Ground Cover Supplement : GC Supplement - Precision Agriculture 2004
and a list of management s. k is in progress to measure eld losses caused by soilborne es in different production This will run over a number rs to cover different seasons. results from the SPAA focus ck at Crystal Brook, 2003, te medium levels (for bread s) of crown rot inoculum at g, were correlated with a cent yield reduction in the nd green zones. e detailed studies are under sing small fumigated plots to e better data on yield losses. eld responses for similar gen levels in different zones e used to develop simple mic models to help allocate to each zone. expect the economics of lling root diseases will be different between zones. When we understand which data layers produce the best zone maps to target soilborne pathogens, the information will be made available to consultants and grain growers to help make better crop selection decisions. Those using variable rate systems will be able to target higher inputs to the more reliable areas of the paddock, and target treatments such as seed dressings to reduce the risk of yield loss where appropriate. These outcomes will thus contribute to more flexible and profitable cropping strategies. GRDC RESEARCH CODE DAS 00035 For more information: Dr John Heap, 08 83039444, email@example.com Dr Alan McKay, 08 83039375, firstname.lastname@example.org 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: n Is the variability relatively stable between seasons and caused by such issues as nutrition, acidity or salinity; or n Is it unstable and caused by issues such as frost or waterlogging or plant species/ variety sensitivity to stresses? n or is it a combination of these? n What is the likely frequency and impact of the problem(s)? 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: n be responsive to farmer group needs; n share information and experiences between groups; n coordinate PA activities with closely related projects, such as those on sub-soil constraints and nutrient management. 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. GRDC RESEARCH CODE DAW 000084 For further information: Bindi Webb, development officer, Department of Agriculture, Geraldton 08 9956 8530, Zone Take-all Rhizoctonia 1 142 55 2 0 20 3 00 0 KM 250 LOCATION OF STUDY SITES FOR THE WESTERN AUSTRALIAN PRECISION AGRICULTURE PROJECT GERALDTON MORAWA MOORA NORTHAM PROJECT STUDY SITES SOUTH-WESTERN AGRICULTURAL AREA PASTORAL REGION NARROGIN MERREDIN KATANNING ALBANY JERRAMUNGUP ESPERANCE KALGOORLIE PERTH Corrigin Farm Improvement Casuarinas -- Walkaway 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. Average season Actual season Poor year (2002) Light soil -$15 (10) -$5 (0) Heavy soil -$69 (0) -$69 (0) Above average year (2003) Light soil $215 (70) $230 (100) Heavy soil $214 (30) $235 (80) 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. For efficiency and profitability, 'where' and 'when' are crucial &5 Modelling shows way to improve margins ure 1: Temporal average map of yield/biomass for the paddock ure 2: Temporal viability map of yield/biomass for the paddock 3. ACPA focus paddock at 2004. 3. Pathogen levels detected agement zones in ACPA paddock at Albury.
GC Supplement - Managing Subsoils 2004