Agriculture Reference
In-Depth Information
reported potential problems (e.g., incompatibility of GIS and precision agriculture
software) related to commercial GIS and GPS systems in the market.
GIS can be applied to precision farming in many different ways. It can be used to
select locations or areas based on certain characteristics (e.g., high yielding areas and
low yielding areas in a field). Another application is data manipulation and analysis
including mathematical or logical operations. An example of this application would
be to create a dry yield map using moisture content and yield maps. GIS is very
effective for handling multiple data layers in precision agriculture. It can integrate
yield maps, nutrient maps, soil type maps, and other data layers for making manage-
ment decisions.
4.3.2 S
PATIAL
D
ATA
A
NALYSIS
AND
M
ANAGEMENT
Z
ONES
Precision agriculture involves a large number of data layers. A data layer can be a
soil organic map, a soil nitrogen map, a crop disease incidence map, a yield map, or
any other map characterizing the spatial variability of a variable within fields. Each
data layer can be stored in either vector format (i.e., soil type and sampling points) or
raster format (i.e., remote sensing imagery). When discrete samples are taken from a
field as in the case of grid soil sampling, data interpolation methods such as IDW and
kriging are commonly used to estimate the values of a variable at unsampled loca-
tions. Discrete data are generally interpolated into regularly spaced raster format,
which can then be used for generating contour maps and for performing spatial GIS
analysis. On the other hand, an airborne image or satellite image can be converted
to a polygon map by using image classification techniques to statistically clustering
image pixels into categories of similar spectral response.
The real impetus for site-specific crop management is within-field spatial vari-
ability. Understanding the magnitude and patterns of spatial variability in measured
variables provides an important basis for dividing a field into appropriate man-
agement units for site-specific crop management. Geostatistics is a useful tool for
describing the spatial dependence of a variable such as crop yield or a soil attribute in
precision agriculture. Spatial dependence implies that samples collected at smaller
separation distances are more likely to have similar values than those collected at
larger separation distances. The semivariogram, or simply variogram, in geostatis-
tics describes the spatial dependence of a variable (Isaaks and Srivastava, 1989). The
variogram shows how strongly and extensively the samples are related in space. The
influence range of the variogram can be used to determine appropriate pixel cell size
into which the field should be divided for variable rate application. Another impor-
tant use of the variogram is for kriging to generate unbiased estimates of a variable
at unsampled locations or at a regular grid.
Because of the limitations associated with using intensive grid soil sampling to
develop prescription maps, the concept of management zones has received consider-
able attention. Researchers have understood the value of dividing whole fields into
smaller, homogeneous regions for fertility management. Earlier studies proposed
the division of fields by soil type (Carr et al., 1991) and landscape position (Fiez et
al., 1994). Other methods of management zone delineation have used remote sens-
ing technologies to characterize within-field spatial variation. Remote sensing-based
