United States Soil Survey Databases

2011 ◽  
pp. 718-729
Keyword(s):  
United States ◽  
Soil Survey

scholarly journals Using soil survey data to model potential Coccidioides soil habitat and inform Valley fever epidemiology

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247263
Author(s):  
Robert R. Dobos ◽  
Kaitlin Benedict ◽  
Brendan R. Jackson ◽  
Orion Z. McCotter
Keyword(s):  
Public Health ◽  
United States ◽  
Natural Habitat ◽  
Organic Matter Content ◽  
Model Potential ◽  
Climatic Conditions ◽  
Matter Content ◽  
Soil Survey ◽  
Valley Fever ◽  
Temperature And Precipitation

Coccidioidomycosis, also known as Valley fever, is a disease that can result in substantial illness and death. It is most common in the southwestern United States and areas of Latin America with arid climates, though reports increasingly suggest its range is wider than previously recognized. The natural habitat of the causative organisms, Coccidioides spp., have been associated with certain soil properties and climatic conditions. Current understanding of its geographic range is primarily defined by skin test studies and outbreak locations. We developed a fuzzy system model to predict suitable soil habitats for Coccidioides across the western United States based on parameters (electrical conductivity, organic matter content, pH, water holding capacity, temperature, and precipitation) from sites where soil sampling has confirmed the presence of Coccidioides. The model identified high coccidioidomycosis incidence areas as having high suitability and identified pockets of elevated suitability corresponding with outbreak locations outside the traditional range. By providing high-resolution estimates of Coccidioides suitability, including areas without public health surveillance for coccidioidomycosis, this model may be able to aid public health and clinical provider decision making. Awareness of possible Coccidioides soil habitats could help mitigate risk during soil-disturbing activities and help providers improve coccidioidomycosis diagnosis and treatment.

scholarly journals Effects of agriculture on the classification of Black soils in the Midwestern United States

2012 ◽  
Vol 92 (3) ◽  
pp. 403-411 ◽  
Author(s):  
Jessica J. Veenstra ◽  
C. Lee Burras
Keyword(s):  
United States ◽  
Agricultural Land ◽  
Laboratory Data ◽  
Soil Classification ◽  
Current Data ◽  
Soil Survey ◽  
Soil Taxonomy ◽  
Midwestern United States ◽  
Reference Base

Veenstra, J. J. and Burras, C. L. 2012. Effects of agriculture on the classification of Black soils in the Midwestern United States. Can. J. Soil Sci. 92: 403–411. Soil surveys are generally treated as static documents. Many soil survey users assume that pedon data generated 30 to 50 yr ago still represents today's soil, as short-term changes in soil properties are perceived to be limited to the soil surface and thus pedologically insignificant. In this study, we re-sampled and re-analyzed 82 pedons with historical descriptions and laboratory data in Iowa, United States, to evaluate changes in soil profile properties and taxonomic classification after approximately 50 yr of agricultural land use. Using historical and current data, we classified sampled pedons using Canadian Soil Taxonomy, US Soil Taxonomy and the Food and Agriculture Association World Reference Base (FAO-WRB). Our results show that soil characteristics have changed significantly enough to change the classification. In each taxonomic system, the classification of 60% or more of the sampled pedons differed from the original. Classification of 15 to 32% of the sampled pedons changed at the Order (or equivalent) level with 11 to 33% of the pedons originally classified as Black soils – Mollisols, Chernozems or Phaeozems – no longer classified as Black soils. The change in soil classification over such a short-time period challenges the validity and usefulness of treating existing soil maps as static documents as well as traditional soil classification hierarchies.

scholarly journals XPolaris: an R-package to retrieve United States soil data at 30-meter resolution

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Luiz H. Moro Rosso ◽  
Andre F. de Borja Reis ◽  
Adrian A. Correndo ◽  
Ignacio A. Ciampitti
Keyword(s):  
United States ◽  
Learning Algorithm ◽  
Geographical Location ◽  
R Package ◽  
Soil Survey ◽  
Soil Series ◽  
Raster Data ◽  
The Core ◽  
Programming Skills ◽  
R Programming

Abstract Objectives This data article aims to introduce the “XPolaris” R-package, designed to facilitate access to detailed soil data at any geographical location within the contiguous United States (CONUS). Without the need of advanced R-programming skills, XPolaris enables users to convert raster data from the POLARIS database into traditional spreadsheet format [i.e., Comma-Separated Values (CSV)] for further data analyses. Data description The core of this publication is a code-tutorial envisioned to assist users in retrieving soil raster data within the CONUS. All data is sourced from the POLARIS database, a 30-m probabilistic map of soil series and different soil properties [Chaney et al. Geoderma 274:54, 2016, Chaney et al. Water Resour Res 55:2916, 2019]. POLARIS represents an optimization of the Soil Survey Geographic (SSURGO) database, circumventing issues of spatial disaggregation, harmonizing, and filling spatial gaps. POLARIS was constructed using a machine learning algorithm, the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART-HPC) [Odgers et al. Geoderma 214:91, 2014]. Although the data is easily accessible in a raster format, retrieving large amounts of data can be time-consuming or require advanced programming skills.

Soil Surveys and Archaeology

2004 ◽  
Author(s):  
Vance T. Holliday
Keyword(s):  
United States ◽  
20Th Century ◽  
Driving Forces ◽  
The United States ◽  
Soil Survey ◽  
State Agencies ◽  
Soil Maps ◽  
National Cooperative ◽  
Local Agencies ◽  
Soil Surveys

Soil survey and mapping is one of the most fundamental and best-known applications of pedology. The preparation of soil maps began in the 19th century (Yaalon, 1997), but systematic county-based soil surveys began in the 20th century in the United States (Simonson, 1987, p. 3). The production of soil maps based on systematic soil surveys has been one of the primary driving forces in pedologic research in both academic and governmental settings in the United States and worldwide through much of the 20th century (Simonson, 1987, 1997; Yaalon and Berkowicz, 1997). For example, soil survey and mapping has been a primary function of the USDA since 1899 (Simonson, 1987, p. 3; Soil Survey Division Staff, 1993, p. 11). Soil maps have been prepared for a variety of uses at scales ranging from a few hectares to those of continental and global magnitude. Published soil surveys contain a wealth of data on landscapes as well as soils, but are generally an underused (and likely misunderstood) resource in geoarchaeology, probably because of their agricultural and land-use orientation. This chapter presents a discussion of what soil surveys are (and are not) and potential as well as realized applications in archaeology. Much of the discussion focuses on the county soil surveys published by the USDA because they are so widely available, although applications of other kinds and scales of soil maps that have been applied in archaeology or that have archaeological applications also are discussed. Many countries in the world have national soil surveys whose primary mission is the mapping and inventorying of the nation’s soil resource. In the United States, soil survey is a cooperative venture of federal agencies, state agencies (including the Agricultural Experiment Stations), and local agencies, coordinated by the National Cooperative Soil Survey (Soil Survey Division Staff, 1993, p. 11). The principal federal agency involved in soil survey is the National Resource Conservation Service (NRCS; formerly the Soil Conservation Service, SCS) of the USDA. The mapping of soils by the NRCS/USDA is probably the agency’s best-known activity. Its many published county soil surveys are its most widely known and widely used product.

Urban Soil Mapping through the United States National Cooperative Soil Survey

Urban Soils ◽  
2017 ◽  
pp. 15-60 ◽  
Author(s):  
Luis Hernandez ◽  
Maxine Levin ◽  
Joe Calus ◽  
John Galbraith ◽  
Edwin Muñiz ◽  
...  
Keyword(s):  
United States ◽  
Urban Soil ◽  
The United States ◽  
Soil Mapping ◽  
Soil Survey ◽  
National Cooperative

scholarly journals The extent of soil loss across the US Corn Belt

2021 ◽  
Vol 118 (8) ◽  
pp. e1922375118
Author(s):  
Evan A. Thaler ◽  
Isaac J. Larsen ◽  
Qian Yu
Keyword(s):  
United States ◽  
Soil Erosion ◽  
Soil Loss ◽  
Crop Yields ◽  
Scale Up ◽  
The United States ◽  
Agricultural Landscapes ◽  
Soil Survey ◽  
Economic Losses ◽  
Corn Belt

Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 105 km2 of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields.

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