepts. This report concentrates on the importance of and techniques used in the hands-on laboratory setting. The hands-on laboratory physical homework is designed to help students personalize and “feel” the concept. To this end, it uses very simple experiments and includes analyses of experimental results. These experiments are devised using the following general guidelines: 1) the scale of the experiment should be within the normal range of the student’s experience; 2) students should (literally) feel the physical process they are trying to measure; 3) differences between situations should be very noticeable and easily measured; 4) data collection tools should be crude and easy to use; 5) data uncertainty and its implications are emphasized throughout. This report describes the reasoning behind and structure of the lab experiences, and provides examples of specific experiments based on these principles.

Resilient agroecosystems are foundational for stable and profitable food, feed, and fiber production in the face of increasing climatic perturbations and environmental stresses. Enhanced soil and environmental benefits of cover crops applied to conservation tillage systems has been well documented. However, little is known about the timespan for interactions of no tillage and cover crops to achieve enhanced yield and yield stability while lowering N fertilization. Using a long-term continuous cotton experiment in southeastern USA, we analyzed yield data collected from 1986 to 2018 from 32 management systems to identify how management duration controls the synergistic effect of applied mineral N rates (0, 34, 67, and 101 kg ha(-1)), cover crops (no cover [NC], hairy vetch [HV], crimson clover [CC], and winter wheat [WW]), and tillage practices (no tillage [NT] and conventional tillage [CT]) on cotton yield and yield stability. Yield stability was analyzed using Finlay-Wilkinson regression model, Wricke's Ecovalance, and coefficient of variation, and a mixed model approach was used to compare the yield and yield stability within three time phases (1 -10 years, 11-20 years, and 21 - 33 years) at the 95% confidence level. During the initial 10 yr period (phase 1) CT resulted in greater cotton yield (7%) and yield stability than NT. However, in phase 2 (11-20 years) and phase 3 (21 - 33 years) NT led to greater yield (7%) and yield stability for almost all cover crop x N interactions, except for zero N following NC and WW. Increased N during the initial phase reduced both yield and yield stability under legume cover crops. During phases 2 and 3, however, the higher N rates (67 and 101 kg N ha(-1)) increased cotton yield, although management systems with 0 and 101 kg N ha(-1) showed the largest temporal yield variability. Legume cover crops increased yield and yield stability under low N rates. The maximum combined yield and yield stability benefit was obtained from HV cover on NT with additional application of 34 kg N ha(-1). Our results suggest that after the initial phase NT delivers the most consistent yield benefits while enhancing yield stability against unfavorable environmental conditions. Long-term integration of legume cover crops (particularly HV) to NT systems was effective in maintaining high yield and increasing yield stability while lowering N rates.

Postconstruction stormwater regulators generally require hydrologic designs generated by approved science-based tools. They then review those designs to ensure that all pertinent rules and codes are met. This design-review sequence is done in various ways, but unfortunately the pass/fail bar is often not clearly defined, making the roles of both designer and reviewer more difficult than necessary. We herein describe a system built to integrate and improve the process, taking into account feedback from both reviewers and regulators while incorporating a physically based model accurately describing site hydrology. We hope this experience and lessons learned will help others develop similar design-review approaches for postconstruction stormwater or other natural resource development. Comprehensive design-review must aim toward a common ultimate goal. The US Environmental Protection Agency (USEPA) promulgated postconstruction stormwater rules, establishing local regulatory bodies as Municipal Separate Storm Sewer System (MS4) entities based on their management of infrastructure collecting urban stormwater runoff and discharging it to streams and rivers (USEPA 2018). Each MS4 must develop a comprehensive plan using ordinances, permits, education, and guidance to encourage progress so that discharges will ultimately meet expectations. The rules hold both for individual permits for larger communities (Phase 1 MS4s) and for state general…