big ideas for using data by brett whelan university of sydney

(or making more use of data)

Presented by

Brett Whelan

Precision Agriculture Laboratory

Faculty of Agriculture and Environment

BIG ideas for using DATA

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

• 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

• 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.


• Optimise production efficiency

• Optimise quality

• Minimise business risk

• Minimise environmental impact

Production Objectives


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

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.


Soil ECa measured using EM38h Engine load (% of total power rating)

Data supplied by Rupert McLaren, McLaren Farms ‘Glenmore’, Barmedman, NSW

Vehicle Engine Load During Sowing

Operation

and

Production

Data

Data

Storage Instigated

Analyses

Farm

Decisions

& Actions

Public Data

Bases

Localised

Industry

Aggregation

Production Decision Support

Farm

Decisions

& Actions Operation

and

Production

Data

Data

Storage

Public Data

Bases

Localised

Industry

Aggregation

Instigated

Analyses

Data

Storage


Operation

and

Production

Data

Data

Storage

Farm

Decisions

& Actions

Public Data

Bases

Localised

Industry

Aggregation

Data

Storage Instigated

Analyses


Farm

Decisions &

Actions

Operation

and

Production

Data Public Data

Bases

Localised

Industry

Aggregation

Data

Storage

Large,

Cloud-based

Proprietary

Agribusiness

Nefarious use


3G

Wireless Coverage

2 – 75 Mbps 2 – 50 Mbps

4G Download

3G

Wireless Coverage

1.1 – 20 Mbps 0.5 – 8 Mbps 0.5 – 3 Mbps

3G Download

3G

Wireless Coverage 3G Upload

0.5 – 3 Mbps slower Considerably slow

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

Farm

Decisions

& Actions Operation

and

Production

Data

Data

Storage

Public Data

Bases

Localised

Industry

Aggregation

Instigated

Analyses

Data

Storage


Real-time,

Adaptable Farm

Decisions &

Actions

Real-time

Operation

and

Production

Data

Public Data

Bases

Localised

Industry

Aggregation

Data

Storage


• 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.


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

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

big ideas for using data by brett whelan university of sydney

Data & Analytics