BIG ideas for using DATA. (or making more use of data)

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1 BIG ideas for using DATA (or making more use of data) Presented by Brett Whelan Precision Agriculture Laboratory Faculty of Agriculture and Environment Page 1

2 Information about the magnitude and variability in production that is present in a farming business is VALUABLE It s use dictates the SIZE & EXTENT OF THE VALUE Page 2

3 Data-Driven Decisions 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. Page 3

4 Data-Driven Decisions 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. Page 4

5 Data-Driven Decisions Production Objectives Optimise production efficiency Optimise quality Minimise business risk Minimise environmental impact Page 5

6 Production Objectives - Cropping 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. Page 6

7 Data-Driven Decisions 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. Page 7

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12 Vehicle Engine Load During Sowing Engine load (% of total power rating) Soil ECa measured using EM38h Data supplied by Rupert McLaren, McLaren Farms Glenmore, Barmedman, NSW Page 12

13 Production Decision Support Localised Industry Aggregation Farm Decisions & Actions Operation and Production Data Data Storage Instigated Analyses Public Data Bases Page 13

14 Production Decision Support Localised Industry Aggregation Farm Decisions & Actions Operation and Production Data Data Storage Instigated Analyses Public Data Bases Page 14

15 Production Decision Support Farm Decisions & Actions Operation and Production Data Localised Industry Aggregation Data Storage Public Data Bases Instigated Analyses Page 15

16 Production Decision Support Nefarious use Operation and Production Data Localised Industry Large, Cloud-based Aggregation Proprietary Data Agribusiness Storage Public Data Bases Farm Decisions & Actions Page 16

17 Wireless Coverage 4G Download 3G 2 75 Mbps 2 50 Mbps Page 17

18 Wireless Coverage 3G Download 3G Mbps Mbps Mbps Page 18

19 Wireless Coverage 3G Upload 3G Mbps slower Considerably slow Page 19

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 Page 20

21 Production Decision Support Localised Industry Aggregation Farm Decisions & Actions Operation and Production Data Data Storage Instigated Analyses Public Data Bases Page 21

22 Production Decision Support Localised Industry Aggregation Real-time Operation and Production Data Data Storage Public Data Bases Real-time, Adaptable Farm Decisions & Actions Page 22

23 Production Decision Support 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. Page 23

24 A Vision McBratney, A.B. & Whelan, B.M. (1995) The Potential for Site-Specific Management of Cotton Farming Systems. CRC for Sustainable Cotton Production, 46p. Page 24

25 Information about the magnitude and variability in production that is present in a farming business is VALUABLE It s use dictates the SIZE & EXTENT OF THE VALUE Page 25