Big ideas for using data by Brett Whelan University of Sydney
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Brett Whelan presents on using data in precision agriculture. The development of precision agriculture has increased the volume and sources of data available. Using data can optimize production efficiency, quality, minimize business risk and environmental impact through data-driven decisions. Key components of using data include data generation, storage in data dormitories such as the cloud, and prescriptive agriculture using probabilistic models. Real-time adaptable decisions will involve integrating diverse data sources to improve sub-paddock management while optimizing whole business profitability and sustainability.
Big ideas for using data by Brett Whelan University of Sydney
- 1. Page 1 (or making more use of data) Presented by Brett Whelan Precision Agriculture Laboratory Faculty of Agriculture and Environment BIG ideas for using DATA
- 2. Page 2 It’s use dictates the SIZE & EXTENT OF THE VALUE Information about the magnitude and variability in production that is present in a farming business is VALUABLE
- 3. Page 3 • The development and application of PA has been in parallel with an increase in the volume and sources of data. • Long before the term ‘Big Data’ was dragged from the literature on digital data storage, through the filter of business management analysts, to now, PA has been working on Big ideas for using Data. Data-Driven Decisions
- 4. Page 4 • The practical goal is to increase the number of (correct) decisions per hectare/animal/machine/season made in the business of farm management. • The potential financial benefits from using data to better managing inputs to match variability in operations varies with each management unit & farming business, but the potential improvements in gross margin ($/ha) are significant. Data-Driven Decisions
- 5. Page 5 • Optimise production efficiency • Optimise quality • Minimise business risk • Minimise environmental impact Production Objectives Data-Driven Decisions
- 6. Page 6 Tull, J. (1731) The New Horse-Houghing Husbandry: or, an essay on the principles of tillage and vegetation. Wherein is shewn, a method of introducing a sort of Vineyard-Culture into the corn-fields, in order to increase their product, and diminish the common expence, by the use of instruments lately invented. Dublin: Printed by Aaron Rhames. Production Objectives - Cropping
- 7. Page 7 Merge (large) data streams from diverse sources and scales with adaptable crop and environmental models that feed information into key decisions. Components include: • Data generation and capture (historic and real-time). These may include yield maps, aerial/proximal sensing (vigour, disease, pest), soil, environment, economics/markets. • Data dormitories. These may eventually store data in the cloud using wireless data transfer. • Prescriptive agriculture. Derived from alternative options for crop management, variable-rate application and farm logistics based on probabilistic assessment of causal relationships. Data-Driven Decisions
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- 12. Page 12 Soil ECa measured using EM38hEngine load (% of total power rating) Data supplied by Rupert McLaren, McLaren Farms ‘Glenmore’, Barmedman, NSW Vehicle Engine Load During Sowing
- 13. Page 13 Operation and Production Data Data Storage Instigated Analyses Farm Decisions & Actions Public Data Bases Localised Industry Aggregation Production Decision Support
- 14. Page 14 Farm Decisions & Actions Operation and Production Data Data Storage Public Data Bases Localised Industry Aggregation Instigated Analyses Data Storage Production Decision Support
- 15. Page 15 Operation and Production Data Data Storage Farm Decisions & Actions Public Data Bases Localised Industry Aggregation Data Storage Instigated Analyses Production Decision Support
- 16. Page 16 Farm Decisions & Actions Operation and Production Data Public Data Bases Localised Industry Aggregation Data Storage Large, Cloud-based Proprietary Agribusiness Nefarious use Production Decision Support
- 17. Page 17 3G Wireless Coverage 2 – 75 Mbps 2 – 50 Mbps 4G Download
- 18. Page 18 3G Wireless Coverage 1.1 – 20 Mbps 0.5 – 8 Mbps 0.5 – 3 Mbps 3G Download
- 19. Page 19 3G Wireless Coverage 3G Upload 0.5 – 3 Mbps slower Considerably slow
- 20. Page 20 Data weight Uploading Data (generic shp files) UAV imagery –10 MB/ha Spraying – 0.7 MB/ha Planting – 13 MB/ha Yield data – 10 MB/ha Soil Mapping – Data 1.5 MB/ha Downloading Data Prescription files – 0.02 MB/ha Source: T. Griffin. Kansas State University
- 21. Page 21 Farm Decisions & Actions Operation and Production Data Data Storage Public Data Bases Localised Industry Aggregation Instigated Analyses Data Storage Production Decision Support
- 22. Page 22 Real-time, Adaptable Farm Decisions & Actions Real-time Operation and Production Data Public Data Bases Localised Industry Aggregation Data Storage Production Decision Support
- 23. Page 23 • A tool that contains the capability of autonomously adapting decision functions and providing farmers with alternative scenarios as input data changes across space and/or time. • Involves the novel integration of relevant data from diverse domains, sources and scales to improve decision management at the sub-paddock level, within bounds of optimising the whole business profitability, and sustainability. • Water, nitrogen and canopy management focus • The Augmented Agronomist…..not the Automated Agronomist…..unless the decision/action warrants. Production Decision Support
- 24. Page 24 McBratney, A.B. & Whelan, B.M. (1995) The Potential for Site-Specific Management of Cotton Farming Systems. CRC for Sustainable Cotton Production, 46p. A Vision
- 25. Page 25 It’s use dictates the SIZE & EXTENT OF THE VALUE Information about the magnitude and variability in production that is present in a farming business is VALUABLE