By Bindi Webb and Miles Dracup
Precision agriculture technologies are increasingly being used to identify areas of paddocks that perform differently, but what process can you follow to interpret and manage the causes for this production variability to maximise profit?
Exploring this question is one of the aims of the Department of Agriculture WA (DAWA)/GRDC precision agriculture project. The project"s other aim is to provide training and support for farmer groups, and build industry capacity to apply precision agriculture (PA) in WA.
Matching land use and inputs to variations in soil and seasonal conditions is important to optimise production, economic returns and environmental protection.
Previous research and reviews of PA have established its potential to improve farm profitability and simultaneously improve the eco-efficiency of agriculture.
An on-farm trial with the Casuarinas Group, south-east of Geraldton, showed the potential gains in profit from high inputs on high productivity zones and low inputs on low productivity zones. When extrapolated to the 160ha test paddock using the Invest, Vary or Cull program, there was a $9000 improvement in returns ($56/ha) compared with treating the paddock uniformly.
So getting both the “where” and “when” of inputs correct is crucial to increasing farming efficiency and profitability. To do that, tools to identify the “where” and “when” are needed, and these are what we are working on. Managing the “where”, will be integrated with new tools for managing the “when”, which are being developed within other DAWA projects on “managing seasonal variation”.
Varying inputs according to land capability is one level of response to spatial variation. However, targeted management of the spatial variation requires identification of its cause(s), as significant additional yield potential might be unlocked by some simple amelioration or treatment. Questions to be asked are:
A process to interpret spatial variation is needed that will draw on and guide the user through the use of different levels of information from simple in-crop observations and weather information, to yield and soil maps, and remotely sensed satellite information.
To be user-friendly the process must be simple and flexible enough to operate with both basic and “high-tech” information as required.
To build industry capacity in adopting PA, the project will support pilot groups through “shared learning”, as scientists work alongside farmer groups to analyse PA information and develop and evaluate responses.
Case studies and gross margin analysis will help determine where different PA technologies are - or are not - suitable for different situations in WA.
In the first year the project team will focus on understanding the value of gamma radiometrics information for managing spatial variability, and interpreting it and other sources of information while developing the diagnostic process.
The skills needs of the focus groups will be analysed to develop focused training packages for farmers and advisers. This training will draw on knowledge developed throughout the GRDC PA Initiative, which can be adapted to meet the needs and circumstances of regional WA.
The project is initially focusing on continuing to build PA skills within the Casuarinas/Walkaway (northern region) and Corrigin (central region) farmer groups. Key findings will then be progressively used with other groups according to needs, opportunities and enthusiasm.
A steering group is being established to facilitate PA activity in Western Australia; it will:
A number of widely spread farmer groups with varying levels of PA activity have been invited to participate: Casuarinas/Walkaway, Corrigin, Holt Rock, Liebe, Mingenew-Irwin, WANTFA, Wongan, and Young River.
For further information:
Bindi Webb, development officer, Department of Agriculture, Geraldton 08 9956 8530
GRDC research code: DAW 000084
A recent modelling study at DAWA by Meredith Fairbanks and Alexandra Edward showed that a wheat crop"s gross margins could be improved when the right amount of fertiliser for a soil type is applied according to the seasonal conditions.
Gross margin returns from applying levels of N appropriate to a light or heavy soil, given the actual sowing date, and gearing for an average season, or for the actual season that eventuated were compared for 2002 (a dry season) and 2003 (above average season) for a typical farm at Merredin using DAWA"s SPLAT model (Table 1).
That losses in 2002 (poor season) were high on the heavy soil and were not as bad on light soil if N rate was matched to the season. Conversely, they showed that returns in 2003 (above average season) were greater when N application reflected the actual season rather than an average season, and especially so on heavy soil.
Table 1. GRoss margins ($/ha) for wheat at Merredin (WA) with N appropriate to the soil type for an average or actual season (in parenthesis, kg/ha), given the actual date of sowing.