Peer-reviewed veterinary case report
Mapping soil minerals and chemicals across the US with Bayesian models
By Bondo KJ et al.·2026·Department of Veterinary Population Medicine, United States·View original on Europe PMC →
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Original publication title: Generating geochemical and mineralogy distributions of soil in the conterminous United States using Bayesian hierarchical spatial models.
Plain-English summary
This research focuses on understanding the distribution of minerals and chemicals in soil across the contiguous United States, which is important for assessing natural resources and environmental health. The scientists developed a method using advanced statistical modeling to create detailed maps showing where different soil elements, like cobalt and zinc, are found. By considering factors like soil type, landscape, climate, and land use, they were able to produce accurate predictions of soil composition. This information can be useful for various fields, including ecology and agriculture. The outcome of this study is a set of tools and maps that can help visualize and analyze soil geochemistry effectively.
Abstract
Characterizing geochemical and mineralogical soil distributions across large spatial extents is essential for understanding mineral resources, ecosystem processes, and environmental risks. Rasters of soil geochemical distributions for the conterminous United States, however, are limited. We present a Bayesian modeling workflow and tool for generating predictive geochemical and mineralogy distribution maps for the conterminous United States using integrated nested Laplace approximation (INLA) with the stochastic partial differential equation approach. By modeling soil geostatistical data with environmental covariates (soil properties, topography, climate, and land cover), we generate predictive distributions of soil geochemistry that can be mapped or extracted for further analyses. As an example, we model the spatial distribution of trace elements in soil relevant to vertebrate health (cobalt, copper, iron, manganese, selenium, and zinc) and provide a workflow that can be used to generate and visualize predictive distributions of 39 other major and trace elements and 21 minerals of the soil survey, supporting a variety of ecological, environmental, and agricultural applications. <b>Bayesian Modeling:</b> Uses R-INLA to predict soil geochemistry across large spatial extents. <b>Covariate Integration:</b> Incorporates environmental variables to increase predictive accuracy. <b>Raster Generation:</b> Produces continuous geospatial layers of element and mineral distributions of the conterminous United States for a variety of applications.
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Search related cases →Original publication on Europe PMC: https://europepmc.org/article/MED/41799829