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					National Cooperative Soil Survey Soil Characterization Data Accessibility

Soil characterization data are is collected by National Cooperative Soil Survey (NCSS)
partners in support of the soil survey program. The data include chemical, mineralogical,
and physical measurements, calculated values, and profile descriptions. The NRCS Soil
Survey Lab provides standard field and laboratory operating procedures. Some partners
continue to collect data while others have discontinued.

Soil characterization data There continues to be a needed for soil characterization data to
document the resource, verify mapping and classification,classification, develop soil
interpretations, and provide modelling input. Traditionally data were disseminated in
formal and informal reports, included in published soil survey or published in scientific
documents. The data are available on-line (Table 1), entered in electronic files, or remain
oin paper forms. Only the on-line data are accessible by the public.
Electronic access to all available soil characterization data needs to be provided to.
    1. More complete characterization of the major soils of the US.                                Formatted: Numbered + Level: 1 +
                                                                                                   Numbering Style: 1, 2, 3, … + Start at: 1 +
    2. Assess the areas of data deficienciesholes.                                                 Alignment: Left + Aligned at: 0.25" + Tab
    3. Reduce redundancy in sampling.                                                              after: 0.5" + Indent at: 0.5"
    4. Increase the kinds of soil and land use for modeling.                                       Formatted: Bullets and Numbering
    1.5.Increase data use.
WHY???


Partner                   Source                                       Last update     pedons
NRCS                      http://ssldata.nrcs.usda.gov/                2006            30,000
University of Missouri    http://soils.missouri.edu/pedon/user.asp     2006            6,400
Ohio State University     http://www.ag.ohio-                          1996            4,000
                          state.edu/~pedology/Soildatabase.html                        potential
Texas A&M                 http://soildata.tamu.edu/                    2004            106
Penn State                http://www.personal.psu.edu/faculty/f/8/f8                   ~1,000
                          i/pedon/download.html (program but no
                          data)
University of Illinois    http://www.il.nrcs.usda.gov/technical/soil   1990            1,552
                          s/soil_lab.html
University of             Database                                     1991            2067
Minnesota
North Dakota State        Database                                     1990            983
Ohio State University     Database                                     2005            3213
Purdue                    Database                                     1986            1664




System The requirements for a system to providing access to soil characterization data
Partners need to agree on the set of minimum definitive information on each measured
value. A suggestion is that the following be known about each measured value:
       Laboratory data source                                                                  Formatted: Indent: Left: 0", First line: 0.5"
               The laboratory data source is the name of the partner that collected the        Formatted: Indent: Left: 1"
               data. The data source should be clearly identified on each report or viewed
               page.
                                                                                               Formatted: Indent: Left: 0.5"
       The procedure used to obtain the measurement
              The procedure should be referenced from the standard laboratory                  Formatted: Indent: Left: 1"
              operating procedures. If the procedure is not currently identified, a method
              of adding procedures needs to be provided.
       The analyte measured
              The analyte is the entity measured and computed to the units of measure          Formatted: Indent: Left: 1"
              specified in the procedure.
       The preparation procedure used to prepare the sample
              The preparation procedures are currently defined in the methods manual.          Formatted: Indent: Left: 1"
              The preparation procedure defines the final moisture state of the prepared
              sample and measurement of the coarse fragments. If the preparation
              procedure is not defined, a method of adding the preparation procedure
              needs to be provided.
       The size fraction on which the value was measured
              The measurement size fraction needs to be recorded. Typically this value         Formatted: Indent: Left: 1"
              is <2 mm, but data are also collected on other size fractions.
       The reporting basis (moisture and size fraction)
              There are two reporting basis of interest. The first is the moisture state for   Formatted: Indent: Left: 1"
              which the data are reported. The standard operating procedures specify
              reporting the data on an oven-dry basis; however data have are also
              reported on an air-dry basis. The second is the soil fraction on which the
              calculations are based. The data are usually reported on a <2 mm basis or
              the fraction on which the data are measured. Coarse fractions are reported
              on a <75 mm and whole soil basis.
       Quality assurance and control protocols used
              The quality assurance and control procedures used to validate and verify         Formatted: Indent: Left: 1"
              the data need to be documented.


The laboratory data source is the name of the partner that collected the data. The data
source should be clearly identified on each report or viewed page

The analyte is the entity measured and computed to the units of measure specified in the
procedure.

The procedure should be referenced from the standard laboratory operating procedures. If
the procedure is not currently identified, a method of adding procedures needs to be
provided.
The preparation procedures are currently defined in the methods manual. The preparation
procedure defines the final moisture state of the prepared sample and measurement of the
coarse fragments. If the preparation procedure is not defined, a method of adding the
preparation procedure needs to be provided.

The measurement size fraction needs to be recorded. Typically this value is <2 mm, but
data are also collected on other size fractions.

There are two reporting basis of interest. The first is the moisture state for which the data
are reported. The standard operating procedures specify reporting the data on an oven-dry
basis; however data have are also reported on an air-dry basis. The second is the soil
fraction on which the calculations are based. The data are usually reported on a <2 mm
basis or the fraction on which the data are measured. Coarse fractions are reported on a
<75 mm and whole soil basis.

The quality assurance and control procedures used to validate and verify the data need to
be documented.




Data input                                                                                      Formatted: Font: Bold
Data are added manually or uploaded electronically.

Ownership control
Workspace to edit, verify, manual input, transfer from other systems, promote to
warehouse, remove from warehouse



Questions                                                                                       Formatted: Font: Bold


Calculated values
Should calculation tools be developed to populate calculated values from the measured
values? How are calculated values distinguished from measured values?



Data staging to data warehouse                                                                  Formatted: Font: Bold
Data staging is a major process that includes: extracting, transforming, loading and
indexing, and quality assurance checking. Extracting means reading and understanding
the source data, and copying the parts that are needed to the data staging area for further
work. Transforming involves 1. cleaning correcting data, resolving domain conflicts,
dealing with missing data elements and parsing into standard formats, 2. purging selected
fields from the legacy data that are not useful for the data warehouse, 3. combining data
sources, and 4. creating surrogate keys for each dimension record to enforce referential
integrity between dimension tables and the fact tables. Loading occurs at the end of the
transformation process. The data are in the form of load record images, which are bulk
loaded to the recipient data mart (usually by replication of dimension and fact tables).
The target data mart must then index the data. Quality assurance can be checked by
running a comprehensive exception report over the entire set of newly loaded data. All
counts and totals must be correct.

Sketch of system

  Source                                                          Data Warehouse
  System s               Data Staging Area                        Presentation
  (Legacy)                                                        Servers
                          Storage:                                Data Mart #1:
                                                    populate,     OLAP
                          flat flies;               replicate,
                          RDBMS;                                   (ROLAP and/or
                                                    recover         MOLAP)
                          other
                                                                     query services;
               extract    Processing:                             dimensional!
                          clean;                                  subject oriented;
                          prune;                                  locally implemented;
                          combine;                                user group driven;
                          remove dupilcates;                      may store atomic
                          household;                                data;
                          standardize;                            may be frequently
                          conform dimensions;                       refreshed;
                          store awaiting                          conforms
                            replication                             to DW bus
               extract    export to data marts;
                                                                               Conformed
                          No user                                              Dimensions
                                                                               and Facts
                          query services
                                                   populate,
                                                   replicate,
                                                   recover

                                                                    Data Mart # 2

              extract




            upload cleaned dimensions




Soil Survey Staff. 2004. Soil Survey Laboratory methods manual. Version 4.0. R. Burt,
Ed. Soil Survey Investigations Report No. 42. U.S. Gov. Print. Office, Washington, DC.

Soil Conservation Service. 1984. Procedures for collecting soil samples and methods of
analysis for soil survey. Soil Survey Investigations Report No. 1. U.S. Gov. Print. Office,
Washington, DC.

				
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