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ORGANIZER:MAILTO:webmaster@argisusers.org
TITLE:Leveraging GIS and Computational Statistics to Enhance Predictive Soil Modeling
DTSTART:20191031T193000Z
DTEND:20191031T200000Z
SUMMARY:Leveraging GIS and Computational Statistics to Enhance Predictive Soil Modeling
DESCRIPTION:Presenter: Bryan Fuentes, University of Arkansas | Bridging geographic information systems (GIS) and computational statistics engines leverages a wide array of tools to analyze spatial datasets and to produce new information. Spatial datasets, which first undergo through geoprocessing routines, can now be easily analyzed with statistical robustness and computational complexity thanks to this bridging. Data analysis and mining, simulations and predictive modeling are some of the computational statistics procedures now commonly applied to spatial information. In soil science, the comprehension of physical, chemical and biological properties of soils is fundamental to establish adequate management practices involving water, microorganisms and crops. The predictive modeling of soil properties becomes useful when precise management is required, but the lack of resources does not allow intensive soil sampling campaigns. This work exposes how the predictive modeling of soils becomes statistically robust and spatially accurate/precise, thanks to the integration of GIS and computational statistics engines.
LOCATION:Magnolia / Dogwood
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