2. Outline
• Simplicity and transparency
• Who says the world has to be complex?
• Acknowledge stakeholders as experts
• Modesty and limitations
• Ideas, comments, suggestions
4. General observations
• Many decisions are simpler than we think
• Many analytic tools are complex, inaccessible or
opaque
• Computers are good at simple tasks (e.g.
arithmetic)
• Humans are good at complex tasks (e.g.
decision making)
5. Complexity and Generality
Relative
aerial
applicability
Detail
Rules Simple Complex
DSS
Of thumb simulation simulation
6. The struggle between usefulness
(goodness) and complexity
http://www.dau.mil/pubscats/PubsCats/atl/2005_11_12/war_nd05.pdf
10. Simplicity and
transparency
• The simplest things generally work
best, and the simpler the better.
• The easier a decision support tool is to use
and support.
• More complex >> less transparent.
• Active demonstrations are most effective
learning tools.
11. Who says farming is
complex?
• Increased complexity is a common pathway
for scientists.
• What challenge farm decision making
though is uncertainty.
• There is a view that many models should be
used in an “instructive” mode.
12. Acknowledge stakeholders as
experts
• Remember who has the greatest vested
interest in problem solving.
• The farmer is clearly the best expert, and
expert farmers often use a range of other
experts to support them.
• Being useful to decision makers requires
getting into their shoes.
14. Tactical decision making
- where is the niche for improved information?
•System status
-history (weather, previous crops)
-monitoring (soil water, weeds, disease)
•Weather futures
- based on history
- forecasts
Decision
•Market futures point
•Fit in the system
•Personal preferences
15. Tactical decision making
- how do farmers view this?
•System status
-history (weather, previous crops)
-monitoring (soil water, weeds, disease)
•Weather futures
- based on history
- forecasts
Decision
•Market futures point
•Fit in the system
•Personal preferences
16. Importance of various elements in decision making
– e.g. planting
8% 20%
Disease
8% risk
Starting
soil water
Gut
feeling
8% Weeds Climate 15%
forecast
adjustment
Soil N
Note:
8% Use this figure to focus
discussion on what are
Seed the issues and their
Price relative importance
availability
(no correct answers)
8%
30%
21. Some issues Queensland farmers
consider
• What are the chances of a planting rain?
• What are current moisture, nitrogen
conditions?
• What are implications for yields?
• Input needs?
22. Component questions for simple
models
• What are current conditions (e.g. moisture heat sum)?
• What are the chances of a future event (e.g. planting
rain, frost, wet harvest)?
• What is skill in a forecast?
• What are the implications of above, and what
management options are there to adjust?
23. Linking conditions NOW and Future probabilities
Recent Now Future
History (the decision
point)
outcome
Rainfall Current Expected drivers
Temperature conditions •Rainfall
•Temperature Range of
Soil water Options
Previous crop
Nutrition Based on and
Soil type
Disease •History outcomes
Management
Weeds •Persistence
•forecasts
-supported by
new observation
Time line
24. What are the chances of getting …
Rainfall 50 mm Temperature > 30 OC Temperature < 3 oC Heat sum 200 oC days
In 10 days, between
Occurs in 54 % of years between 1912-2010
Maximum
in each
year
Previous analysis
25. How is the season progressing?
Rainfall Max. temp. stress days Min. temp days Heat sum oC days
Between
Season to date rainfall from dd/mm/yyyy to dd/mm/yyyy
9th , 5th and 1st decile
Previous analysis
26.
27. Enlightened DSS design
• Question focused, client focused
• Easy to use and ready access
• Multiple access points
• Transparency
• Information, not advice
• Efficient
• Recognise life cycle