Project Overview
The spatial complexity of crop yield in farm fields is a manifestation
of the local redistribution of climate and biophysical variables due to
topography. Topography modifies crop yields due to the redistribution
of water, solar radiation, and soil materials. Primary and secondary terrain
attributes are ideal for revealing previously unseen variability in topography
and associated land surface processes, and provide useful explanatory
variables for indicating landscape processes that drive crop yield and
overall soil fertility. Primary terrain attributes are elevation, slope and aspect. Secondary terrain attributes
are compound interactions between elevation, aspect and slope. By using the Exploring Agricultural Relationships
in 3D software, an increased under-standing of the complex relationships
among the multiple factors affecting crop growth will be developed.
The objectives in developing Exploring Agricultural Relationships in
3D are to:
1. Develop a 3-D visualization tools for use with the Internet that will
facilitate multidisciplinary, multi-state research and education activities,
and
2. Enhance education and outreach programs with use of the 3D visualization
and analysis techniques.
Exploring Agricultural Relationships in 3-D is a useful educational medium
for studying terrain relationships in a virtual environment. Users can
zoom, turn, roll, pan, fly-through, or walk-through 3-D representations
of these data to explore and visualize relationships among variables.
In addition, users can share information and interact with a data set
without downloading or transferring it. Individuals who have data sets
containing elevation information can use the DBF2IFF tool to prepare the
data for viewing on their personal computer with the Exploring Agricultural
Relationships in 3-D software.
The crops currently being studied as a part of the USDA funded project
are corn, cotton, peanuts, sugar beets, and wheat. These crops are grown
in very different climates on fields that range from very little or no
topographic variability (e.g., cotton in Georgia) to those with a lot
of topographic variability (e.g., wheat in Montana). Image data are acquired
from satellites, high and low altitude aircraft, and the project’s
unmanned aerial vehicle (UAV). Other crop data include crop surface measurements,
yield measurements from combine flow meters, and drilled field core samples.
The software allows individuals to explore terrain, climatic conditions,
and agricultural production in states and counties where the field research
was conducted. The learning techniques are based on Data Mining and Knowledge
Discovery, which attempt to extract knowledge and insight from spatial
data sets without a predefined notion about the potential relationships.
Data from the various research projects have been prepared and are a part
of a series of self directed educational activities designed to help the
viewer better understand the terrain relationships in a virtual environment.
After using the prepared educational examples to become familiar with
the software by using the prepared educational examples, users should
feel free to utilize the software tools to explore and study their own
data sets.
Self directed educational activities utilizing field data collected in
this research project are available at ???????????(web address).
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