rt.
Je
S
1S
via
trail,
ma ted
se
om
eds,
le
id
B
ty
comes
| of
jSSary
lote
Ing
which can support the full range of stated requirements. The key com-
ponent of an automated data base is its stability and usefulness over
time. The APU and its data elements. provide this stability. The APU
design rationale is directly transferable to a data base design con-
cept. This rationale is to identify and utilize those independent
variables within the agriculture environment upon which remote sens-
ing technology and data analysis can be baselined. There are three
such variables: 1) soils, 2) climatic regions, and 3) historical
events/data.
Within this information requirements environment, efficient automated
support to data storage and retrieval becomes a mandatory element
for exploitation of remotely sensed data. Given this statement of
fact, and a baseline of independent variables (APU's), the USDA
developed the functional and operational design concept for an
application test data base.
B. Design
1. Functional Design Concept
Following the cost and utility requirements defined in the preced-
ing section, the functional design architecture of an APU-
oriented data base is summarized.
a. Logical Structure. The entire agriculture and potential
agricultural universe is delineated by a fixed grid cell with
a unique latitude and longitude address (x and Y). The
agricultural universe is defined by APU's. This master data
set contains soils, climatic, geological, and administrative
boundaries, historical agricultural statistics, and current
meteorological data.
Linked to this master data set via stored indices are 1 through
N commodity/country data sets. These secondary sets contain
the following generic elements: 1) Statistical sample alloca-
tion by commodity/region, 2) commodity kind, 3) multispectral
data used in commodity analysis, 4) yield model forms by
crop/region, 5) statistical aggregation and/or probability
parameters by crop and region, 6) multispectral coverage
requirements by crop/region/year, and 7) crop calendars by
region.
It has been argued that this design concept is extremely
costly, unnecessarily complex, and defies efficient reorganiza-
tion. This is only true if one pursues the logic that all
data sets are maintained on-line until needed in the analysis
process. The physical data structure concept of our dis-
tributed network also embodies a physical structure concept
of multiple storage levels activated upon demand and deacti-
-vated upon completion of specified tasks.