Soil Survey Geographic Database Research Articles

Management zone classification for variable-rate soil residual herbicide applications

AbstractThe use of soil residual herbicides, along with other practices that diversify weed management strategies, have been recommended to improve weed management and deter the progression of herbicide resistance. Although soil characteristics influence recommended application rates for these herbicides, the common practice is to apply a uniform dose of soil residual herbicides across fields with variable soil characteristics. Mapping fields for soil characteristics that dictate the optimal dose of soil residual herbicides could improve the efficiency and effectiveness of these herbicides, as well as improve environmental stewardship. The objectives of this research were to develop and quantify the accuracy of management zone classifications for variable-rate residual herbicide applications using multiple soil data sources and soil sampling intensities. The maps were created from soil data that included (i) Soil Survey Geographic database (SSURGO), (ii) soil samples (SS), (iii) soil samples regressed onto soil electrical conductivity (EC) measurements (SSEC), (iv) soil samples with organic matter (OM) data from SmartFirmer® (SF) sensors (SSSF), and (v) soil samples regressed onto EC measurements plus OM data from SmartFirmer® sensor (SSECSF). A modified Monte Carlo cross validation method was used on ten commercial Indiana fields to generate 36,000 maps across all sources of spatial soil data, sampling density, and three representative herbicides (pyroxasulfone, s-metolachlor, and metribuzin). Maps developed from SSEC data were most frequently ranked with the highest management zone classification accuracy compared to maps developed from SS data. However, SS and SSEC maps concurrently had the highest management zone classification accuracy of 34% among maps developed across all fields, herbicides, and sampling intensities. One soil sample per hectare was the most reliable sampling intensity to generate herbicide application management zones compared to one soil sample for every 2 or 4 hectares. In conclusion, soil sampling with ECa data should be used for defining the management zones for variable-rate (VR) residual herbicide applications.

Geologic Map of the Bedford 30- x 60-minute Quadrangle, Indiana

The Geologic Map of the Bedford 30- x 60-minute Quadrangle shows the Mississippian and lower Pennsylvanian bedrock units and the overlying Pleistocene and Holocene deposits distributed over eight counties. The mapped area includes five physiographic divisions of the Southern Hills and Lowlands Region in south-central Indiana. Bedrock units and unconsolidated deposits were characterized by outcrop field observations, and new and archived borehole data. Bedrock data was consolidated into a single mapping database, which was then processed using gridding and contouring tools in ArcGIS Pro, resulting in rock formation contact lines, mappable at 1:100,000 scale. Additional information used to delineate contacts for unconsolidated deposits was based upon Indiana Department of Natural Resources water well database and soils data from the Soil Survey Geographic (SSURGO) database. Light detection and ranging (LiDAR) digital elevation models were also used to identify landforms and refine contacts between surficial units. The mapped bedrock units, from east to west, range in age from the Borden Group (lower Mississippian) to the Raccoon Creek Group (Lower Pennsylvanian) with the strike oriented generally from northwest to southeast. Quaternary deposits are found in river and stream valleys and filling the Wabash and Scottsburg lowlands with glacial deposits. Quaternary sediments were mostly deposited during the last two major advances of the Laurentide Ice Sheet into south-central Indiana. Brief summaries of all mapped bedrock units and unconsolidated deposits are listed on the map sheet, with more detailed descriptions in the accompanying pamphlet. Bedrock survey drill hole data points are also displayed on the map with corresponding links to expanded drill hole information for each of the survey drill holes listed in the pamphlet. In addition to the map and pamphlet, a composite spatial data set that conforms to the standardized database schema known as GeMS (Geologic Map Schema) is also available for download from this site. Both ESRI-licensed geodatabase and open shapefile versions of the spatial data set are available for download. Metadata records associated with each element within the spatial data set contain detailed descriptions of their purpose, constituent entities, and attributes. This geologic map was funded in part through the STATEMAP program supported by the U.S. Geological Survey under Cooperative Agreement No. G21AC10646.

Significant opportunities for tree crop expansion on marginal lands in the Midwest, USA

AbstractIntroductionTree crop orchards can help diversify the monocropping practices in agricultural lands such as the Midwestern region of the United States which is currently dominated by corn‐soybean rotation. These crops can not only expand farm income but also help stabilize soil, purify water, and provide other ecosystem services. Despite these benefits, tree crops as agroforestry systems are not widely established in the region. Identifying marginal land that is more suitable for tree crops over row crops can help the growers and the environment.Materials and MethodsUsing the case of Missouri, USA, this study mapped marginal land for nut‐producing tree crops using a three‐step process. The first step includes the identification of marginal land suitable for a wide variety of tree crops. The second step involved GIS‐based multicriteria analysis of biophysically suitable areas for three commercially viable tree species, that is, eastern black walnut, pecan, and Chinese chestnut. The final step maps the marginal land for tree crops based on their suitability to grow under different environmental vulnerabilities, mainly flooding, erosion, and sloped conditions. The Gridded Soil Survey Geographic Database developed by the United States Department of Agriculture provided information on soil biophysical and environmental characteristics.ResultsThe spatial analysis showed that Missouri has around 16,000 sq. km of land suitable for Black walnut, followed by 10,801 sq. km and 4587 sq. km for pecan and Chinese chestnut, respectively. The study found very few tree crop orchards on these marginal lands.ConclusionThere are ample opportunities to expand tree crops on marginal cropland. Converting these lands to tree crops could provide communities with economic, human health, and environmental benefits. This multistep methodology could serve as a screening tool to identify tree‐cropping areas for environmental improvement and rural America's economic revitalization.

CN-N: A Python-based ArcGIS Tool for Generating SCS Curve Number and Manning’s Roughness

Water resources engineers and geospatial analysts often face the challenge of spatially estimating parameters such as the Soil Conservation Service (SCS) Curve Number (CN) and Manning’s roughness number (n), which are critical for predicting runoff and streamflow in hydrologic studies. Addressing the above challenge, this paper presents an innovative ArcMap tool developed using Python. This tool streamlines the SCS-CN and Manning’s n spatial calculations and is designed to handle large datasets, even at the scale of the entire US. Additionally, it offers the unique capability of geoprocessing mixed soil types and seamlessly integrating data if the watershed spans over different states. Our tool automates the integration of land cover data, hydrologic soil group data, and hydrologic boundaries. The tool reads watershed boundaries and uses the National Land Cover Database (NLCD) and the Gridded Soil Survey Geographic Database (gSSURGO) to develop SCS-CN and Manning’s n spatial layers. The tool also offers users the unique flexibility to add any desired values for CN or Manning’s n in the form of a so-called lookup table, which is a great help with the iterative process of calibrating hydrologic or hydraulic models. Our tool addressed one of the major limitations of its predecessors, acknowledging the existence of mixed hydrologic soil groups, e.g., B/C or C/D, and allowing for user adjustments to address hydrologic or hydraulic models’ calibration needs. The tool was developed with a flexible framework to incorporate additional spatial parameters soon, such as the spatial green-ampt parameters. With a user-friendly interface and integration capabilities, the tool is invaluable for hydrologic and hydraulic studies at local, regional, and global scales.

Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters

AbstractAccurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

Creating a distribution model of three crayfish species of the genus Faxonius (Decapoda, Cambaridae) in Michigan streams using publicly accessible data

Abstract This research seeks to create a predictive model of habitat suitability for use in determining waterbodies vulnerable to introduced species within the state of Michigan. Three members of the genus Faxonius (Decapoda, Cambaridae) were selected as test taxa for the model due to several species’ propensity for significantly altering the ecosystems they inhabit in Michigan and elsewhere. Michigan State University and the Michigan Department of Natural Resources (MDNR) conducted extensive field surveys of crayfish species assemblages across 461 stream sites from 2014-2016. This project compares these field data to data from publicly available national datasets with the purpose of revealing ecosystems that are vulnerable to population expansion. We identify patterns in Faxonius habitat at local (100 acres) and landscape (1000 acres) scales by associating crayfish occurrences throughout Michigan with variables characterizing landscape conditions thought to be important factors affecting their spread, growth, and survival. An Artificial Neural Network (ANN) model using variables from Soil Survey Geographic Database (SSURGO) and National Land Cover Database (NLCD) successfully identified stream sites and watersheds in Michigan vulnerable to range expansion by Faxonius rusticus (Girard, 1852), Faxonius propinquus (Girard, 1852), and/or Faxonius virilis (Hagen, 1870). We found several habitat variables that influence our predictions. The most important variable describing F. rusticus presence was local (100-acre) scale Open Water land cover class, whereas for F. propinquus, the high-intensity developed land cover class at the local scale was the most important, while it was the shrubland land cover class at the local scale for F. virilis. This research demonstrates a powerful method to identify locations using remote sensing data that can be prioritized for conservation efforts that are threatened by invasive crayfish species.

How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the U.S. Midwest?

Cropland carbon budget depicts the amount of carbon flowing in and out of agroecosystems and the changes in carbon stocks of soil and living biomass during the same period. Soil carbon credit is the additional change in soil carbon stock under certain farming practices compared with the business-as-usual practices. Accurately calculating cropland carbon budget and soil carbon credit is critical to assessing climate change mitigation potential in agroecosystems. The calculation of cropland carbon budget and soil carbon credit is sensitive to local soil and climatic conditions, especially initial soil organic carbon (SOC) stock, which is determined by both SOC concentration (SOC%) and bulk density (Bulk_Density). SOC stock data are either from soil sampling or gridded public survey data. In agroecosystem models, SOC stock data are a key model input for quantifying cropland carbon budget and soil carbon credit. However, various types and degrees of uncertainties exist in SOC stock datasets, which propagate to the quantification of SOC stock change. In particular, a large discrepancy is found in two widely used SOC stock datasets — Rapid Carbon Assessment dataset (RaCA) and Gridded Soil Survey Geographic Database (gSSURGO) — in the U.S. Midwest, with a relative difference (quantified using Normalized Root Mean Square Error, NRMSE) of 48.0% for 0–30 cm SOC stock between the two datasets. It remains largely unclear how uncertainty in SOC stocks affects the calculation of cropland carbon budget and soil carbon credit. To address this question, we used a well-validated process-based agroecosystem model, ecosys, to assess the impacts of SOC stock uncertainty on carbon budget and soil carbon credit calculation in the U.S. Midwestern corn-soybean rotation systems. Our results reveal the following findings: (1) A sizable discrepancy exists in simulated cropland carbon budget between using gSSURGO and using RaCA for their SOC% and Bulk_Density as model inputs, with a Pearson correlation coefficient (r) of only 0.4 for simulated change of SOC stock (ΔSOC) using these two different soil datasets. (2) Simulated cropland carbon budget components were more sensitive to initial SOC% than to Bulk_Density. For example, the upper and lower quartiles of multi-year averaged ΔSOC were −29.8 and 4.8 gC/m2/year for the selected counties respectively, with an uncertainty of 13.7 and 0.7 gC/m2/year induced by uncertainties in initial SOC% and Bulk_Density, respectively. (3) Both simulated ΔSOC and its uncertainty were negatively correlated with initial SOC%, whereas ΔSOC was negatively correlated with air temperature, and ΔSOC uncertainty was positively correlated with air temperature. (4) The uncertainty of calculated soil carbon credits was much smaller compared with the uncertainty of calculated absolute carbon budgets assuming the same SOC stock uncertainty level in the inputs. Specifically, in our assessment comparing planting cover crops vs no cover crop, the uncertainty of calculated soil carbon credits induced by initial SOC% uncertainty was less than 4% (relative to the quantified value of the soil carbon credits) for 90% of the cases. Our analysis highlights that high accuracy measurement of SOC% as inputs is needed for the calculation of cropland carbon budgets; however, soil carbon credit quantification is much less sensitive to the initial SOC% inputs, and the current publicly available soil datasets (e.g., gSSURGO) are largely suitable for the calculation of soil carbon credits.

Estimating land cover-based soil organic carbon to support decarbonization and climate resilience planning in Massachusetts

Land management and land cover change exert a strong influence on soil organic carbon (SOC) storage. As scientific, political, and business communities increase their awareness of the essential roles SOC plays in climate regulation and ecosystem functions, efforts to quantify the impacts of land use and management on SOC have increased rapidly. Existing methods of estimating SOC stocks from widely available data do not account for land cover, and are therefore of limited usefulness in understanding the impacts of past and future land use change. This project explores a method of linking land cover to SOC using data from public datasets and the scientific literature, to provide an SOC Inventory for Massachusetts, and compares the results to those derived from a common baseline approach. Our method derives average land cover SOC values by combining data from the USDA-NRCS Rapid Carbon Assessment and the National Cooperative Soil Characterization Database with values from a meta-analysis of scientific literature. These are applied to the total area of the 20 most abundant land cover classes of Massachusetts. We compare this land cover-based approach with a baseline using SOC values found in the Soil Survey Geographic Database (SSURGO), applied to each soil map unit found within Massachusetts. Our approach produced an estimated stock of 481 million metric tons of SOC, 29% and 109 million metric tons greater than the SSURGO baseline. We use these estimates to explore the use of the land cover based SOC values to project the impacts of likely land cover change by 2050.

Financial exposure to environmental liabilities in Lake Huron drainage area farmlands: a GIS and hedonic pricing approach

PurposeThis paper aims to quantify the impact of environmental contamination on farmland valuation. It applies data fusion and hedonic pricing approaches to quantify the contribution of nitrogen and phosphorus loading on farmland sales transactions. It further suggests approaches to improve internalization of environmental cost in valuation approaches using shadow pricing. The work informs the field of environmental, social and governance (ESG) investing by fusing environmental data with financial transactions.Design/methodology/approachThis paper is an empirical study implementing hedonic pricing of farmland in the Lake Huron major drainage area. Data sources and fusion were derived from AcreValue, the United States Department of Agriculture's Gridded Soil Survey Geographic database (gSSURGO) and the United States Geological Survey's Spatially Referenced Regression on Watershed Attributes database (SPARROW).FindingsThe results suggest that environmental contamination has statistically significant positive determination power on farmland prices such that prices increase with contamination. Conventional metrics such as percentage of cultivated land in the parcel, root zone depth, whether the parcel is designated by the Natural Resource Conservation Service as prime farmland, and the size of the farmland parcel contribution to farmland value as well. The results indicate that environmental impacts are not accurately accounted for in farmland transactions.Research limitations/implicationsThis paper points to inaccurate valuation of environmental contamination in farmland value. While geocoding allowed for positioning of farmland sales transactions relative to modeled areas of contaminant loading in the Lake Huron drainage area, the interpretation indicates that value is driven by cultivation. Hence, generalization to other areas needs a cautious approach. Empirical testing across locations and drainage areas with diverse farmland features will serve to verify the modeled data used in this study.Practical implicationsThe lack of integration of externalities in land valuation has implications on lending and disclosure practices, as financial service providers increasingly seek to account for ESG risk on their loan books and broader investment portfolios. The impact of farmland accounting practices for contamination such as shadow pricing may impact land valuation based on future cash flows, and may serve to inform sustainability-linked lending practices to farm operations.Originality/valueThis is the first paper to fuse data from AcreValue, gSSURGO and SPARROW to discover the explanatory power of nutrient contamination in farmland value in the Lake Huron major drainage area.

Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.

Influence of Land Use and Topographic Factors on Soil Organic Carbon Stocks and Their Spatial and Vertical Distribution

Soil organic carbon (SOC) plays a critical role in major ecosystem processes, agriculture, and climate mitigation. Accurate SOC predictions are challenging due to natural variation, as well as variation in data sources, sampling design, and modeling approaches. The goal of this study was to (i) understand SOC stock distribution due to land use (restored prairie grass—PG; lawn grass—LG; and forest—F), and local topography, and (ii) assess the scalability of SOC stock predictions from the study site in North Carolina (Lat: 36°7′ N; Longitude: 80°16′ W) to the geographic extension of the Fairview soil series based on the US Soil Survey Geographic (gSSURGO) database. Overall, LG had the highest SOC stock (82 Mg ha−1) followed by PG (79 Mg ha−1) and forest (73.1 Mg ha−1). SOC stock decreased with the depth for LG and PG, which had about 60% concentrated on the surface horizon (0–23 cm), while forest had only 40%. The differences between measured SOC stocks and those estimated by gSSURGO and modeled based on land use for the Fairview series extent were comparable. However, subtracting maps of the uncertainty predictions based on the 90% confidence interval (CI) derived from the measured values and estimated gSSURGO upper and lower values (an estimated CI) resulted in a range from −17 to 41 Mg ha−1 which, when valued monetarily, varied from USD 33 million to USD 824 million for the Fairview soil series extent. In addition, the spatial differences found by subtracting the gSSURGO estimations from measured uncertainties aligned with the county administrative boundaries. The distribution of SOC stock was found to be related to land use, topography, and soil depth, while accuracy predictions were also influenced by data source.

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Soil organic matter content (SOM) and cation exchange capacity (CEC) are important agronomic soil properties. Accurate, high-resolution spatial information of SOM and CEC are needed for precision farm management. The objectives of this study were to: (1) map SOM and CEC in a low relief area using only lidar elevation-based terrain attributes, and (2) compare the prediction accuracy of SOM and CEC maps created by universal kriging, Cubist, and random forest with Soil Survey Geographic (SSURGO) database. For this study, 174 soil samples were collected from a depth from 0 to 10 cm. The topographic wetness index, topographic position index, multi resolution valley bottom flatness, and multi resolution ridge top flatness indices generated from the lidar data were used as covariates in model predictions. No major differences were found in the prediction performance of all selected models. For SOM, the predictive models provided results with coefficient of determination (R2) (0.44–0.45), root mean square error (RMSE) (0.8–0.83%), bias (0–0.22%), and concordance correlation coefficient (ρc) (0.56–0.58). For CEC, the R2 ranged from 0.39 to 0.44, RMSE ranged from 3.62 to 3.74 cmolc kg−1, bias ranged from 0–0.17 cmolc kg−1, and ρc ranged from 0.55 to 0.57. We also compared the results to the USDA Soil Survey Geographic (SSURGO) data. For both SOM and CEC, SSURGO was comparable with our predictive models, except for few map units where both SOM and CEC were either under or over predicted.

Exploring Local Riverbank Sediment Controls on the Occurrence of Preferential Groundwater Discharge Points

Groundwater discharge to rivers takes many forms, including preferential groundwater discharge points (PDPs) along riverbanks that are exposed at low flows, with multi-scale impacts on aquatic habitat and water quality. The physical controls on the spatial distribution of PDPs along riverbanks are not well-defined, rendering their prediction and representation in models challenging. To investigate the local riverbank sediment controls on PDP occurrence, we tested drone-based and handheld thermal infrared to efficiently map PDP locations along two mainstem rivers. Early in the study, we found drone imaging was better suited to locating tributary and stormwater inflows, which created relatively large water surface thermal anomalies in winter, compared to PDPs that often occurred at the sub-meter scale and beneath riparian tree canopy. Therefore, we primarily used handheld thermal infrared imaging from watercraft to map PDPs and larger seepage faces along 12-km of the fifth-order Housatonic River in Massachusetts, USA and 26-km of the Farmington River in Connecticut, USA. Overall, we mapped 31 riverbank PDPs along the Housatonic reach that meanders through lower permeability soils, and 104 PDPs along the Farmington reach that cuts through sandier sediments. Riverbank soil parameters extracted at PDP locations from the Soil Survey Geographic (SSURGO) database did not differ substantially from average bank soils along either reach, although the Farmington riverbank soils were on average 5× more permeable than Housatonic riverbank soils, likely contributing to the higher observed prevalence of PDPs. Dissolved oxygen measured in discharge water at these same PDPs varied widely, but showed no relation to measured sand, clay, or organic matter content in surficial soils indicating a lack of substantial near-surface aerobic reaction. The PDP locations were investigated for the presence of secondary bank structures, and commonly co-occurred with riparian tree root masses indicating the importance of localized physical controls on the spatial distribution of riverbank PDPs.

Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha−1 year−1) using a newly-developed relation with the Soil Survey Geographic Database range production data (R2 = 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers.

Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Integration of land cover change with soil information is important for valuation of soil carbon (C) regulating ecosystem services (ES) and disservices (ED) and for site-specific land management. The objective of this study was to assess the change in value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order (Soil Taxonomy), land cover, and land cover change (National Land Cover Database, NLCD) using information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. The total estimated monetary mid-point value for TSC in the state of South Carolina was $124.42B (i.e., $124.42 billion U.S. dollars, where B = billion = 109) with the following monetary distribution in 2016 and percent change in value between 2001 and 2016: barren land ($259.7M, −9%) (i.e., $259.7 million U.S. dollars, where M = million = 106), woody wetlands ($33.8B, −1%), shrub/scrub ($3.9B, +9%), mixed forest ($6.9B, +5%), deciduous forest ($10.6B, −7%), herbaceous ($4.8B, −5%), evergreen forest ($28.6B, +1%), emergent herbaceous wetlands ($6.9B, −3%), hay/pasture ($7.3B, −10%), cultivated crops ($9.9B, 0%), developed, open space ($7.0B, +5%), developed, medium intensity ($978M, +46%), developed, low intensity ($2.9B, +15%), and developed, high intensity ($318M, +39%). The percent change in monetary values was different from percent change in areas because different soil orders have different TSC contents. The percent changes (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover with $1.1B in likely “realized” social cost of C mostly associated with Ultisols ($658.8M). The Midlands region of the state experienced the highest gains in the “high disturbance” classes and corresponding SC-CO2 with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in SC-CO2 for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Spatial and temporal analyses of land cover can identify critical locations of soil carbon regulating ecosystem services at risk.

Mapping Soil Properties to Advance the State of Spatial Soil Information for Greater Food Security on US Tribal Lands

Knowledge, data, and understanding of soils is key for advancing agriculture and society. There is currently a critical need for sustainable soil management tools for enhanced food security on Native American Tribal Lands. Tribal Reservations have basic soil information and limited access to conservation programs provided to other U.S producers. The objective of this study was to create first ever high-resolution digital soil property maps of Quapaw Tribal Lands with limited data for sustainable soil resource management. We used a digital soil mapping (DSM) approach based on fuzzy logic to model the spatial distribution of 24 soil properties at 0–15 and 15–30 cm depths. A digital elevation model with 3 m resolution was used to derive terrain variables and a total of 28 samples were collected at 0–30 cm over the 22,880-ha reservation. Additionally, soil property maps were derived from Gridded Soil Survey Geographic Database (gSSURGO) for comparison. When comparing properties modeled by DSM to those derived from gSSURGO, DSM resulted in lower root mean squared error (RMSE) for percent clay and sand at 0–15 cm, and cation exchange capacity, percent clay, and pH at 15–30 cm. Conversely, gSSURGO-derived maps resulted in lower RMSE for cation exchange capacity, pH, and percent silt at the 0–15 cm depth, and percent sand and silt at the 15–30 cm depth. Although, some of the soil properties derived from gSSURGO had lower RMSE, spatial soil property patterns modeled by DSM were in better agreement with the topographic complexity and expected soil-landscape relationships. The proposed DSM approach developed property maps at high-resolution, which sets the baseline for production of new spatial soil information for Quapaw Tribal soils. It is expected that these maps and future versions will be useful for soil, crop, and land-use decisions at the farm and Tribal-level for increased agricultural productivity and economic development. Overall, this study provides a framework for developing DSM on Tribal Lands for improving the accuracy and detail of soil property maps (relative to off the shelf products such as SSURGO) that better represents soil-forming environments and the spatial variability of soil properties on Tribal Lands.

Soil Diversity (Pedodiversity) and Ecosystem Services

Soil ecosystem services (ES) (e.g., provisioning, regulation/maintenance, and cultural) and ecosystem disservices (ED) are dependent on soil diversity/pedodiversity (variability of soils), which needs to be accounted for in the economic analysis and business decision-making. The concept of pedodiversity (biotic + abiotic) is highly complex and can be broadly interpreted because it is formed from the interaction of atmospheric diversity (abiotic + biotic), biodiversity (biotic), hydrodiversity (abiotic + biotic), and lithodiversity (abiotic) within ecosphere and anthroposphere. Pedodiversity is influenced by intrinsic (within the soil) and extrinsic (outside soil) factors, which are also relevant to ES/ED. Pedodiversity concepts and measures may need to be adapted to the ES framework and business applications. Currently, there are four main approaches to analyze pedodiversity: taxonomic (diversity of soil classes), genetic (diversity of genetic horizons), parametric (diversity of soil properties), and functional (soil behavior under different uses). The objective of this article is to illustrate the application of pedodiversity concepts and measures to value ES/ED with examples based on the contiguous United States (U.S.), its administrative units, and the systems of soil classification (e.g., U.S. Department of Agriculture (USDA) Soil Taxonomy, Soil Survey Geographic (SSURGO) Database). This study is based on a combination of original research and literature review examples. Taxonomic pedodiversity in the contiguous U.S. exhibits high soil diversity, with 11 soil orders, 65 suborders, 317 great groups, 2026 subgroups, and 19,602 series. The ranking of “soil order abundance” (area of each soil order within the U.S.) expressed as the proportion of the total area is: (1) Mollisols (27%), (2) Alfisols (17%), (3) Entisols (14%), (4) Inceptisols and Aridisols (11% each), (5) Spodosols (3%), (6) Vertisols (2%), and (7) Histosols and Andisols (1% each). Taxonomic, genetic, parametric, and functional pedodiversity are an essential context for analyzing, interpreting, and reporting ES/ED within the ES framework. Although each approach can be used separately, three of these approaches (genetic, parametric, and functional) fall within the “umbrella” of taxonomic pedodiversity, which separates soils based on properties important to potential use. Extrinsic factors play a major role in pedodiversity and should be accounted for in ES/ED valuation based on various databases (e.g., National Atmospheric Deposition Program (NADP) databases). Pedodiversity is crucial in identifying soil capacity (pedocapacity) and “hotspots” of ES/ED as part of business decision making to provide more sustainable use of soil resources. Pedodiversity is not a static construct but is highly dynamic, and various human activities (e.g., agriculture, urbanization) can lead to soil degradation and even soil extinction.

A regional soil classification framework to improve soil health diagnosis and management

AbstractSoil health research has developed soil tests to validate management strategies for creating healthier soils, such as cover crops, compost, and reduced tillage. Missing in this research agenda are soil health management strategies and diagnostics tailored to the systematic variation of soils. The Soil Survey Geographic (SSURGO) database offers comprehensive estimates for many properties linked to soil health indicators. This research uses unsupervised classification of soil surface (0–30 cm) properties and root restrictive horizons in an internationally important agricultural region, the Central and Coastal Valleys of California, covering 5.6 million ha of mostly cropland. K‐means clustering of 10 soil properties derived from SSURGO revealed groups distinct from USDA‐NRCS Soil Taxonomy. Clustering metrics, data visualization, and validation tests justified a seven‐region soil health conceptual model that divided the landscape into two broad classes—those with and without performance limitations. Results revealed three pedogenic conditions limiting soil performance: (a) root restrictive horizons that impede root growth into deeper soil; (b) salinity and alkalinity harmful to crops; and (c) shrink–swell properties making soils challenging to cultivate. Soils without performance limitations, soils with restrictive horizons, and salt‐affected soils are further divided by texture and/or organic matter for a total of seven soil health regions. In reality, variation in soil properties is often continuous across the landscape and sometimes fractal in nature, but this research establishes a testable framework for evaluating soil health diagnostics according to geographically coherent differences in soil properties, demonstrating potential for how soil health management and interpretation can be tailor‐made to the landscape.

Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations

AbstractAccurately identifying the soil map unit component at a specific point‐location within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone‐based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non‐soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a sampling‐location based on the numerical similarity of soil property values between the sampled soil profile and the soil components mapped in that area. To evaluate this soil identification framework, we used a subset of the U.S. National Cooperative Soil Survey Soil Characterization Database (NCSS–SCD) as our soil profile test dataset and the U.S. Soil Survey Geographic (SSURGO) database as our reference dataset using profile data of soil components in the area surrounding each test profile. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of generalizing depth‐wise variability (i.e., depth‐support) and generalizing across feature space (i.e., soil properties). Three soil property groups (i.e., Novice, Expert, Expert‐Plus) representing different levels of detail and three types of depth‐support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class, rock fragment volume class, and soil color) resulted in nearly as high identification accuracy (46–53%) as that achieved with an Expert (48–57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert‐Plus dataset (53–60%). This study also showed minimal effect from the type of depth‐support used to represent depth‐wise variability. Furthermore, we evaluated several measures of soil functional similarity (i.e., ecological sites, land capability, taxonomic distance) which resulted in management relevant accuracies ranging from 65–89%. These findings support the utility of simple soil observations sampled at fixed depths for soil identification.

Distribution of silty mantles in soils of the Northcentral Appalachians, USA

Across the northcentral Appalachians, USA, high silt content soils are found as silty mantles or deep, high silt content pedons. The origin of such soils can be attributed to additions of wind-blown dust deposits (WBD) or local parent materials (i.e. shales or siltstone lithology). Previous research on silt soils originating specifically from WBD attributed to late marine isotope stage (MIS) 2 loess has often been isolated to drainageways receiving outwash from deglaciation. We hypothesize that thin (<25–50 cm) silty mantles, and some deep silt soils occurring farther from outwash systems, are also indicative of post MIS 2 WBD and their extent is widespread. To test this hypothesis, we evaluated over 900 pedons from an ~119,280 km2 area of the northcentral Appalachians, USA to: (i) develop a particle size signature indicative of soils largely derived from WBD versus local parent materials, (ii) determine the potential depth of WBD additions to soils, and (iii) document the spatial extent of WBD versus deep, high-silt content soils across part of the region. Results suggest that silty mantles are prevalent across the study area and have a particle size signature indicative of loess and the mean depth of WBD additions to soils is ~50 cm. Below 50 cm, local lithology or pedogenesis more influences particle size trends. Pedon results were applied in a spatial modeling effort using the USA Soil Survey Geographic Database (SSURGO) to document the extent of silty mantles (over non-silt sourced parent materials) and deep, high silt content soils. Model results indicate silty mantles are common on stable landscape positions or positions that accumulate sediments (depressions or valleys). Our finding of silty mantles, or deep high silt content profiles, over lithologies high in sand infers a source other than the underlying lithology. In addition, the presence of such silty mantles on summits and upper slopes suggests the importance of these landforms as stable positions preserving WBD. Our results suggest that stable landforms preserve WBD deposits and where topography is of lesser relief, broader deposition across several aspects is common. Aspect dependent deposition is tied to glaciofluvial sources of WBD, and modeled silty mantles correspond strongly to regional studies of loess.