CSIRO's research in Precision Agriculture is providing farmers and land managers with the tools to understand and benefit from the variability of their land.
It is undeniable that our agricultural land is variable.
Rural landscapes generally comprise a mix of flat and hilly land, a variety of land uses and drainage lines ranging from small short-lived creeks to large rivers.
A range of soil types and soil properties exist and there may be considerable diversity of ecosystem function and condition across the landscape.
One consequence of landscape variability is that productivity is also variable.
Farmers have known this for as long as they have been growing crops, but without methods for observing or reacting to this variation, they have been forced to manage paddocks and farms as though they were uniform.
A precision approach
“Land is variable. No two soil particles, fields, farms or regions are the same. Precision agriculture allows us to take advantage of landscape variation to achieve specific economic and environmental goals.”
Dr Rob Bramley, Team Leader, Precision Agriculture
Precision Agriculture seeks to exert more control over a production system by recognising variation and managing different areas of land differently, according to a range of economic and environmental goals.
To do this, the tools of Precision Agriculture are used to collect large amounts of data on crop performance and the attributes of individual production areas (for example, fields, paddocks and blocks) at a high spatial resolution.
A number of enabling technologies are critical to Precision Agriculture. These include the global positioning system (GPS), geographical information systems (GIS), soil sensors and yield monitors which, with GPS, enable georeferenced records of yield to be collected ‘on-the-go’ during harvest.
With these technologies, growers are better able to observe, understand and manage the variability in their production systems by tailoring inputs to desired outputs.
Precision Agriculture can also be used as a tool to help match land use to land use capability. This helps address sustainability issues by optimising profitability in the productive parts of the landscape while conserving biodiversity and natural resource base in less productive parts.
Key research questions
Key questions facing farmers, land managers and researchers about a precision approach to farming are:
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Is variation predictable?
Are patterns of yield variation constant from year to year or otherwise predictable? If they are, Precision Agriculture may increase the likelihood that a particular management action will deliver a desired or expected outcome.
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Does variation in yield equate to variation in quality?
Are patterns of variation in yield matched by patterns of variation in quality? If they are, targeted management becomes more appealing. In the case of winegrapes for example, it would not be advantageous to focus on yield if this were at the expense of quality.
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What drives variation and can it be managed?
What are the key drivers of variation? If these are known and manageable, there is an opportunity to both tailor inputs to a specific economic or environmental goal and selectively harvest the outputs into different product streams.
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Are there economic or environmental benefits for targeted management?
Does targeting management deliver an economic or environmental benefit over conventional uniform management - a practice which assumes that paddocks are homogenous with respect to potential productivity?
CSIRO’s Precision Agriculture research
CSIRO’s research in Precision Agriculture is addressing these key questions in a range of farming systems, including viticulture, broadacre cropping, dairy and sugar farming.
Our research also looks at developing and refining tools to assess, monitor and redress environmental and economic risks associated with agricultural practices – potentially resulting in efficient use of water and fertilisers, targeted management of nitrogen pollution and maintenance of soil fertility.
Read more about CSIRO Sustainable Ecosystems.
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