Trends in Digital Game Based Learning (DGBL). What does DGBL mean to you?
What does Digital Agriculture mean? What are the …...2016/02/10 · What does Digital Agriculture...
Transcript of What does Digital Agriculture mean? What are the …...2016/02/10 · What does Digital Agriculture...
What does Digital Agriculture mean?
What are the big picture opportunities for the red meat industry?
Digital Strategy Forum
Steve Sonka October 6, 2016
Page 2
Agenda
Perspectives of digital ag innovation
Decisions – not digits
Digital data are/is different
Digital Ag opportunities
Page 3
What Do I Mean by Digital Ag?
Employing sensors and technologies to capture data in digital form
AND
Using tools and techniques to
» Summarize/Analyze/Synthesize
» Communicate
digital and other information
to improve decision making
Page 4
Digital and Beef
Page 5
The Digital Dilemma !!!
“There’s a lot of data being generated: A), not all of it is being captured, B), of what’s captured, a fraction is being used.”
Mckinsey & Co., August, 2016
Page 6
Geo-Spatial in Rwanda
Page 7
6.2 Million Data Sets
Page 8
Food Retailing Embraces Big Data
Page 9
Perspectives of Digital Ag Innovation
(Hyper)Rapid change
Lessons from outside food/ag
Outside of production ag in Australia
– Global uses
– Global sources
– Developing as well as developed ag
– Downstream & upstream
Page 10
Agenda
Perspectives of digital ag innovation
Decisions – not digits
Digital data are/is different
Digital Ag opportunities
Page 11
Management in Ag
Operational Financial Strategic
Decisions Signals
From “Observation” Ag To “Why-Based” Ag To “Precision” Ag
Page 12
Observation Ag – About 1952
Measurement: • An economic activity • Conditioned by technology • Managed by observing “What”
Page 13
Why-Based Ag – 20th Century
External
Page 14
Precision Agriculture Circa 1996
Page 15
Insights for Digital Ag
Producers need more than summarization
Value proposition is segmenting
– Precision ag: large non-adopter segment
Producer’s decision purpose hasn’t changed
– Digital ag alters economic capabilities
Page 16
Agenda
Perspectives of digital ag innovation
Decisions – not digits
Digital data are/is different
Digital Ag opportunities
Page 17
Dimensions of Digital Data: 3 Vs and an A
Variety
Volume Velocity
Variety
Page 18
Data Sources – Today !
Page 19
Data Sources
This is THE change factor in Digital agriculture
Page 20
Dimensions of Digital Data: 3 Vs and an A
Variety
Volume Velocity
Analytics
Page 21
Dimensions of Digital Data: 3 Vs and an A
Variety
Volume Velocity
Analytics
Big Data
Lots of Data
Page 22
Digital Ag is About Knowing What;
Page 23
Digital Ag is About Knowing What; Is It Enough to Know WHAT?
Society will need to shed some of its obsession for causality in exchange for simple correlations:
not knowing why but only what.
Page 24
The Truth about Nutrition
The Japanese eat very little fat – suffer fewer heart attacks than do the British or Americans
The French eat a lot of fat – suffer fewer heart attacks than do the British or Americans
The Italians drink excessive amounts of red wine – suffer fewer heart attacks than do the British or Americans
The Japanese drink very little red wine – suffer fewer heart attacks than do the British or Americans
The Germans drink a lot of beer and eat lots of sausages – suffer fewer heart attacks than do the British or Americans
Page 25
The Truth about Nutrition
SO…………..
EAT what you want,
Apparently it’s SPEAKING ENGLISH
that kills you!
Page 26
Full Success for Digital Ag: Combining Why and What!
Page 27
Insights for Digital Ag
Everything (almost!) can be data
Digital tools need to support producers
– Inform not tell
– Gap for large (but not BIG) data sets
Linking Why with What is essential
Page 28
Agenda
Perspectives of digital ag innovation
Decisions – not digits
Digital data are/is different
Digital Ag opportunities
Page 29
The Food System & Big Data/Digital Ag
Bioinformatics
Optimizing Production Systems
Consumer behaviors
Genetics
Input Supply
Production
1st Handler
Manufacturer/ Retailers
Page 30
Strategy for Digital Ag
Domain is dynamic & complex
Producers need access to analysis
Data has changed & “why” matters
Pressures for adoption – Producer efficiency & profitability
– Food system downstream & upstream
– Responding to regulation
SummaryThoughts