CISION
tory
48
ogies appropriate
and information
e appropriateness
ions between all
le navigation and
(GPS) linked to
integrated by
e way that we are
ig environmental
n of GPS with
| sampling allows
erenced data sets
, affecting crop
formation system
propriate tool for
of these data.
currently being
rs to quantify and
>xpected through
puts should result
net return, food
petitiveness.
will be enhanced,
ment of inputs in
ape features.
|y referenced data
over the course of
, consultants, and
ill become better
4. The compilation of accurate spatially and
temporally referenced agricultural and
environmental data on a microsite scale
wil provide an unprecedented
opportunity to study and understand
agricultural systems.
DEVELOPMENT OF DATA LAYERS
The precision farming project at the
National Environmentally Sound Production
Agriculture Laboratory (NESPAL) has scientists
from each of the disciplines involved in crop
production. The individuals responsible for data
acquisition for the various components of the
project are listed.
Soil Variability - R. Beverly, J. E. Hook, S.
Pocknee, O. Plank.
As early as the 1930's, scientists were
recording within field variability in soils and
recommending variable rate treatment as a
method of addressing yield variability
(Goering, 1993). Soil variability within a field
is the root of many other sources of variability
and directly influences parameters such as
nutrient availability, water supply and physical
rooting conditions. Populations of weeds,
insects and diseases are also influenced
indirectly by soil characteristics. Therefore, it is
imperative to have a thorough understanding of
the soil resource if spatial variability of factors
affecting crop yield are to be addressed.
Currently, the most common method of
gathering soil data is to extract soil cores from a
field on a grid layout. In 1995, we collected soil
samples on either a 20m or a 0.4ha grid. Soil
pH and levels of phosphorus, calcium,
potassium, manganese, magnesium and zinc
were determined from the samples.
Maps (Figure 1) often provide insight into
production practices and their effect on the
condition of the soil. The obvious striping of pH
in this field likely results from several
production practices. The trucks applying the
lime were known to travel in a north-south
pattern. It is possible some areas have been
continually overlapped resulting in an uneven
application of lime. The field has also been
plowed in a north-south pattern to create
terraces which moved the topsoil towards the
areas of high pH. The hard hose irrigation
system also tracks in line with the high pH
areas. Although the development of this pH
pattern is complex, the problem can be
addressed in several ways once observed on a
map. A control map constructed from this
layer, integrating georeferenced pH data and
variable rate technology, allows application of
lime site- specifically. Additionally, the farmer
can alter plowing patterns to change the
distribution of topsoil.
3132.60
3132.58
3132.56
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3132.48
Longitude
3132.46
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-8338.82 -8338.78 -8338.74 -8338.70 -8338.66 -8338.62
Latitude
Figure 1. Soil pH in a Peanut Field.
Although information obtained from grid
sampling is extremely valuable, it is labor
intensive and expensive to collect. Alternative
methods for mapping soil properties are being
introduced and tested. Generally, these involve
a continuous method of soil assay which can
determine soil characteristics "on-the-go" with
little disruption of the soil. Technology such as
Ground Penetrating Radar (GPR) (Raper et al.,
1990, Smith et al, 1989) and Electromagnetic
Induction (EM) (Sudduth et al., 1994) are being
adapted to measure parameters such as soil
moisture and texture (Figure 2). More research
is needed before data of this sort is understood
completely. Incorporation of these data into a
GIS with other factors affecting the crop may
help identify relationships.
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