Educational
Resources
These
resources are supplied as part of a collaboration between ACPA and
GRDC. They are made available for widespread use in the agriculture and
education sectors as part of the GRDC National Strategic Initiative on
PA (SIP09). Resources will be added as they are completed. The
release plan will follow the Site-Specific
Crop Management Cycle.
A GENERAL INTRODUCTION TO PRECISION AGRICULTURE
(1.10 MB)
As farm machinery has increased in size, there has been a tendency to
treat individual paddocks as uniform in respect to important yield
controlling factors such as soil physical condition and nutrition. This
is basically known as operating under an increasing economy of scale –
bigger, faster, and cheaper per hectare. To make this feasible,
an increase in farming area or more profitable ways of utilising the
time saved using this machinery is required. Now however, farmers and
the wider rural and urban communities are thinking a little harder
about this practice of managing agriculturally productive land as
uniform across each field. It is now being argued that such
practices could lead to a poor use of resources (fertilisers,
pesticides, fuel) and subsequently impose financial, environmental and
social costs. The significance of these costs (such as input
waste, yield reduction and soil, water and air contamination) to whole
farming systems can now receive serious consideration.
AN
INTRODUCTION TO CONCEPTS, ANALYSIS & INTERPRETATION (640 KB)
Background
notes for a 1-day workshop that provides a brief introduction to the
general concepts involved in PA and some insight into starting down the
analysis and interpretaion path. Usefull for a quick, broad overview of
PA for those not requiring the detail provided in the other resources
offered on this webpage.
GLOBAL
NAVIGATION SATELLITE SYSTEMS (718 KB)
The linchpin of modern
Site-Specific Crop Management in all its forms - from crop scouting to
variability monitoring to vehicle guidance. This is a comprehensive yet
easy to follow tour through the important aspects of how these systems
help us know where we are, navigate to where we may want to go, and
help in vehicle guidance and tracking.
CLEANING UP YIELD DATA (981 KB)
Raw data files from yield monitor
software come in a variety of formats and qualities. A cleaning process
based on the distribution of the yield data provides a basic way to clean any
data set. More sophisticated methods will eventually be available that work
with any data set, but for the present, the process described here should help
get data ready for further analysis.
CONVERTING GEOGRAPHIC TO CARTESIAN COORDINATES
(508 KB)
The spatial data produced by
yield moitors and other PA sensing systems records the location of
observations using geographic coordinates provided by the Global
Navigation Satellite System (GNSS) which is being used in the
operation. At present the Global Positioning System (GPS) is the most
widely used GNSS and it transmits a geopgraphical position in latitude
and longitude. While a geographical position is unique on the globe,
when it comes to using distances between observations in mapping
calculations, the distance represented by 1 degree in longitude is
greater on the equator than it is nearer to the poles. Conversion to
cartesian coordinates (Easting and Northing) which are measured in
‘metres’ overcomes this problem.
MAKING YIELD MAPS (3.79 MB)
In Australia, yield monitors
are now standard on many new makes of combine harvester. Coupling
these monitors with GPS technology allows growers to geo-reference
their yield information. Nevertheless, to make decisions from
this information it needs to be presented in a form that is easy to
interpret. Yield maps permit this by visually displaying the
data. However if yield maps are incorrectly constructed and/or
displayed then any decision stemming from them may be incorrect.
There are many options for making and displaying yield data and a
protocol to aid Australian grain growers in making correct maps is
provided here.
CLUSTERING FIELD DATA (1.99 MB)
The production of ‘pretty’
maps of various production variables is satisfying, however maps can
only be used to visually assess relationships. Consequently the quality
of the cartography may have a large impact on the quality of decisions
made. The analysis of multiple layers of field data needs to have some
statistical justification to avoid these problems. Cluster algorithms
are one (of numerous) statistical methods that can be used to ‘fuse’
data from different sensors and/or times together into a single useful
data layer - a managment class map.
A PROCESS FOR IMPLEMENTING SITE-SPECIFIC CROP
MANAGEMENT (2.17 MB)
Obviously we don’t farm to
intentionally loose money and in general this is not the case. But if
we consider farming over a short time frame (say a growing season) then
financial losses do occur. Incorporating Site-Specific Crop Management
into farm management will be no gaurantee against future losses, but
the risk of short-term financial losses may be minimised by using the
information gained and optimising the product input/output ratio. All
the while, we also profit from progress in long-term improvements in
operability, landscape and environmental management, product marketing,
storage of knowledge relevant to enterprise management and our
contribution to society.
THE UNIVERSITY OF SYDNEY