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2021 NCSS National Conference – Abstracts

Live Oral Pre-Recorded Posters Focus Teams

Live Oral Technical Presentations — Wednesday, June 9, 2021

1a: Dynamic Soil Properties 1b: Ecological Site Descriptions 2a: Soil Health 2b: Soil Survey

1a: Dynamic Soil Properties

1a-1: Leveraging biogeochemical modeling to generate novel dynamic soil property maps

Presenting Author: Jason P. Ackerson, Agronomy Department, Purdue University
Co-author: Davide Cammarano, Agronomy Department, Purdue University

Developing dynamic soil survey products requires an understanding of the temporal evolution of dynamic soil properties (DSPs) due to changing management and climate regimes. Typically, quantification of the temporal evolution of DSPs is conducted using long-term field trials. While these trials provide valuable insights into the temporal evolution of DSPs, due to the logistics of operating long-term trials, the scope of such trials is limited to a small number of soils and management regimes. Therefore, we need methods to scale the understanding of DSP evolution from long-term experiments to unstudied combinations of soils and management.  In this paper we present a framework that leverages biogeochemical process modeling to quantify DSP evolution and generate new DSP data.  We used the agricultural system and biogeochemical model DSSAT-CENTURY to simulate the temporal evolution of soil carbon stocks over 100 years of corn-soy bean rotations for 120 soil components in MLRA 111. We then use the simulated time series of carbon stocks to calibrate empirical relationships between soil carbon stock dynamics and static soil properties (e.g. drainage, texture, etc.). These empirical relationships were then used to scale results of the modeling analysis to every soil component in the study region producing maps of equilibrium soil carbon stocks and carbon stock vulnerability. This approach demonstrates how we can couple existing soil data products (e.g. SSURGO) with biogeochemical process models to develop novel DSP maps.

1a-2: How does texture influence soil dynamic properties influence on soil N?

Presenting Author: Dorcas Franklin, University of Georgia, Department of Crop & Soil Sciences
Co-author: Anish Subedi, University of Georgia, Department of Crop & Soil Sciences

Management decisions driving differences in availability of nitrogen in pasture soils can be useful for forage production and environmental protection. In our previous studies in pasture soils in Georgia Piedmont, we found dynamic soil properties significantly influence soil N. Soil compaction (measured as Bulk Density) and both stable (Loss-on-Ignition Carbon; LOI) and active (Permanganate Oxidizable Carbon; POXC) carbon pools were significant predictors of plant available nitrogen measured as nitrate (NO3) and inorganic nitrogen (NO3 + NH4; IN). POXC, which is more sensitive to management changes than LOI, was found to be more reliable predictor of IN which could help farmers prevent toxic N forage concentrations.  In this study, in grazed pasture soils from Watkinsville and Eatonton in the Georgia Piedmont, we investigated the differences in prediction of plant available N fractions using dynamic soil properties BD, LOI and POXC as predictors in a recursive partition model for different texture classes. The threshold values were determined for the best predictors which significantly changed levels of IN or NO3 in the soil. Results showed that POXC was a better predictor of NO3 and IN for all textural classes except Loam and Sandy Clay. Bulk density was better predictor for Loam and Sandy Clay soils.

1a-3: Conceptualizing permafrost as a dynamic soil property: assessing and integrating dynamic change over space and time

Presenter: Nic Jelinski, University of Minnesota Department of Soil, Water, and Climate

Permafrost is a critical soil property that influences soil properties and interpretations over multiple spatial and temporal scales in circumpolar regions. The very nature of the English term (permafrost) used to describe materials which remain at or below zero degrees C for two or more years implies a permanence that neither the technical definition of the term nor the physical and ecological realities of the phenomena conform to. Permafrost is best conceptualized as a dynamic soil property that responds to inter-annual variability, ecological and land use change, and long-term climate trends. Conflicting viewpoints on the genesis and classification of permafrost-affected soils, and resulting issues in representations of permafrost-affected soils in spatial products and databases can be traced to its dynamic nature. Permafrost can therefore be integrated into existing frameworks for dynamic soil properties, which can reveal new paths forward for delivering dynamic information to soil survey users that is consistent with inter-annual, ecological, and climatic change.

1a-4: Harnessing the Power of the NCSS to Inform Science-Based Management Decisions for Increased Soil Carbon Sequestration and Soil Health

Presenting Author: Jocelyn Lavallee, Colorado State University
Co-authors: Amy Swan, Michelle L. Haddix, and M. Francesca Cotrufo

Effective land-based solutions to climate change mitigation and soil health improvements require actions that maximize soil carbon storage without leading to nitrogen losses. Land management for C sequestration is often informed by bulk soil C inventories, without considering the form in which C is stored, its persistence, or the soil’s capacity for additional storage. Recent frameworks suggest that soil organic carbon stocks, along with their formation, persistence, and responses to N availability can be better described if soil organic matter is broadly divided into a particulate organic matter (POM) and a mineral associated organic matter (MAOM) components. Additionally, current understanding of the specific effects of land management on soil C cycling comes largely from studies conducted on the topsoil (< 30 cm), but physicochemical and biological properties differ markedly between subsoils and topsoils, and there is increasing evidence that management of soil C storage should consider both. Despite their importance for C storage, we know very little of how to predict subsoil C storage across different soil types, or how management affects it. Our current work couples the National Cooperative Soil Survey (NCSS) Soil Characterization Database and the National Soil Survey Center – Kellogg Soil Survey Laboratory (NSSC-KSSL) soil archive with soil organic matter physical fractionations to determine drivers of both topsoil and subsoil C stocks and their distribution between MAOM and POM fractions. We will present results of our work thusfar, including initial fractionations of 500 diverse topsoils from the NSSC-KSSL archive and their relationships to key predictor variables. Our project aim is to use machine-learning techniques to predict C storage and storage capacity on the basis of variables in the NCSS and other databases, including soil physicochemical properties, and soil taxonomy, ecological edaphic site properties, land management, and climate. This work will maximize the use of the NCSS and provide direct ties between the soil’s capacity to accrue and store C and management practices.

1a-5: Measuring Soil Carbon within San Antonio City Limits

Presenting Author: Travis Waiser, USDA-NRCS, South Central Soil Survey Region
Co-author: Ashley Anderson, USDA-NRCS, South Central Soil Survey Region

Residents of San Antonio, Texas contacted their local NRCS office about conducting a study to measure the current organic carbon in the soils within the city limits of San Antonio, Texas. These residents were aware of the Rapid Carbon Assessment Study that was completed by NRCS a few years ago. These citizens proposed a partnership between themselves, the NRCS, and the City of San Antonio to obtain a base line of soil carbon stored in the soils of the city and then develop a plan of best management practices that the city, businesses, and homeowners could use to improve the amount of carbon stored in the soils within the city. San Antonio lies within Bexar County and includes the intersection of four Major Land Resource Regions (MLRA). The goal of this study is to produce an estimate of how much carbon is currently stored within the city limits. Using stratification data by soil types, land cover, density of urban land structures, parks, schools, among other features that might be important, and combine that data with existing digital soils layers. ArcGIS software will be used to overlap all the layers and select potential sampling sites. Soil carbon will be measured with a new technique called mid-infrared spectroscopy (MIR). Using MIR, soil scientists and lab staff will be able to provide results in a shorter timeframe and at a lower overall cost as compared to traditional lab techniques.

1a-6: Dynamic Soil Properties and Ecological Site Inventory: Benefits of Dual Project Development and Execution

Presenting Author: Dee Cabaniss Pederson, USDA-NRCS, Georgia
Co-author: Dan Wallace, State Resource Inventory Coordinator, NRCS, Athens, Georgia

Dynamic soil properties (DSPs) projects provide ranges of soil potential and central tendencies relevant to management at human timescales. Ecological sites provide a framework for describing the relationship between soils and vegetation. They are groupings of soil map unit components with similar vegetation, primary productivity, and management requirements. Dynamic soil property information should be housed in ecological site descriptions (ESDs) and extended across the landscape and soil map units. These potentials can inform land management decisions and augment conservation planning efforts. Developing complementary DSP and ESI projects and conducting them concurrently maximizes agency resources and increases the usefulness of refined soil survey information.

1a-7: Dynamic Soil Properties and Ecological Site Inventory on Coastal Plain Soils: An Integrated Approach on Longleaf Pine (Pinus palustris) and Pasture Land Uses

Presenting Author: Daniel F. Wallace, USDA-NRCS, Georgia
Co-authors: Dee Pederson, Assistant State Soil Scientist; Philip Brown, State Grazing Lands Specialist; and Scott Moore, MLRA Soil Scientist

In 2010, Georgia NRCS and partners assessed dynamic soil properties (DSPs) on a longleaf pine ecological site. A DSP soil change study was executed on Tifton and Dothan map units in MLRA 133A while collecting vegetation information using Jornada Monitoring Manual protocols. Contrasting managements were 1. Frequently burned longleaf pine savannas >80 years old and 2. Cow/calf beef cattle pastures >15 years old. Random sampling of six longleaf and six pasture sites allowed for statistical inference. Vegetative measures included species composition, plant productivity, and either pasture condition scoring or forestry measures. Measured DSPs included soil organic carbon, infiltration, aggregate stability, compaction, earthworms, and β–glucosidase activity. Properties were compared across and within management systems. Results exhibit higher total carbon content and infiltration rates in surface horizons of longleaf savanna than pasture. However, higher pH, electrical conductivity, and β–glucosidase activity in pastures reflect fertilization and cattle presence. Seventy-four plant species occurred in one longleaf site compared to a maximum of nine species in pasture. Conversely, plant productivity averaged 5,700 lbs/ac in pasture but only 3,200 in longleaf ground layers, again reflecting management. These results demonstrate the power and importance of a soil change study. Comparing property ranges in different land uses shows the potential ranges in properties of soils and vegetation. Such data can provide conservation planners and land managers more accurate expectations for soil health management systems.

1a-8: Stratifying soils for locally relevant and nationally coherent soil health targets

Presenting Author: Vance W. Almquist, Soil Health Institute
Co-authors: Looker, N.; Morgan, C.L.S.; Honeycutt, C.W., Soil Health Institute, Morrisville, North Carolina

As the Nation’s farms transition from small-scale trials of soil health management practices to full-scale operationalization, practitioners and researchers alike are expressing the need for decision tools that allow concrete evaluations of management outcomes on soil health and ways of understanding those benefits at scale. One data product of interest, identified by a variety of stakeholders as being central to continued implementation of soil health management is the concept of a benchmark or a Soil Health Target, which would quantify how ‘healthy’ a soil can become. Despite the conceptual simplicity of Soil Health Targets, there are several technical challenges which need to be addressed for them to become an operational, useful concept that can be applied at the continental scale. Primary among those challenges is the development of a nationally coherent product which allows for the extrapolation of soil health measurements to comparable soils, thereby enabling a benchmark condition to be established. Herein we present a parsimonious approach, using soil taxonomy, to group soils into 27 unique categories. These categories represent soil physical (texture, drainage, climate) and chemical (clay chemistry, CEC, Base Saturation, etc.) properties known to drive differential responses in soil condition due to changes in management. Several case-studies will be discussed to illustrate the utility of this approach in the development of Soil Health Targets.

1b: Ecological Site Descriptions

1b-1: The Completion of Ecological Site Descriptions through the Characterization of Dynamic Soil Properties and Their Relation to Soil Organic Carbon

Presenting Author: Shannon Newell, University of Tennessee, Knoxville
Co-authors: Jennifer Mason and Belinda Ferro
, USDA-NRCS, Soil Survey Office in Clinton, Tennessee

Quantifying how dynamic soil properties (DSP) are affected by different management regimes is essential for understanding how we can better manage and conserve these vital resources. The Dewey soil series of East Tennessee is considered a “critical” soil type that the Natural Resource Conservation Service (NRCS) is currently working to incorporate into Ecological Site Descriptions (ESD). The State and Transition Models (STM) within the ESDs associated with the Dewey soil series will be evaluated and refined based on the results of this study. For this study, the University of Tennessee is working alongside the National Cooperative Soil Survey (NCSS) in an effort to enrich these ESDs with a wide range of physical, chemical, and biological soil characteristic data. To accomplish this, soil has been collected from five sites each considered representative of one of five different management regimes: well-managed cropland, poorly-managed cropland, well-managed pasture, poorly-managed pasture, and a reference state. The collected soil has been transported to both the Kellogg Soil Survey Laboratory (KSSL) and to the University of Tennessee Institute of Agriculture (UTIA). The KSSL will analyze soil samples in accordance to the DSP intensive tier guidelines, and UTIA will run duplicate analyses along with some unique biological characteristic analyses. Resulting data will be analyzed using structural equation modeling (SEM) to produce a visual representation of the data and to enhance STMs associated with the current ESDs for East Tennessee.

1b-2: Title: Leadership Development in Soil and Ecological Inventory through a Mentoring Program

Presenting Author: Ann J. Tan, USDA-NRCS, Soil and Plant Science Division
Co-author: Jamin K. Johanson, USDA-NRCS, Soil and Plant Science Division

Leadership development is critical for organizations to thrive and be sustainable over time. Organizations that promote technical skills as well as mentoring and leadership development are poised to innovate and meet the challenges of an ever-changing future. Soil survey and ecological inventory are highly specialized fields that frequently involve collaboration with partners. In order to work effectively with partners and pass on tacit knowledge from one generation to another, a culture of strong leadership is needed at all levels. Mentoring programs allow the workforce to connect between different offices, strengthen personal leadership, and perpetuate institutional knowledge. The Leadership, Diversity, and Recruitment Focus Team is actively pairing mentors with recently hired soil scientists, ecological site specialists, and supervisors in the NRCS Soils and Plant Sciences Division to help strengthen the soils and ecological site workforce by emphasizing personal leadership and technical skill development. This talk presents a brief overview of the mentoring program and an opportunity for NCSS members to serve as mentors or collaborators in the program.

1b-3: A quantitative soil-geomorphic framework for developing and mapping Ecological Site Groups

Presenting Author: Travis W. Nauman, U.S. Geological Survey, Southwest Biological Science Center
Co-authors: Michael C. Duniway, U.S. Geological Survey and Joel T. Humphries, Bureau of Land Management

Land management decisions require context to understand how a landscape may respond under different scenarios, circumstances, or alternatives. As rangeland ecologists’ understanding of non-linear ecological dynamics have evolved into state and transition model (STM) theory, more emphasis has been put into discerning and mapping the soil, geomorphology, and climate parameters that mediate ecological dynamics. The USDA Natural Resources Conservation Service (NRCS) Ecological Site Description (ESD) inventory has become the foremost system in classifying lands using STMs. However, completing an exhaustive inventory of ESDs in the USA has proved challenging, and there has been criticism of both the inconsistent level of detail in areas completed and the ability to objectively support some assertions made in exiting ESDs. To address these issues, this study looks at quantitative approaches to generalizing ecological site concepts based on unifying underlying soil, geomorphology, and climate drivers. Using existing ESD and vegetation monitoring data, a simple hierarchical soil geomorphic unit (SGU) framework based on topographic mediation of moisture, soil salinity, soil depth, slope, rock content, and soil texture can represent much of the ecological dynamics catalogued in ESDs. Analysis of production data, STMs, and regional monitoring data show that newly mapped SGUs represent more variation than commonly used climate parameters. An optimized combination of SGUs with climate zones resulted in an ecological site group framework that condensed over 826 unique ESDs at various stages of completeness in the regional soil survey down to 35 groups mappable using digital soil mapping workflows.

1b-4: Red spruce (Picea rubens) ecological states and restoration pathways quantified through soil organic carbon

Presenting Author: James E. Leonard, West Virginia University
Co-author: James A. Thompson, West Virginia University

Ecological site descriptions (ESD) are an important tool used to restore landscapes that have been impacted by disturbance by providing detailed management prescriptions specific to the ecological site (ES) in question. To date, two red spruce (Picea rubens) ES have been approved for use in the Eastern Allegheny Plateau and Mountains, helping organizations like the Central Appalachian Spruce Restoration Initiative guide restoration management in West Virginia. High elevation red spruce forests have the capacity to sequester significant quantities of soil organic carbon (SOC), and, as a result, influence other dynamic soil properties (DSP) and ecosystem services stemming from soils. Research associating SOC stocks and forest ESD is minimal. Studies have analyzed how SOC can benefit ecosystem services, yet none seek to compare SOC stocks across multiple ecological states to address both management outcomes and restoration pathways that could potentially increase SOC sequestration while restoring impaired ecosystem services. Here, preliminary SOC stock data and O horizon thickness is compared among ecological states of both the Spodic Shale Upland Conifer Forest and Spodic Intergrade Shale Upland Hardwood and Conifer Forest ES. Differences between average SOC stocks for ecological states can help land managers to understand possible future changes to DSP and ecosystems services influenced by SOC when transitioning between alternative ecological states or restoring to the reference state condition.

1b-5: Estimation of soil organic matter with color in Ecological Site Descriptions of Wisconsin

Presenting Author: Bryant C. Scharenbroch, University of Wisconsin–Stevens Point, College of Natural Resources
Co-authors: Ella Aspenson, Krista Bryan, Jacob Buettner, Dan Connolly, Hunter Lemler, Trace Miller, Jacob R. Prater, Emma Schmidt, Teresa Wolf, and Emily Yulga, University of Wisconsin–Stevens Point, College of Natural Resources

Soil organic matter (SOM) is a dynamic soil property and a key attribute in many soil interpretations. Accurate and practical in-field assessments of SOM are useful for soil survey, health, management, conservation, and many other purposes. Soil color may be used to estimate SOM, however more accurate predictions are likely if local relationships between color and SOM are used. Provisional Ecological Site Descriptions (PESD) were developed and sampled for ten Major Land Resource Areas (MLRA) in Wisconsin. Approximately 200 PESDs, 700 pedons, and 4,200 horizons were described and sampled. Loss on ignition SOM and color was determined on these soil samples. Moist and dry color was determined on ground and homogenized soil samples using a Konica Minolta chroma meter, a Nix Pro camera, a Munsell Capsure device, and the Munsell soil color book. Data analyses were conducted to develop predictive models for estimation of SOM with color. Regression analyses were conducted on the whole data set. To test if sample grouping improved accuracy of SOM prediction with color, the dataset was stratified by MLRA, PESD, and master soil horizons. Preliminary data analyses suggest Munsell soil color value is most related to SOM and this can be accurately estimated with a variety of methods. Grouping data by master horizon tended to result in stronger and more robust predictions of SOM with color. These findings are being utilized to develop practitioner tools for accurate in-field estimation of SOM.          

1b-6: Implementing the US Forest Service’s National Hierarchy to map Landtype Associations on the Superior National Forest, northern Minnesota, USA

Presenting Author: Gregory Nowacki, USDA Forest Service, Eastern Region
Co-authors: Katie Frerker, USDA Forest Service, Superior National Forest; Jeff Kroll, USDA Forest Service, Superior National Forest; Roger Risley, Natural Resources Conservation Service (retired); Ryan Toot, USDA Forest Service, State & Private Forestry; Jim Barott, USDA Forest Service, Superior National Forest (retired); Emily Engstrom, USDA Forest Service, Eastern Region; Casey McQuiston, USDA Forest Service, Shoshone National Forest; Kyle Steele, USDA Forest Service, Mark Twain National Forest.

The USDA Forest Service uses the National Hierarchical Framework of Ecological Units to classify, map, and describe 8 levels of ecosystems within a nested format. At the mid-level of the hierarchy are Landtype Associations―landscape-scale ecological units (10,000-100,000 acres) that possess similar landforms, parent materials, vegetation patterns, and soil catena. We employed this ecological classification and mapping system to delineate Landtype Associations on the Superior National Forest (northern Minnesota) and surrounding areas. In this region past glaciation has had a profound effect on the land, leaving distinct geomorphic features reflecting various depositional processes, such as silty/clayey glaciolacustrine basins, sandy outwash plains, and loamy till plains, drumlin fields and moraines. Where areas have been severely scoured by ice, most vividly expressed along the Canadian border, ecosystems have developed on a thin veneer of glacial drift overlying bedrock. Here, on bedrock-controlled landscapes, the surface terrain and soil mineralogy closely match that of the underlying bedrock. Since geomorphic surfaces largely control the arrangement and expression of ecosystems at this scale, they were used as the primary basis for Landtype Associations. The maritime effect (warmer, wetter conditions) of Lake Superior was also a defining factor, overriding the cold continental climate along the lakeshore. By using this approach, an impressive array of physical and biological characteristics was captured simultaneously; landscape-scale characteristics that are important to land managers and researchers alike, including vegetation types, site productivity, soil properties, climate, and topographic features.

1b-7: Predicting Controls on Soil Organic Carbon Storage and Loss in State-and-Transition Models in Critical Ecological Sites across Tennessee

Presenting Author: Sean Schaeffer, University of Tennessee

(Abstract pending)

1b-8: Connecting the Dots: Dynamic Soil Properties and Ecological Sites

Presenting Authors: Erin Hourihan, Ecological Site Data Quality Specialist and Colin Walden, Ecological Site Specialist, USDA-NRCS, Soil and Plant Science Division

DSP projects document soil functions including nutrient cycling, water storage, and biodiversity all of which are important considerations for describing ecological states and community phases. However, the relationship between community composition and soil properties in the development of alternative stable states is not well understood. Ecological site sciences have not developed a set of systematic protocols for recognizing differences in soil properties and processes among ecological states. Describing the relationship between a continuum of soil properties, soil processes, and vegetation, stresses the need for DSP projects be accompanied by vegetation properties that permit inferences to be made regarding ecological processes. The relationship between the vegetation properties collected for these projects and the ecological process must be clearly defined and relatively insensitive to daily or seasonal fluctuations in moisture, temperature, or light. Methods like gap intercept and line-point intercept produce vegetation properties that are useful indicators of ecological function, including rates of infiltration, runoff, germination and survival. All of which drive ecological transitions and community phase changes; however, these attributes have not been adequately described at ecological site scale with alternative stable states, community phases and ecological thresholds in mind. Well-developed datasets that include specific vegetation attributes will allow these indicators of ecological function (canopy cover, litter cover, basal cover) to be connected to specific ecosystem attributes and contribute to STMs that describe the soil system in addition to the vegetation community.

2a: Soil Health

2a-1: Auburn University Soil Health Research

Presenting Author: Audrey Gamble, Auburn University

(Abstract pending)

Soil Health Indicator Reference Conditions for Soil Survey
Presenting Author: Skye Wills, USDA-NRCS, National Soil Survey Center

In order to use soil health indicators as a tool for land management, benchmark and reference values are needed for different kinds of soils. Traditional soil survey maps provide information on properties that do not change with time (i.e.: inherent soil characteristics). However, users are interested in more dynamic properties that may change as influenced by land use and management. In order to bridge soil health assessment and soil survey products, the concepts of state and transition models can be used to relate vegetative communities and land management condition to dynamic soil properties and soil health metrics. The Soil and Plant Science Division of NRCS is coordinating a project called Dynamic Soil Properties for Soil Health (#DSP4SH) as part of a broader Science of Soil Health Initiative. The project consists of sixteen individual cooperative agreements with CESU universities in CA, KS, IL, MN, NC, OR, TX, WA, WI, HI, AZ, ND, MO, AL, CT and MD that use common protocols and procedures to evaluate proposed soil health metrics The local MLRA Soil Survey Offices also collected characterization pedons in each soil and land use evaluated. The initial data analysis indicates that cooperator data can be used to ascribe reference values for soil health indicators including aggregate stability, active carbon, and soil enzymatic activity.  Properties that vary with season and weather conditions such as respiration and microbial community structure (assessed with the phospholipid fatty acids (PLFA) method) are more difficult to quantify. Initial results indicate that the magnitude of biological indicators differ largely by region and soil evaluated, management systems impact biological parameters within soils, and sample locations within individual fields also vary significantly.  Physical properties tend to vary by soil texture and vegetation type. Next steps include comparing traditional characterization pedons with samples collected by cooperators and measured at the Kellogg Soil Survey Laboratory.

2a-3: Hung out to dry: how the reliance on metrics developed for soil health assessment in temperate systems may lead to erroneous management advice in arid systems

Presenting Author: Dr. Kirsten Ball, University of Arizona
Co-authors: Dr. Joseph Blankinship and Sam Rathke, University of Arizona

Despite the mounting popularity of soil health research worldwide, arid-system soil health is one of the most acutely underdeveloped area of knowledge in applied soil health research. Most scientific knowledge around the quantification and management of soil health has been developed in temperate systems, and as a result there is a limited understanding of how to manage alkaline, moisture deficient (and often saline) soils. Accordingly, many of the current proposed metrics for soil health assessment may be inappropriate for arid systems-particularly those which are pH dependent, of a biological nature, or which rely upon the presence of a significant amount of organic matter to induce a chemical reaction. Further, common arid-system soil metrics like exchangeable cations and soil inorganic carbon content, known to strongly influence crop health and soil organic matter accumulation are not usually included in soil health assessment. This presentation discusses the potential pitfalls of taking a temperate approach to arid-system soil health assessment, by presenting compelling data to suggest that important soil health metrics like soil organic carbon are strongly influenced by carbonates, and that without quantifying them we may be making erroneous recommendations for managing arid croplands.

Soil Health Status Baseline Development Using Soil pH Monitoring of Soil and Water in Pits on the 90-Acre Property of Prairie View A&M University
Presenting Author: Richard W. Griffin, Ph.D., Prairie View A&M University
Co-authors: Edward K. Timms and Armondo Waters, Prairie View A&M University

Soil reaction or pH is a primary indicator for soil health, since it provides a reflection of growth potential for beneficial plants, such as row crops, pasture, shrubs and trees. The project objective was to measure pH of soil and water conditions that can be used to infer knowledge about soil health status and growth potential for beneficial plant and weed species. Soil and Water pH were measured using a portable probe that was calibrated using the 3-point standardization method. Soil samples were collected from 7 locations in 3 soil units and at 2 depths to assess pH conditions in topsoil and subsoils. Triplicate (3) samples of soil and water were used to produce mean and standard deviation values for statistical analyses. Soil pH data from 6 Soil Pits and 1 Pond Dam samples provided spatial variability information that assisted overall project goals of baseline assessment. Water pH data from 3 Soil Pits and 1 Pond adjacent to property were monitored to compare with soil pH values surrounding same sites. Soil pH values at Soil Pits 1, 3, and 5 were significantly lower (p<0.05) than Water pH values as well as at Pond Dam compared to Pond Adjacent to 90-acre property. This observation indicated that acidity of soil was not significantly lowering pH of water in ponds either on or adjacent to property. We recommend testing soils on adjacent landscape positions along with a focus on adjacent upslope properties that receive inputs that may increase pH levels on this property.

2a-5: Evaluating Soil Health Indicators in West Texas Soils

Presenting Author: Katie Lewis, Texas A&M University

(Abstract pending)

2a-6: Evaluation of Soil Health Metrics in the Williamette Valley Region

Presenting Author: Regina O’Kelley, Oregon State University
Co-authors: Lucas Norton-Guerra, Cedric Pimont, and David D. Myrold, Department of Crop and Soil Science, Oregon State University

Soil organic carbon is an important component of soil health, but measuring the total pool of organic carbon has drawbacks as a soil health indicator. Some of these obstacles may be alleviated by measuring permanganate oxidizable carbon (POXC), a popular quick and inexpensive index for estimating the most easily oxidized organic carbon in the soil. Also called active carbon, POXC is often interpreted as the most readily available form of carbon in the soil, and has been reported to be sensitive to management differences.

In the Willamette Valley, Oregon, Dynamic Soil Properties for Soil Health study, we measured POXC, soil organic carbon (SOC) and carbon mineralization rates on six management systems across two soil series. We sampled soil from no-till and conventionally tilled perennial grass seed fields and hazelnut orchards on the Woodburn series (Aquultic Argixerolls) and from vineyards, Christmas Tree farms and second-growth timber stands on the Jory series (Palehumults).

Although POXC was correlated to SOC and carbon mineralization rates, there is limited evidence that it represents a particularly active form of carbon. Carbon mineralization was better explained by SOC than by POXC, and had no relationship with POXC normalized by SOC. POXC was more sensitive to depth than to management type in both soils. Our results do not negate the benefit of POXC as a practical indicator for microbial C resources in the soil, however, we recommend that users exercise caution when interpreting the indicator as a particular fraction of carbon.

2a-7: Soil Health Monitoring Network – On-site Soil Health Data Collection

Presenting Author: Garrett Liles, California State University-Chico

(Abstract pending)

2a-8: Comparison of Soil Health Metrics in Glacial-Till Soils with Various Management Intensity

Presenting Author: Huijie Gan, University of Connecticut
Co-authors: Wayne Roper, Tom Morris, and Karl Guillard, Department of Plant Science and Landscape Architecture, University of Connecticut

There are many efforts to determine which soil properties are most representative of soil health (SH) status of managed lands. However, many questions remain regarding selection of "a minimal set" of soil measurements that best indicate SH status and detect changes in response to SH-promoting practices. In this study, we compared a standard set of SH indicators from six managed systems in Connecticut: conventional-till corn, no-till corn, lawn, hay, managed forest, and unmanaged forest. As management intensity increased, measurements of field infiltration, soil total carbon (C), total nitrogen (N), and microbial respiration all showed declining trends. Cornfields had significantly lower measurement values compared with grassy habitats and forests (P < 0.05). Cornfields with no-till practices exhibited higher soil total C, total N, and steady-state infiltration than the conventional-till fields. In comparison, microbial respiration was highly site-specific and did not differ between no-till and conventional-till cornfields. In addition, soil total C had the strongest correlations with other SH indicators of field infiltration (Spearman's rank correlation ρ = 0.60), microbial respiration (ρ = 0.84), and soil N (ρ = 0.97), suggesting that soil total C has a major influence on SH status. We will also explore whether other soil health metrics (soil labile C, enzyme activities, and microbial PLFA composition) would provide additional understanding of SH status across the management intensity gradient.

2b: Soil Survey

2b-1: Deadly Dust on Arizona Highways: Developing an Improved Dust Risk Index Based on Soil Stabilization Mechanisms and Ecological Site Descriptions

Presenting Author: Joseph Blankinship, University of Arizona, Department of Environmental Science
Co-authors: Samuel Rathke and Craig Rasmussen, University of Arizona, Department of Environmental Science; Jason Field, University of Arizona, School of Natural Resources & Environment; Eduardo Sáez, University of Arizona, Department of Chemical & Environmental Engineering

Declining soil health continues to increase dust pollution in the Desert Southwest. Dust clouds from barren lands near highways impair vision leading to fatal automobile accidents and other human health effects. With growing concerns of fallow/abandoned croplands, drought, and desertification, there is an urgent need to determine which desert soils are most at risk for producing dust, especially along major highways. Rather than the current Wind Erodibility Index that is not based on actual measurements of dust emission, stakeholders need a ground-truthed Dust Risk Index based on the latest scientific understanding of how physical, chemical, and biological factors stabilize desert soils with ease of spatial scaling through the Ecological Site Description (ESD) framework. We are using a network of passive dust samplers at a known dust hot spot along a two-mile stretch of Interstate-10 between Phoenix and Tucson, Arizona to compare existing indices of wind erodibility (texture, dry-aggregate stability, calcium carbonate) to new predictors that span soil properties that are physical (surface roughness, wet-aggregate stability), chemical (cations, organic residues), and biological (biocrusts, vegetation). The combination of predictors that best predicts actual dust emission will be used to develop a Dust Risk Index that is now being validated at four additional dusty highway locations in different ESDs across Arizona.

2b-2: Fine scale mapping of soil organic carbon in tidal marshes using lidar-derived geomorphic relationships

Presenting Author: Brian Yellen, University of Massachusetts Amherst
Co-authors: Bonnie Turek, Qian Yu, Jonathan Woodruff, Hannah Baranes, Timothy Cook, Justin Richardson, Konstantinos Andreadis, University of Massachusetts Amherst

Tidal marshes play an outsized role in sequestering carbon due to their high productivity and continual accretion in response to sea level rise. Estimates of tidal marsh carbon stocks and sequestration rates in subaqueous soils vary widely, in part because soil organic content differs considerably within and between marsh sites. Traditional tools in soil mapping, such as slope, parent material, and floral species assemblage fail to capture tidal marsh soil heterogeneity as these settings are flat, largely comprised of organogenic histosols, and have relatively few species present. In tidal marsh settings, soils build vertically to keep pace with sea level rise and autocompaction via the accumulation of mineral and organic material. With increasing distance from tidal creeks, which supply sediment, mineral sediment supply decreases and soil organic content increases accordingly. 

Our group has developed methodologies to map soil organic carbon at the meter scale for tidal marshes in the northeast using detailed maps of tidal creek networks. Lidar DEM’s were used to map tidal creek networks in order to calculate distances from creeks. Distance from tidal creeks for any given location in the marsh is the weighted average of the distances from different size classes of creeks, defined by stream order. We collected field samples with fixed volumes to assess soil organic content at several tidal marshes in Massachusetts that capture a range of geomorphic and tidal conditions. Samples were collected in transects perpendicular to major tidal creeks. Our weighted creek distance metric was regressed against soil organic content of field samples in order to map carbon at the same resolution of lidar datasets - in this case, 1 m pixels. We will present our methodology and compare predicted values of soil organic matter to those measured in the field.

2b-3: SSURGO Glitches – Where, Why, and How to Fix Them

Presenting Author: Dylan Beaudette, USDA-NRCS, National Soil Survey Center
Co-authors: Zamir Libohova, Stephen Roecker, Charles Ferguson, Drew Kinney, and Skye Wills, USDA-NRCS, National Soil Survey Center

It is common knowledge among soil scientists and power users of our data that thematic maps created from the Soil Survey Geographic Database (SSURGO) do not always “agree” at political boundaries. While most of the discrepancies have been rectified by the Soil Data Join Re-correlation (SDJR) initiative, there remain some obvious “glitches”, especially regarding soil properties such as organic carbon and rock fragment volume. Discrepancies tend to be worse at depth. Gridded soil property, class, and condition maps (typically derived from gSSURGO or gNATSGO) are critical inputs to research questions that span large geographic extents. The high visibility of “glitches” in these products often encourages users to rely on other, non-authoritative, soil data products. Typical alternatives include derivatives of gSSURGO/gNATSGO, or predictions generated by interpolation via statistical models fit at points such as SoilGrids. While these alternatives appear to be free of edge-matching “glitches”, much of the fine-scale detail in known spatial patterns (especially abrupt transitions related to lithologic or landform breaks) is lost. Since the start of SDJR initiative, the tools and means to “fix” these glitches for continuous and better harmonized soil data have evolved. Soil scientists at the Soil and Plant Science Division (SPSD) who understand the root cause of these glitches are better positioned to address them and generate seamless soil products. We present some new tools and processes to identify the “where” and “why” of edge-matching errors in SSURGO, and suggestions on how to fix them.

2b-4: Pattern Mining Soil Systems in North Carolina

Presenting Author: Hunter Winsor, Doctoral Student, Department of Plant and Environmental Sciences, New Mexico State University
Co-author: Colby W. Brungard, Department of Plant and Environmental Sciences, New Mexico State University

Soil systems are areas of spatially repeating patterns of soils found from stream center to interstream divide. Soil systems in North Carolina were descriptively mapped by soil scientists based on their expert knowledge of the area (Daniels et al, 1984,1999).  This soil systems map communicates the conceptual soil-landscape models (i.e., block diagrams) of soil surveyors by bounding areas to the extent of where a soil block diagram applies.  We applied the concept of soil systems to soil survey data (SSURGO) in North Carolina and used data mining techniques to quantitatively mine conceptual soil-landscape models from soil maps. An unsupervised data mining approach (co-occurrence clustering) was used to extract areas of repeating patterns of soils (i.e., soil systems) from the soil maps. Then, sequential association rule mining was used on each area to find patterns of soils for each soil system. To understand how expert knowledge differed from the results of the data mining, we compared the descriptions of the soil systems from Daniels et al (1984,1999) to the sequential patterns mined. This process can be used to quantitatively mine soil-landscape conceptual models, and the associated area to which those conceptual models apply, across the United States wherever SSURGO data exists. 

2b-5: Identification and Determination of Spatial Distribution of Acid Sulfate Soils in Puerto Rico and U.S. Virgin Islands

Presenting Author: Cabezudo-Vázquez, O.H., Graduate student at University of Puerto Rico–Mayagüez
Co-authors: Dr. Luis Perez Alegria and Dr. Raul Macchiavelli, University of Puerto Rico–Mayagüez and Dr. Gustavo Martínez, UPR Agricultural Experimental Station

Oxidation of soil pyrite due to hydrologic disturbances is responsible for severe soil acidification and the production of noxious greenhouse gases. Coastal Lowland acid sulfate soils (CLASS) are an environmental hazard worldwide due to the associated high acidity (pH < 4), and threats posed by runoff discharge. We use digital soil mapping (DSM) techniques to device a more effective, and least time and resource consuming alternative than traditional field sampling schemes for the determination of the probability of occurrence of CLASS in Puerto Rico and U.S. Virgin Islands.  Soil samples collected from coastal areas in Puerto Rico were classified based on anaerobic incubated pH results after 7 weeks. Out of 58 samples at a depth of 0-12 in, 17 reached pHfinal < 5, and after incubation hence were labeled as positive for CLASS. Sulfur content was analyzed for positive samples resulting in a 0.61 inverse correlation. Two separate setups of a predictive model (Model A and Model B) were completed using DSM techniques and a Random Forest (RF) hyper-tuning parameters classifier. Sentinel 2 sensor indices and SCORPAN model data were used as covariates for the region of interest, established by Holocene correlated data with sulfur deposition. Model A resulted best with an out of bag error of 19%, test error of 23%, and a spatial prediction for CLASS in PR of 40.42 mi2 with a mean probability of 65%. Resulting CLASS mapping shows the first assessment of the technique in the Caribbean region.

2b-6: Using RStudio for Enhanced Digital Soil Mapping

Presenting Author: Tyson Hart, USDA-NRCS, South Central Soil Survey Region
Co-author: Sara Russell, USDA-NRCS, South Central Soil Survey Region

Digital soil mapping (DSM) has been a focus of soil scientists in recent years as a means of using predictive modeling to provide improved soil maps. The Nacogdoches Soil Survey Office (SSO) in Texas started using DSM techniques to create soil map layers and data for the southern portion of the Western Coastal Plain, Major Land Resource Area (MLRA) 133B, in the spring of 2020. The goal was to use DSM to create a more comprehensive map that would be useful to field offices and the general public. The soil scientists used ArcSIE as a platform to create new algorithms for RStudio. The process uses multiple covariates created in spatial platforms using high-resolution LiDAR (5-meter). The resulting codes allowed for larger datasets to be analyzed while enhancing the computing time. The project is currently undergoing ground truthing to test the validity of the digital soil maps.

2b-7: Using DSM Methods for Initial Soil Survey–Altus Air Force Base

Presenting Author: Tyson Morley, USDA-NRCS, South Central Soil Survey Region
Co-author: Tyler Kemph, USDA-NRCS, South Central Soil Survey Region

The Soils 2026 Initiative is working to provide comprehensive soil and ecological site inventory data for the entire US.  Portions of the Central Red Rolling Plains, Eastern Part (MLRA 78C) in Southwestern Oklahoma are currently in the beginning stages of using Digital Soil Mapping (DSM) techniques to fill incomplete areas in the database at Altus Air Force Base. 

An overview of MLRA 78C and the landscapes of southwest Oklahoma and the history of the Altus Air Force Base and host aircraft from the past and present are detailed.  Rationale for current incomplete status of Altus Air Force Base is also presented.  The use of Digital Soil Modeling techniques to complete both a SSURGO vector soils survey along with a raster-based soil survey are described and explained.  The goal of the project is to provide high quality comprehensive data products to the Web Soil Survey database.

2b-8: The Loxley and Denham Springs Soil Survey Offices Activities

Presenting Author: Jerome Langlinais, USDA-NRCS, Southeast Soil Survey Region
Co-authors: Sandy Page and Rachael Heisey, USDA-NRCS, Southeast Soil Survey Region

Low Activity Clays and Kandic Soils in SW Alabama, NW Florida and SE Mississippi encompass parts of 7-LOX, 7-MER, 7-TAL, and 7-TUS soil survey offices. These concerns are from the update of Kandic soils in older soil surveys in the region. There is presently a list of soil series updated to kandic taxonomic great groups and lack sufficient data to prove or disprove its existence or range. Additional challenges are to update map units in landscapes consisting of both non-kandic and kandic soils. Studies are presently underway to document the western range of Kandic soils and other dynamic soil properties.

Coastal Zone Soil Survey continues to engage in research of estuarine environments. Soil cores to 2 meters and laboratory data for the soil environments in the Mobile and Pascagoula River Systems along with degrading marsh systems exemplified by those found at Grand Bay NERR provide much needed soil and vegetation information.  Preliminary results suggest the northern Gulf Coast shore environment between the Florida panhandle and Mississippi River Delta is unique and characterization of baseline conditions is essential.

Additional marsh efforts are to collect soil core data at select Coastwide Reference Monitoring System (CRMS) sites among marsh ecosystems in Louisiana. The are 390 CRMS sites with variables including vegetation records; frequent water elevations, salinity, and temperature; surface elevation changes; and soil properties to 24 cm or so. Efforts are underway to collect soil core data to 200cm and complement existing soils data.  Characterized among the nation’s most fragile and valuable ecosystems, the coastal areas of the northern gulf coast need reliable soil survey data which will benefit a significant number of non-traditional USDA-NRCS customers.