|WILLS, SKYE - Natural Resources Conservation Service (NRCS, USDA)|
|Herrick, Jeffrey - Jeff|
|NAUMAN, TRAVIS - Us Geological Survey (USGS)|
|BRUNGARD, COLBY - New Mexico State University|
|BEAUDETTE, DYLAN - Natural Resources Conservation Service (NRCS, USDA)|
|O'GREEN, ANTHONY - University Of California, Davis|
Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/2/2018
Publication Date: N/A
Interpretive Summary: Soil surveys can provide access to a virtual treasure trove of knowledge and information. Mobile apps such as SoilWeb and mySoil now allow anyone with access to a GPS-enabled mobile phone to determine which soil map unit (SMU) they are in. These tools, however, still require an understanding of soil survey and map unit concepts to correctly identify the soil series map unit component (SMUC). Therefore, individuals wishing to access soil information for a particular location most commonly assume that the dominant SMUC exists across the entire SMU (DC-based algorithm). The DC-based algorithm is simple and straightforward, but is often unreliable. Here, we developed two new algorithms using information from traditional soil maps based on 1) location alone (location-based algorithm), and 2) location together with soil properties that can be relatively easily determined with guidance provided through smart phone and other mobile platforms (observation-based algorithm). Our results showed that observation-based algorithms could significantly increase the reliability of SMUC identification by using a small set of easily-collected field data, suggesting that the observation-based algorithm be used whenever possible. However, if field observations of soil texture and slope are unavailable, the location-based algorithm may be useful to slightly improve predictions (relative to simply using the dominant SMUC) for locations where 1) they are close (< 15 m) to a boundary of a soil map unit polygon (SMUP) and 2) there are multiple possible components within the SMUP, although the performance of both the location- and DC-based algorithm were significantly lower and less satisfactory than that of the location-based algorithm. Providing these algorithms (especially the observation-based) through applications on mobile devices would improve the utility of soil survey information for non-soil scientists and trained professionals alike.
Technical Abstract: Use of soil survey information by non-soil scientists is often limited by their inability to 6 select the correct soil map unit component (COMP). Here, we developed two approaches that 7 can be deployed to smartphones for non-soil scientists to identify COMP using location alone, or 8 location together with easily observed field data (i.e., slope, depth to restrictive layer, and soil 9 texture by depth). In addition, we also compared the two newly developed approaches with a 10 traditional approach identifying COMP based on the dominant COMP (DC-based approach). All 11 three approaches were tested using the Rapid Assessment of U.S. Soil Carbon (RaCA) database 12 and the combined USDA-Natural Resources Conservation Service (NRCS) Soil Survey 13 Geographic database (SSURGO) and USDA-NRCS State Soil Geographic Database 14 (STATSGO2). Results indicated that the observation-based approach performed significantly 15 better than the other two approaches, suggesting that a small set of easy-to-measure site-specific 16 observations could significantly improve the COMP identification. Location- and DC-based 17 approaches, overall, had similar low performance. However, the location-based approach slightly 18 improved identifications over the DC-based approach for cases where 1) there were multiple 19 possible components within the soil map unit and 2) components were located in close proximity 20 to a boundary of a different soil map unit polygon (SMUP). The benefit of using the location- based approach may be greater in specific soil survey areas where topography 21 was the major 22 factor leading to the creation of the map unit legend.