413-9 The National Cooperative Soil Survey Database - a Resource for Soil Research.

Poster Number 464-533

See more from this Division: SSSA Division: Pedology
See more from this Session: Soil Pedology Poster II

Wednesday, November 9, 2016
Phoenix Convention Center North, Exhibit Hall CDE

Skye A. Wills, Soil Science Division, USDA-NRCS, Lincoln, NE, Henry Ferguson, USDA-NRCS-NSSC, Lincoln, NE, Zamir Libohova, National Soil Survey Center, USDA-NRCS, Lincoln, NE, Stephen Roecker, USDA- NRCS, Indianapolis, IN, Dylan Beaudette, USDA-NRCS, Sonora, CA and Lucas E. Nave, University of Michigan Biological Station, Pellston, MI
Abstract:
The National Cooperative Soil Survey (NCSS) is a partnership of Federal, regional, State, and local agencies and private entities and institutions that gather and disseminate soil information according to a comment set of standards.  The USDA-NRCS Soil Science Staff maintains standards in the National Soil Survey Handbook and lead the effort of collate and distribute soil survey data and information.  The NCSS Characterization database has been available through web queries for some time.  More recently as Mcrosoft Access database that contains the most commonly requested data from the National Cooperative Soil Survey Laboratories has been made available. The database includes data from the Kellogg Soil Survey Laboratory and cooperating universities. In addition to commonly requested data, the Access database includes metadata tables that describe the column headings of the laboratory data tables.

The NCSS Database supports various research needs. For example, since 2011, the USDA-NRCS has been the single largest data contributor to the International Soil Carbon Network Database. Through three versions of the ISCN Database, the NRCS has contributed soil and site data from its NCSCD and NASIS Databases, which have supported research proposals and publications by the ISCN membership. Today, the ISCN is a community of over 400 academic and agency scientists from around the world, and NCSS data have a home among the 71,198 profiles (431,324 layers) contributed and used by this community of researchers.  The database has been used to inform soil mapping and relationships among soil properties.  Current work includes exploration of state-of-the-art machine learning methods to fit models of Pedo-Transfer-Functions (PFTs) for quantitative (bulk density) and qualitative (USDA taxonomical great group) soil properties. Future work will link laboratory measured properties to available morphology information.

See more from this Division: SSSA Division: Pedology
See more from this Session: Soil Pedology Poster II